The Brief Addiction Science Information Source (BASIS)

The WAGER Vol. 13(4) - Gambling Problems: All or Nothing?

The American Psychiatric Association diagnostic guidelines conceptualize pathological gambling (PG) as either present (five or more signs or symptoms) or absent (four or fewer signs or symptoms). This diagnostic scheme for PG weights all signs and symptoms equally. Many researchers and clinicians have suggested that a more continuous conceptualization, in which the diagnostic guidelines consider the severity of specific signs and symptoms, would be more appropriate for the disorder than this dichotomous unweighted approach.  In this edition of the WAGER, we review a study by Strong & Kahler (2007) which investigated the psychometric properties of the 10 DSM-IV criteria, and evaluated the gambling severity continuum.

Strong and Kahler analyzed data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a national general population survey of 43,093 adults that includes a DSM-IV-based measure of pathological gambling. The researchers applied a Rasch Model – an analysis that assesses the severity of symptoms and their probability of being endorsed by individuals at different places along a severity continuum of PG – to the 2,180 respondents who endorsed one or more lifetime PG criteria.

Figure 1. Probability of endorsing specific PG signs and symptoms at different levels of gambling problem severity (reproduced from Strong & Kahler, 2007).

Note. Level of Gambling Problem Severity is determined by the Rasch Model but corresponds roughly to number of symptoms endorsed. Each curve refers to a symptom: 1 = preoccupation w/ gambling; 2 = tolerance; 5 = escape; 6 = chasing; 7 = lying; 3 = loss of control; 10 = relying on others to cover debt; 4 = withdrawal; 9 = jeopardizing relationships; 8 = illegal acts.

Twelve percent of all gamblers endorsed being ‘preoccupied with gambling,’ and this problem fell in the mild range of the severity continuum; less than 0.1% of all gamblers endorsed committing “illegal acts to finance gambling,” and this problem fell at the most severe end of the continuum. Figure 1 shows the distribution of the 10 PG criteria along the dimension of gambling severity (i.e., # of symptoms endorsed). The symptoms are not equally distributed across the continuum; the figure also shows that, in some cases, a relatively larger increase in gambling severity is required before further gambling symptoms are observed. These gaps provide guidance about the relationship between specific symptoms and possible thresholds for different levels of gambling problem severity. Preoccupation (curve 1) appears to be a diagnostic gateway for other problems. Tolerance, escape, chasing, lying, and loss of control occur at similar severity levels, but a large increase in severity is required before reliance on others, withdrawal symptoms, jeopardizing relationships, or illegal acts occur.

This study’s main analyses utilized lifetime criteria for PG, which is a limitation. Lifetime measurements can be problematic because of memory recall problems and reinterpretation of past events, as well as other biases. An analysis of past year symptoms could possibly reveal a different structure to the data. Despite this limitation, this study suggests that, contrary the current unweighted dichotomous classification scheme for pathological gambling, gambling problems fall along a severity continuum and different signs and symptoms occur at different thresholds of severity.

What do you think? Comments should be addressed to Line Gebauer and Sarah Nelson.

References

Strong, D. R., & Kahler, C. W. (2007). Evaluating the continuum of gambling problems using the DSM-IV. Addiction, 102(713-721).

The WAGER, Vol. 13(3) - Game Preferences of Pathological Gamblers and Odds of Recovery

Researchers have speculated as to whether different types of games have different associations with disordered gambling (Ladd & Petry, 2007; Urbanoski & Rush, 2006).  However, few studies have tested this speculation in a rigorous manner.  This week’s WAGER takes a second look (see WAGER 13(2) for the first look) at Kessler, Hwang, LaBrie, Petukhova, Sampson, Winters, & Shaffer (2008) who examined the epidemiology of gambling and games played.  In this WAGER, we will examine the study’s assessment of the distribution of pathological gambling (PG) and recovery across different forms of gambling.

The National Comorbidity Survey Replication (NSC-R), a nationally representative sample of 9,282 English speaking adults (Kessler & Merikangas, 2004), used the Composite International Diagnostic Interview (CIDI; Kessler & Ustun, 2004) to assess DSM-IV criteria (American Psychiatric Association, 1994) for Axis I disorders among participants.  For gambling, the instrument also assessed what types of games each participant played and, for gamblers who experienced problems, whether they experienced recovery (defined as being symptom free for the two years prior to the interview). 

Table 1. Lifetime prevalence of types of gambling and their associations with PG and recovery (adapted from Kessler et al., 2008).

Note.  CI = confidence interval; OR = odds ratio. Presented ORs are adjusted for sex, race-ethnicity-age of onset (AOO) of 1st gambling, years since 1st gambling, and 11 game types.
+ Problem gamblers defined as participants who endorse one or more DSM-IV criteria for PG.
*p < 0.05

More than half (54.7) of gamblers with problems (i.e., meeting 1 or more lifetime DSM-IV criteria for PG) played 7 or more games, compared to 17.1% of non-problem gamblers. All groups favored games in a similar ranked order.  Table 1 shows those who played “games of mental skill” (e.g., cards) were more likely to qualify for PG than others.  Table 1 also shows that casino sports book gambling at casinos was associated with higher odds of recovery among problem gamblers than other games, whereas slots, bingo, and pull tabs were associated with lower odds of recovery.

These results reflect patterns of association, so we cannot determine whether gambling problems developed as a result of game choice or influenced game choice. Therefore, implicating one game as more dangerous or more difficult to recover from than another exceeds the limits of this study’s methodology. 

The study is important for many reasons. It employs a representative sample and used replicable and reliable methods to collect data. The study shows that people with gambling problems do not play games randomly; there are some significant trends associated with likelihood of developing problems and the likelihood of recovery. Future research using longitudinal designs will be necessary to shed light on the psychosocial or game characteristics that account for the patterns shown in the table and, perhaps ultimately, hold potential to improve prevention and early detection.

What do you think?  Comments can be addressed to Leslie Bosworth

References

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

Kessler, R. C., Hwang, I., LaBrie, R., Petukhova, M., Sampson, N. A., Winters, K. C., et al. (in press). DSM-IV pathological gambling in the National Comorbidity Survey Replication. Psychological Medicine [preprint available]

Kessler, R. C., & Merikangas, K. R. (2004). The National Comorbidity Survey Replication (NCS-R): Background and aims. International Journal of Methods in Psychiatric Research, 13(2), 60-68.

Kessler, R. C., & Ustun, T. B. (2004). The World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research, 13(2), 93-121.

Ladd, G. T., & Petry, N. M. (2007). Disordered Gambling Among University-Based Medical and Dental Patients: A Focus on Internet Gambling. Psychology of Addictive Behaviors, 16(1), 76-79.

Urbanoski, K. A., & Rush, B. R. (2006). Characteristics of people seeking treatment for problem gambling in Ontario: Trends from 1998 to 2002. Journal of Gambling Issues, 16.

The WAGER, Vol. 13(2) - Chickens, Eggs, and Psychiatric Comorbidity Among PGs

People with gambling-related problems also are likely to qualify for other psychiatric disorders  (McIntyre et al., 2007; Shaffer et al., 2007). Understanding the temporal sequence of PG and comorbid disorders can provide information about how PG relates to other disorders and suggests causal links. Unfortunately, there is little research that clarifies how the onset of PG relates temporally to the onset of other disorders. This week’s WAGER reviews a large epidemiological study by Kessler, Hwang, LaBrie, Petukhova, Sampson, Winters & Shaffer (2008), which investigated comorbid disorders among people with PG. In addition, this study examined the temporal onset of PG and the onset of other disorders.

The National Comorbidity Survey Replication (NSC-R), a nationally representative sample of 9,282 English speaking adults (Kessler & Merikangas, 2004), used the Composite International Diagnostic Interview (CIDI; Kessler & Ustun, 2004) to assess DSM-IV criteria (American Psychiatric Association, 1994) for Axis I disorders, and age of onset (AOO; i.e., the age at which people first reported the first symptom of a given disorder) for those with disorders. 

Table 1.  Lifetime psychiatric comorbidity among participants with lifetime PG (adapted from Kessler et al., 2008)

* Prevalence significantly greater among PGs compared to the rest of the sample (p < .05).

Note: Any mood disorder = major depressive disorder or dysthymia and bipolar disorder.  Any anxiety disorder = phobias, generalized anxiety, panic, and post-traumatic stress disorder.  Any impulse control disorder = oppositional-defiant, conduct, attention deficit hyper activity, and intermittent explosive disorders.   Any substance use disorder = alcohol or drug abuse, dependence, and nicotine dependence.

The lifetime prevalence of pathological gambling (PG) within the sample was 0.6%.  Almost all participants who had lifetime PG also had another lifetime disorder (96.3%) and 64.3% suffered from three or more disorders.  Table 1 shows that mood disorders, anxiety disorders, and substance use disorders were significantly elevated among participants with PG, and that other disorders were more likely to precede PG than to occur afterward or begin at the same time.  In fact, 74.3% of participants with PG and another disorder experienced the other disorder before PG.

These results provide important information about the temporal relationships among disorders. However, this study cannot conclusively determine whether any disorder caused PG, or PG caused another disorder. Furthermore, the data in this study derived from retrospective self-report; consequently, AOO might not be accurate because of recall errors.  However, the results suggest that PG rarely exists alone. This study adds important new data about typical pattern of onset.  The study also illuminates the need for mental health and medical professionals to assess PG along with the variety of other disorders with which it is often comorbid.

What do you think?  Comments can be addressed to Leslie Bosworth.

References

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

Kessler, R. C., Hwang, I., LaBrie, R., Petukhova, M., Sampson, N. A., Winters, K. C., et al. (in press). DSM-IV pathological gambling in the National Comorbidity Survey Replication. Psychological Medicine [preprint available]

Kessler, R. C., & Merikangas, K. R. (2004). The National Comorbidity Survey Replication (NCS-R): Background and aims. International Journal of Methods in Psychiatric Research, 13(2), 60-68.

Kessler, R. C., & Ustun, T. B. (2004). The World Mental Health (WMH) Survey Initiative version of the World Health Organization (WHO) Composite International Diagnostic Interview (CIDI). International Journal of Methods in Psychiatric Research, 13(2), 93-121.

McIntyre, R. S., McElroy, S. L., Konarski, J. Z., Soczynska, J. K., Wilkins, K., & Kennedy, S. H. (2007). Problem gambling in bipolar disorder: Results from the Canadian Community Health Survey. Journal of Affective Disorders, 102(1), 27-34.

Shaffer, H. J., Nelson, S. E., LaPlante, D. A., LaBrie, R. A., Albanese, M., & Caro, G. (2007). The epidemiology of psychiatric disorders among repeat DUI offenders accepting a treatment sentencing option Journal of Consulting and Clinical Psychology, 75(5), 795-804.

The WAGER Vol. 13(1): When Your Heart Beats for Gambling

The excitement of gambling is often described as an increased arousal, where gamblers experience an almost euphoric state. This euphoria is believed to be an important factor in the reinforcement of gambling. Moodie & Finnigan (2005) investigated elevated arousal in frequent and infrequent fruit machine players, as well as non-gamblers. Each group consisted of 21 individuals (three females in each group). Frequent gamblers were identified as people who gambled more than three times a week. Infrequent gamblers in this sample gambled an average of two times a month and non-gamblers had no history of gambling. The study gave participants £6 with which to gamble and measured their heart rate at baseline, before and after each of 20 plays on a fruit machine located inside a real gambling arcade, and one minute after the gambling session ended.

As demonstrated in Figure 1, results of a one-way repeated-measures ANOVA showed an interaction between group and time on arousal. Though non-gamblers had the highest baseline heart rate, frequent gamblers showed a significantly higher increase in heart rate than both infrequent and non-gamblers. Unlike infrequent and non-gamblers, their heart rates continued to rise after the gambling session. Researchers also measured heart rate changes in response to wins, nudges, bonuses and special game features. These machine characteristics were all associated with elevated arousal, which was further exacerbated in frequent gamblers. Significant differences were found between non-gamblers and frequent gamblers for wins and nudges, and between all three groups for features.

Figure 1. Arousal among Frequent, Infrequent, and Non Gamblers in Reponse to a Gambling Task

One major limitation of the study was that the baseline heart rates of the non-gamblers were extremely high relative to both the frequent and infrequent gamblers.  This might be due to the novelty of the gambling surroundings and the research task, and suggests that the higher increase in arousal found in frequent gamblers might potentially be an artifact since the non-gamblers were already aroused. More participants and a longer duration of the gambling session would benefit the study and allow for other observations such as adaptation over time. Nevertheless, it is an important finding that frequent gamblers show increased arousal during gambling, and that not only wins but also the interactive features of the game influence arousal.

What do you think? Comments should be addressed to Line Gebauer.

References

Moodie, C., & Finnigan, F. (2005). A comparison of the autonomic arousal of frequent, infrequent and non-gamblers while playing fruit machines. Addiction, 100(1), 51-59.

The WAGER Vol. 12(11): The Importance of Sensitivity: In Models and for Interpreting What You Read

Have you noticed lately that several different cellular phone companies are running ads claiming to be America’s leading network? Have you wondered how they can all claim to be number one? One company claims to have the fewest dropped calls of any network. This sounds impressive until you begin to wonder how many calls actually connect in the first place. Companies can’t drop calls if they don’t complete them in the first place.

To understand and evaluate the wealth of information that advertising and press releases provide us, it is important to be an informed consumer.  Knowing what questions to ask when reading claims in the media is crucial to becoming an active processor, rather than a passive absorber, of this information. To know what questions to ask, a basic understanding of statistics and research methodology is necessary. We attempt to provide some of that understanding in the BASIS every week. Although the research we review is important, the ability to understand and evaluate the research you come across in other settings is just as important.

I recently read a press release touting a technology able to identify players at risk for gambling problems “with a precision of more than 90%.” A very impressive claim at first glance. But let’s investigate this claim more thoroughly. What does 90% precision mean in this case? The study behind this claim is not publicly available – no surprise – so we have to speculate. Given what we know about gambling disorders and problems, it is interesting to explore what data could lead to such a claim.

Here is one way to achieve 90% precision:

In this example, there are 7 actual gamblers with problems, and 93 actual recreational gamblers without problems, represented by the columns. The rows represent how the given model (e.g., technology, diagnostic test, etc.) classifies those people. In this case, the predictive model correctly classifies 90% of the sample (i.e., the 87 who are both predicted and are actual recreational gamblers and the 3 who are both predicted and are actual gamblers with problems). This represents the 90% precision claim. However, disordered gambling is a “low” base rate event: less than 2% of the population typically qualifies as pathological gamblers and approximately 3-5% more as problem gamblers. As a result, most people classified by any model will fall into the recreational category, both in terms of their actual and predicted symptoms.

In the example, 93 do not have gambling problems, and the model correctly classifies 94% of those 93. (1) This relationship accounts for the bulk of the model’s classification accuracy. If instead, we consider the model’s capacity to accurately classify actual gamblers with problems, a different picture emerges. There are 7 actual gamblers with problems in the example. The model correctly classifies 3 of them: 43%. (2) Of the 9 people that the model classifies as gamblers with problems, only 33% are actual gamblers with problems. (3)

There are two key points here: First, statistics can sound impressive, but without the appropriate context, statistics easily can be misinterpreted. Second, specific to a disorder or problem with a low occurrence in the general population, it is relatively easy to achieve very good overall classification accuracy. To truly evaluate a model’s ability to classify, as in this case, it is important to know how well it classifies actual cases (e.g., gamblers with problems), not just non-cases.

Protecting the public health is best served by developing screening procedures that have a high sensitivity (i.e., ability to identify those people who have problems). Assuming that the procedures themselves don’t have any extraordinary risks, it is generally optimal to include false positives (i.e., people identified as having problems who do not actually have problems) rather than to miss identifying the cases with problems. Informed consumers should demand to know all the parameters of a model’s accuracy, and in the case of infrequently occurring problems, particularly the sensitivity.

What do you think? Comments should be addressed to Sarah Nelson.

Notes

1  This (i.e., correctly classifying 94% of those without problems) is the specificity of the predictive model.
2  This (i.e., correctly classifying 43% of those with problems) is the sensitivity of the predictive model.
3  This (i.e., 3 of the 9 classified as having problem actually having problems) is the positive predictive value of the predictive model.

The WAGER Vol. 12(10): Another DUI: Deciding Under the Influence of Emotion

Somatic markers (i.e., affective states associated with former experience) guide healthy decision-making (Bechara & Damasio, 2002). Research suggests that poor decision-making among disordered gamblers (Goudriaan, Oosterlaan, de Beurs, & van den Brink, 2005) might originate from prefrontal cortex dysfunction that impairs the use of somatic markers to guide decisions (Brand et al., 2005; Cavedini, Riboldi, Keller, D'Annucci, & Bellodi, 2002). However, recent research (Preston, Buchanan, Stansfield, & Bechara, 2007) suggests an alternative source of decision-making difficulties on gambling tasks: anticipatory stress.

Preston and colleagues (2007) assigned forty university students to either an experimental condition, in which they thought they would have to give a public speech after completing the Iowa Gambling Task (IGT: Bechara, Damasio, Damasio, & Anderson, 1994; Bechara, Tranel, Damasio, & Damasio, 1996), or a control condition (i.e., IGT only). The IGT has subjects choose between four card decks; each card is associated with either a win or a loss, and the decks differ in payoff rate. Disadvantageous decks give large immediate rewards but also large or frequent losses, whereas advantageous decks give smaller rewards but also smaller overall losses. 

Figure 1. Improvement on IGT as a Function of Group and Learning Block

Note. The figure displays the improvement from block 1 -2, and from 2-3. Participants in the experimental group show significant (p<.05) delayed and diminished learning compared to controls. The negative score of the controls from block 2-3 is due to the fast learning from block 1-2, which makes further improvement difficult.

Individuals in the experimental group learned the task more slowly than controls (See Figure 1), more often failed to correct disadvantageous choices, and had suboptimal outcomes.

Though this study is limited by its small sample size, it might hold important implications for our understanding of disordered gambling. The findings suggest that anticipatory stress significantly influences the emotional guidance of decision-making; other emotional interference might have a similar effect. Therefore, impaired decision-making among disordered gamblers might not be due only to a neurological deficit as some have suggested elsewhere. Several studies have shown high co-morbidity between disordered gambling and emotional problems (e.g., depression, mania, or anxiety -- Crockford & el-Guebaly, 1998; Petry, Stinson, & Grant, 2005). It is possible that these emotional problems have a similar influence to that of anticipatory stress on disordered gamblers’ decision-making, perhaps interfering with the somatic markers that ought to guide that decision-making and thereby exacerbating gambling problems. For example, research has found that depressed patients show impaired performance on the IGT (Must et al., 2006).

The potential influence of these emotional problems on decision-making in gambling situations reinforces the importance of recognizing and addressing these co-morbid disorders when treating people with gambling-related problems.

What do you think? Comments can be addressed to Line Gebauer.

References
Bechara, A., Damasio, A. R., Damasio, H., & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 7-15.

Bechara, A., & Damasio, H. (2002). Decision-making and addiction (part I): Impaired activation of somatic states in substance dependent individuals when pondering decisions with negative future consequences. Neuropsychologia, 40(10), 1675-1689.

Bechara, A., Tranel, H., Damasio, H., & Damasio, A. R. (1996). Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex, 6, 215-225.

Brand, M., Kalbe, E., Labudda, k., Fujiwara, E., Kessler, J., & Markowitsch, H. J. (2005). Decision-making impairments in patients with pathological gambling. Psychiatry Research, 133(1), 91-99.

Cavedini, P., Riboldi, G., Keller, R., D'Annucci, A., & Bellodi, L. (2002). Frontal lobe dysfunction in pathological gambling patients. Biological Psychiatry, 51, 334-341.

Crockford, D. N., & el-Guebaly, N. (1998). Psychiatric comorbidity in pathological gambling: a critical review. Can J Psychiatry, 43(1), 43-50.

Goudriaan, A. E., Oosterlaan, J., de Beurs, E., & van den Brink, W. (2005). Decision making in pathological gambling: A comparison between pathological gamblers, alcohol dependents, persons with Tourette syndrome, and normal controls. Cognitive Brain Research, 23(1), 137-151.

Must, A., Szabó, Z. n., Bódi, N., Szász, A., Janka, Z. n., & Kéri, S. (2006). Sensitivity to reward and punishment and the prefrontal cortex in major depression. Journal of Affective Disorders, 90(2), 209-215.

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.

Preston, S. D., Buchanan, T. W., Stansfield, R. B., & Bechara, A. (2007). Effects of anticipatory stress on decision making in a gambling task. Behavioral Neuroscience, 121(2), 257-263.

The WAGER 12(9) : The Pleasure of Gambling: Is it in the Winning and the Losing?

Research indicates that the dopaminergic reward system is one of the key mechanisms in learning and reinforcement of adaptive behaviors. Upon receipt of a reward, dopamine is released in the brain, which leads to pleasurable feelings and reinforces behavior. However, dopamine also reinforces maladaptive behaviors, such as substance-induced and behavioral addictions (Reuter, Raedler, & Rose, 2005; Zack & Poulos, 2007). In this edition of the WAGER, we review research by Fiorillo, Tobler & Schultz (2003) that examines the role of dopamine in uncertain situations.

Fiorillo and colleagues presented 2 primates with visual stimuli associated with a 0%, 25%, 50%, 75%, or 100% chance of receiving a reward (liquid) and recorded the activity of these primates’ midbrain dopamine neurons. Once the primates had learned the associations, the dopamine neurons showed increased activity between the stimuli presentation and the delivery of reward for stimuli predicting uncertainty of reward (25%, 50%, 75%). Activation was highest for stimuli predicting a 50% probability of reward (i.e., the highest uncertainty). See Figure 1.

Figure 1. Dopaminergic Activity in Response to Uncertain Stimuli in Two Primates (reproduced from Fiorillo et al., 2003, Figure 3c).

There are some limitations to this study. The reward system in primates might differ from humans’ in important ways. The study also used a very simple conditioning paradigm, whereas a real gambling situation is much more complex.

This increased dopaminergic activation under uncertainty appears to direct attention to the predictive stimulus to facilitate learning. The anticipatory activity might also reinforce risk taking in uncertain situations. Though potentially adaptive, this increased activation under uncertainty also might reinforce maladaptive behaviors such as excessive gambling in some people (Fiorillo, 2004). Most games rely on chance, so gamblers constantly face uncertain situations. Disordered gamblers might be particularly sensitive to the sustained anticipatory dopamine activation produced by these uncertain situations, making them feel good and continue to gamble regardless of whether they win or lose. Future research needs to look directly at this group.

What do you think? Comments can be addressed to Line Gebauer.

References

Fiorillo, C. D. (2004). The uncertain nature of dopamine. Molecular Psychiatry, 9(2), 122-123.

Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003). Discrete coding of reward probability and uncertainty by dopamine neurons. Science, 299(5614), 1898-1902.

Reuter, J., Raedler, T., & Rose, M. (2005). Pathological gambling is linked to reduced activation of the mesolimbic reward system. Nature Neuroscience, 8(2), 147-148.

Zack, M., & Poulos, C. X. (2007). A D2 antagonist enhances the rewarding and priming effects of a gambling episode in pathological gamblers. Neuropsychopharmacology, 32(8), 1678-1686.

The WAGER Vol. 12(8) - In the face of adversity: Do protective factors prevent risky behavior?

Researchers have focused much attention on identifying high risk populations and risk factors involved in addictive behaviors such as disordered gambling.  Less attention has been paid to resilience and the role it plays in preventing risky behavior in adolescent populations.  Luthar and Cicchetti (2000) define resilience as a person’s ability to adapt positively (e.g., not exhibit emotional, behavioral, and social problems) in the face of adversity (i.e., negative life events). In this edition of The WAGER we examine research by Lussier, Derevensky, Gupta, Bergevin, & Ellenbogen (2007) that investigated resilience and youth gambling behavior.

Researchers recruited 1,273 participants aged 12 to 19 from 12 schools across Montreal, Quebec, Canada.  Students responded to a 300 item anonymous survey during a 50 minute class period.  The survey included: the Gambling Activities Questionnaire (GAQ; Gupta & Derevensky, 1996) and the Diagnostic and Statistical Manual of Mental Disorders Multiple Response Juvenile (DSM-IV-MR-J; Fisher, 2000)  to ascertain gambling activity, as well as the Individual Protective Factors Index (IFPI; Springer & Phillips, 1992)  to assess protective and risk factors.

Analyses revealed that 7.2% and 3.2 % of the sample were classified as at-risk and probable pathological gamblers (PPGs) respectively.  The researchers used tertile splits of both the protective and risk factors and retained only the extreme highest and extreme lowest scores to form four groups of subjects: vulnerable (high risk-low protective), resilient (high risk-high protective), safe (low risk-low protective), and insulated (low risk-high protective).  Researchers found that among subjects in the vulnerable group, non-gamblers were underrepresented and there were more of the at-risk and PPGs than in the total sample.  In the insulated group, there were more non-gamblers and fewer at-risk and PPGs. (see Table 1).  Further examination revealed that the mean gambling severity score for the resilient group (M=0.3, SD=0.78) was less than a third of that of the vulnerable group (M=1.12 SD 1.74), even though the groups had similar levels of risk.

The results are promising, but the study has a few limitations.  The research design is cross-sectional and does not allow researchers to establish a temporal relationship.  Surveys were anonymous, but often conducted under the supervision of a teacher.  This might lead to underreporting of risk factors and gambling behavior.  Despite these limitations, the finding that the resilient group, although at high risk for developing problems, resembled the low risk groups in its prevalence of disordered gamblers , suggests that this group was indeed protected.  This is important from a public health perspective because it is easier to modify protective factors such as personal competence than it is to remove certain familial or environmental risk factors.  Future prevention efforts should focus more attention on increasing protective factors to prevent addictive behaviors.

What do you think?  Comments can be addressed to John Kleschinsky.

References

Fisher, S. (2000). Developing the DSM-IV criteria to identify adolescent problem gambling in non-clinical populations. Journal of Gambling Studies, 16(253-273).

Gupta, R., & Derevensky, J. L. (1996). The relationship between gambling and video game playing behavior in children and adolescents. Journal of Gambling Behavior, 12, 375-394.

Lussier, I., Derevensky, J. L., Gupta, R., Bergevin, T., & Ellenbogen, S. (2007). Youth gambling behaviors: An examination of the role of resilience. Psychology of Addictive Behaviors, 21(2), 165-173.

Luthar, S. S., & Cicchetti, D. (2000). The construct of resilience: implications for interventions and social policies. Development and Psychopathology, 12(4), 857-885.

Springer, J. F., & Phillips, J. L. (1992). Extended national youth sports program, 1991-1992 evaluation: II. Individual protective factors index (IFPI) and risk assessment study. Folsom, CA: EMT Associates.

The WAGER Vol. 12(7) - Changes in gambling problems across the lifetime: Calling “chronic” and “progressive” into question

The disease model of addiction assumes that addiction is chronic and progressive in nature. Current research suggests that addiction might not be as chronic or progressive as the model assumes. In this WAGER we examine a recent attempt by Sartor et al. (2007) to examine the course of pathological gambling over the lifetime using a gambling history measure modeled after the Lifetime Drinking History measure.

As part of a larger study of gambling behavior, researchers completed telephone interviews with 1,675 male twins from the Vietnam Era Twin Registry, 70% of those targeted. This study focuses on the 1,343 participants who recalled 25 or more lifetime gambling experiences. Interviewers administered the Lifetime Gambling History (LGH), modified from the Lifetime Drinking History developed by Skinner and Sheu (1982). The LGH identifies gambling phases (i.e., a respondent-defined period of time with consistent gambling behavior) over respondents’ lifetimes by querying respondents about significant changes in gambling activity and corresponding life events over time. For each phase, the LGH assesses the presence of gambling problem symptoms.

Participants reported an average of 1.3 gambling phases. Pathological gamblers (i.e., those who endorsed 5 or more symptoms for at least one phase) indicated an average of 3.1 phases and were also more likely (17.1% compared to 6.0% for the entire sample) to report phases of abstinence. Among participants who reported two or more phases (N=183) 48.6% reported no change in symptoms, 14.8% reported only decreases in symptoms, 13.7% reported only increases in symptoms, and 23.0% reported both positive and negative changes in symptoms over their lifetime (see Table 1).

Limitations of the current study include the retrospective self report, and the sample, which comprises only male twins between the ages of 45 to 60 who recalled gambling 25 times or more in their life. The results demonstrate that among those who reported more than one gambling phase, approximately half had symptoms that changed across time. The study did not report, though it is important to know, what proportion of those with stable symptom patterns and those with changing symptom patterns qualified as lifetime problem or pathological gamblers.  If the group with no change in symptoms consisted primarily of gamblers without problems or at-risk gamblers, the interpretation is very different than if that group consisted primarily of problem or pathological gamblers. Future research might assess the validity of the LGH by comparing the results obtained from the LGH to those obtained prospectively.

What do you think?  Comments can be addressed to John Kleschinsky.

References

Sartor, C. E., Scherrer, J. F., Shah, K. R., Xian, H., Volberg, R., & Eisen, S. A. (2007). Course of pathological gambling symptoms and reliability of the Lifetime Gambling History measure. Psychiatry Research, 152(1), 55-61.

Skinner, H. A., and Sheu, W.J.,. (1982). Reliability of alcohol use indices.  The Lifetime Drinking History and the MAST. Journal of Studies on Alcohol, 43, 1157-1170.

The WAGER Vol. 12(6): E-GOAAAAALLL! Sports Gambling on the Internet

Internet gambling has gained increasing public scrutiny during the past decade (see WAGER (12(4) and this week’s editorial), affecting policy, regulations, and public concern about its safety. There is little peer-reviewed research on Internet gambling, and the available research relies on retrospective self-report. This week, The WAGER presents the first ever research study of the actual betting behavior of a large cohort of Internet sports gamblers.

LaBrie and colleagues (in press) analyzed the actual betting behavior of the 40,499 sports gamblers who subscribed to the European Internet betting service, bwin Interactive Entertainment AG, during February 2005. Daily records of the deposits and betting transactions of these gamblers provided information during the next eight months for two types of sports wagers: fixed-odds and live-action. The current review focuses on the more typical bets on the outcomes of sporting events, fixed-odds bets.

Figure 1. Eight-Month Fixed-Odds Betting Behavior of the Sample and the Top 1%

The inset in Figure 1 shows the pattern of betting for the sample. Differences between the mean and median values demonstrate most bettors bet and lost smaller amounts than the average indicates. We found that betting activity in our sample was discontinuous; that is, a small number (1% for each variable; see Figure 1, for example) wagered and lost disproportionately high amounts.

Limitations of the current study include its use of subscribers to a single European online betting company, its inability to determine whether bettors were placing wagers on other online sites, and the absence of variables measuring participants’ financial situation. The results suggest that although the majority of the sample demonstrated moderate gambling behavior (e.g., a median loss of 33 Euros over eight study months), there is a group of online bettors (i.e., 1%) who demonstrate more excessive gambling behaviors. Future research on these gamblers is necessary to determine whether the gambling patterns of these very involved bettors are clinically relevant.

What do you think? Comments on this article can be addressed to Sarah Nelson at basis@basisonline.org.

References

LaBrie, R. A., LaPlante, D. A., Nelson, S. E., Schumann, A., & Shaffer, H. J. (2007 Online First). Assessing the Playing Field: A Prospective Longitudinal Study of Internet Sports Gambling Behavior. Journal of Gambling Studies.

The WAGER 12(5) - Addiction and Technology - VLTreatment: Can changing features of Video Lottery Terminals decrease erroneous beliefs and gambling behavior?

 Video lottery terminals (VLTs) are electronic devices that allow gamblers to play a variety of games, including card games, roulette, and spinning reel games. Multiple U.S. states and most Canadian provinces have legalized VLTs, and recent studies have indicated that disordered gamblers prefer VLTs to other games (e.g., Ladouceur, Sylvain, Boutin, & Doucet, 2002). Certain features of VLTs (e.g., lights and sounds, the speed at which players can go from game to game, and the ability to stop the reels) might increase cognitive errors and lead to increased gambling (Cloutier, Ladouceur, & Sevigny, 2006; Griffiths, 1993; Ladouceur & Sevigny, 2005). In this WAGER, we review two recent studies that examined the effect of manipulating VLT characteristics on erroneous beliefs and persistence of play.

Study 1: Pop-Up Messages about Gambling Misperceptions

Cloutier et al. (2006) administered the Inventory of Erroneous Beliefs Related to Gambling (ICROLJ) to 768 undergraduates at Laval University. Students rated their agreement with a series of erroneous beliefs using a scale from 0 to 10. Two months after initial contact with students, the researchers selected for further participation in the study the forty students who scored highest on erroneous beliefs about gambling. These participants were randomized to either the control group, which received a 7 second pause after every 15 games, or the experimental group, which received a pop-up message correcting common misperceptions about gambling after every 15 games. Researchers gave each participant 400 credits, equal to $20 Canadian dollars, to play on a VLT as long as they wished. At the conclusion of the study, participants completed the ICROLJ again to measure erroneous beliefs.

The analysis of the differences between the two groups found that the experimental group had a significant decrease on strength of erroneous beliefs, as measured by the ICROLJ, compared to the pause group (see Figure 1). However, number of games played did not differ between the two groups.

Study 2: Stopping the Reels

Ladouceur and Sévigny (2005) recruited occasional video lottery gamblers (defined as less than once per month) by using newspaper ads and bulletin boards in Quebec City and on the Laval University campus. The researchers excluded gamblers who scored five or above (i.e., qualified as pathological gamblers) on a modified telephone version of the South Oaks Gambling Screen (SOGS). The study design randomized 38 participants to either the experimental group, in which participants played a VLT with a stopping device (i.e., a feature that allows players to touch the screen in order to stop play, but does not affect the outcome of the game), or the control group whose VLTs had no stopping device. All participants experienced the same series of wins and losses. The researchers gave all participants $10 for taking part in the study and an additional $5 to begin play. Participants could stop at anytime and receive the dollar amount of the credits remaining on their VLTs up to $20.

Participants in the experimental group played twice as many games as participants in the control group, and nearly one third (32%) of the experimental group reported that they believed using the stopping device increased their chances of winning. The results of these studies are limited mainly to college students and might not be replicated in a sample of regular VLT users and problem gamblers. These studies were also conducted in controlled settings that simulated a gambling atmosphere. Casinos have markedly different environments that could modify behaviors and perhaps yield different results.

Together, these studies indicate that altering specific aspects of VLTs might affect gambling beliefs and behavior for somewhat naïve players. Though Cloutier et al. found that pop-up messages did not impact behavior compared to controls, it is plausible that the seven second delay in play experienced by the control group might have been an intervention that decreased play by giving players time to consider their options. The ability to change play patterns by altering VLT characteristics is a research area worth pursuing. Future research should include larger samples and a population that is not limited to college students. If VLT alterations such as messages, pauses, and removing stopping devices decrease gambling problems even slightly, the overall health improvement could be marked given the large proportion of problem gamblers who prefer VLTs.

What do you think? Comments on this article can be addressed to John Kleschinsky at basis@basisonline.org.

References

Cloutier, M., Ladouceur, R., & Sevigny, S. (2006). Responsible gambling tools: pop-up messages and pauses on video lottery terminals. Journal of Psychology, 140(5), 434-438.

Griffiths, M. (1993). Fruit machine gambling: The importance of structural characteristics. Journal of Gambling Studies, 9(4), 387-399.

Ladouceur, R., & Sévigny , S. (2005). Structural characteristics of video lotteries: effects of a stopping device on illusion of control and gambling persistence. Journal of Gambling Studies, 21(2), 117-131.

Ladouceur, R., Sylvain, C., Boutin, C., & Doucet, C. (2002). Understanding and treating pathological gamblers. London: Wiley.

The WAGER Vol. 12(4) - Addiction and Technology - Internet Gambling: Caught in the Web?

In 2006, the United States Senate, under former-majority leader Bill Frist, voted to make Internet gambling illegal by passing the Internet Gambling Enforcement Act (Rose, 2006). Legislation often moves at a faster pace than science. Consequently, bills and laws often reflect public opinion rather than empirical evidence. In the absence of scientific research, the Senate relied largely on anecdotal reports to support the Act. This week’s WAGER will examine the current state of research about Internet gambling and what that research can tell us about the extent and impact of such gambling.

A PsycInfo database search using the terms “Internet” and “gambling” yielded 72 peer reviewed articles, 24 of which specifically address Internet gambling. The others are articles primarily about compulsive Internet use or traditional gambling. Of those 24 articles, only 11 report original research. Since the chief concerns fueling legislation have been concerns about the perceived prevalence of Internet gambling and the extent of problematic Internet gambling, it is important to examine what current research can tell us about these rates.

Of the 11 empirical studies, five contain prevalence estimates for Internet gambling. In addition to these studies, we investigated the published reports from the four US national surveys that measured the prevalence of gambling and gambling-related problems (Gerstein et al., 1999; Kallick, Suits, Dielman, & Hybels, 1979; Petry, Stinson, & Grant, 2005; Welte, Barnes, Wieczorek, Tidwell, & Parker, 2002). The national survey conducted by Welte and colleagues (2002) was the only one which published rates of Internet gambling. Finally, we investigated studies of Internet gambling prevalence mentioned in the articles obtained through our PsycInfo search. This strategy identified four additional studies with prevalence rates for Internet gambling (including one non-peer-reviewed report: the AGA State of the States Survey), bringing the total to ten. Table 1 presents the design and findings of those 10 studies.

Table 1. Internet Gambling Prevalence Rates

§ Used Internet =16h/week and had Internet access at home for =1yr.

These results indicate that in the US general population, the rate of Internet gambling is between 0.3% and 4%. The rate found for college students falls within that range, but the research suggests that internet gambling might be higher among lower income respondents (i.e., reduced-cost health care seekers: 6.9%-8.1%), and higher still among casino gamblers. Only the studies by Petry (2006), Petry & Mallaya (2004), and Ladd & Petry (2002) provided information about gambling problems. Both the Petry (2006) and Ladd & Petry (2003) studies found that people who reported Internet gambling were more likely to have gambling problems than others. The Petry & Mallaya (2004) study found that disordered gamblers were more likely to report Internet gambling than others.

However, these studies are limited in their ability to accurately identify the extent of Internet gambling. Few of the studies used samples representative of the general population. For example, the study by Woodruff & Gregory might have obtained its elevated rate because casino gamblers are more likely to gamble on the Internet, because Detroit residents are more likely to gamble on the Internet, or because people who are willing to fill out a questionnaire when approached are more likely to gamble on the Internet. Additionally, all of the studies rely on self-report. Memory can bias this kind of data because of memory-errors and misinterpretation of questions (Williams & Wood, 2004). Adults might tend to underreport and youth to over report negative behavior. Finally, none of these studies provide information about gambling problems specifically related to Internet gambling.

To move forward, the field of gambling studies needs to conduct rigorous research on the distribution of Internet gambling, the distribution of Internet gambling problems, and the risk factors for those problems. It is time to provide legislators with empirical research using representative samples and real-time observations. Scientifically sound research is necessary to report accurately about the Internet gambling phenomenon, and to provide policy makers with valid information so that they can make informed decisions.

In the 1960’s, many parents stopped their children from placing a record needle onto a group of rock and rollers from Britain for fear of what bad influence this group might have on vulnerable youth. In the new millennium, people still institute rules based on fears and expectations rather than sound knowledge. Science has the potential to correct this longstanding tradition through systematic observation. The extent of Internet gambling and problematic Internet gambling will become evident only through the application of scientific methods. In the short-term, we can hope that legislators will examine a larger body of empirical evidence available about Internet gambling. In the long-run, careful scientific observation can provide a better understanding of Internet gambling and its potential problems.

What do you think? Comments on this article can be addressed to Erinn Walsh.

References

American Gaming Association. (2006). 2006 State of the States: The AGA Survey of Casino Entertainment. Washington D. C.: American Gaming Association.

Gerstein, D., Murphy, S., Toce, M., Hoffmann, J., Palmer, A., Johnson, R., et al. (1999). Gambling impact and behavior study: Report to the National Gambling Impact Study Commission. Chicago: National Opinion Research Center.

Griffiths, M. D. (2001). Internet gambling: Preliminary results of the first U.K. prevalence study. eGambling: The Electronic Journal of Gambling Issues Retrieved June 3, 2004, from http://www.camh.net/egambling/issue5/research/ griffiths_article.html

Ialomiteanu, A., & Adlaf, E., M. (2002). Internet gambling among Ontario Adults. Retrieved June 3, 2004, from http:// www.camh.net/egambling/issue5/research/ialomiteanu_adlaf_article.html

Kallick, M., Suits, D., Dielman, T., & Hybels, J. (1979). A survey of American gambling attitudes and behavior (Research report series, Survey Research Center, Institute for Social Research). Ann Arbor, MI: University of Michigan Press.

Labrie, R. A., Shaffer, H. J., LaPlante, D. A., & Wechsler, H. (2003). Correlates of college student gambling in the United States. Journal of American College Health, 52(2), 53-62.

Ladd, G. T., & Petry, N. M. (2002). Disordered gambling among university-based medical and dental patients: A focus on Internet gambling. Psychology of Addictive Behaviors, 16(1), 76-79.

Meerkerk, G.-J., Van Den Eijnden, R. J. J. M., & Garretsen, H. F. L. (2006). Predicting Compulsive Internet Use: It's all about sex! Cyberpsychology & Behavior, 9(1), 95-103.

Petry, N. M. (2006). Internet gambling: An emerging concern in family practice medicine? Family Practice, 23(4), 421­426.

Petry, N. M., & Mallya, S. (2004). Gambling participation and problems among employees at a university health center. Journal of Gambling Studies, 20(2), 155-170.

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.

Rose, I. N. (2006). The Unlawful Internet Gambling Enforcement Act of 2006 analyzed. Gambling and the Law Retrieved February 2, 2007, from http://www.basisonling.org/editorials.htm

Welte, J. W., Barnes, G. M., Wieczorek, W. F., Tidwell, M.-C., & Parker, J. (2002). Gambling participation in the U.S.— Results from a national survey. Journal of Gambling Studies, 18(4), 313-337.

Williams, R. J., & Wood, R. T. (2004). The proportion of gaming revenue derived from problem gamblers: Examining the issues in a Canadian context. Analyses of social issues and public policy, 4(1), 33-45.

Woodruff, C., & Gregory, S. (2005). Profile of Internet gamblers: Betting on the future. UNLV Gaming Research & Review Journal, 9(1), 1-14.

The WAGER Vol. 12(3) - Putting Loss Aversion on the (Neural) Map

Think about your friend offering you a bet over lunch. You have an equal chance of winning or losing. If you lose, you have to give him the candy bar you brought for dessert; if you win, he has to give you his dessert. How much better would his dessert have to be for you to make the wager? Better than your dessert, right? In fact, prospect theorists claim that when faced with a 50/50 chance of losing an amount of money or gaining some other amount, the potential gain has to be twice as much as the potential loss before people will accept the gamble (Kahneman & Tversky, 1979). This is because people tend to be loss averse: they assign greater weight to losses than gains. A recent study by Tom, Foxx, Trepel, and Poldrack (2007) investigated which areas of the brain are involved in these types of risky decisions. The researchers tested two possibilities that could account for loss aversion: (a) separate brain systems process gains and losses, with losses activating systems involved in processing emotions like fear and anxiety; or (b) the same brain system processes gains and losses, but represents them differently.

Tom et al. (2007) used an fMRI to scan nine female and seven male participants while they decided whether to accept a series of 256 gambles. All gambles had a 50/50 chance of winning from $10 to $40 or losing from $5 to $20. Participants received all possible win/loss combinations (e.g., a chance of winning $20 or losing $6) and selected whether they would “strongly accept,” “weakly accept,” “weakly reject,” or “strongly reject,” each gamble. To isolate the areas of the brain involved in evaluating gambles from areas involved in reacting to wins and losses, participants did not learn about the outcomes of their gambles during the scanning procedure. However, participants did bring their own money ($30 of their own and $30 provided by the researchers) to the experiment and believed that a random selection of three of the gambles they evaluated would be used to calculate their win or loss for the session.

As predicted by prospect theory, participants, on average, did not accept gambles unless the potential gain was approximately twice the potential loss. FMRI analyses showed that the greater the possible gain, the more certain brain systems known to be involved in anticipating reward(1) were activated. The greater the possible loss, the less those same systems were activated. Importantly, greater losses did not differentially activate any other brain systems, and areas such as the limbic system, which are involved in processing emotion, were not activated by increasing gains or losses.

Figure 1. Individual differences in behavioral and neural loss aversion (reproduced from Tom et al., 2007).

These results support hypothesis (b): the same brain system processes gains and losses. In fact, the researchers found that the decrease in activity in that system in response to greater possible loss was greater than the increase in activity in response to greater possible gain, suggesting that the brain itself is weighting wins and losses differently. These brain activation patterns also reflected individual differences in loss aversion. As Figure 1 shows, people who accepted riskier gambles (i.e., those lower on behavioral loss aversion) had less activation in response to increasing possible gains and less decrease in activation in response to increasing losses (i.e., lower neural loss aversion); those who were more loss averse had higher activation in response to increasing possible gains and greater decrease in activation in response to possible losses (i.e., higher neural loss aversion). Put simply, those who made riskier bets were less sensitive (neurally) to the possible gains or losses.

The current study was limited in ways common to fMRI studies and gambling experiments: the sample was small, there might have been brain activation in areas not measured by the researchers, and it was impossible to create a realistic gambling context within the scanner (though the study did allow participants to put their own money at risk). Despite these limitations, the study supported prospect theory’s prediction that people weight losses twice as much as gains, suggested that this behavior can be explained by the greater sensitivity of a single brain system to losses than wins, and showed that behavioral differences in accepting or rejecting risky gambles are reflected by different levels of activation within that brain system. These findings indicate that the average person might have a hardwired predisposition to be risk averse. Conducting a similar study with disordered gamblers might demonstrate whether these gamblers utilize the same brain systems in evaluating gambles (presumably scoring on the low end of neural loss aversion) or whether they process these gambles in a qualitatively distinct way, using different brain circuitry than the participants in the current study.

What do you think? Comments on this article can be addressed to Sarah Nelson.

Notes

1. Specifically, the researchers found that the ventromedial prefrontal cortex, anterior cingulate cortex (ACC), and orbitofrontal cortex (OFC) showed both increased activity in response to greater gains, and decreased activation in response to greater losses.

References

Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263-291.

Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315, 515-518.

The WAGER Vol. 12(2) - How You Lost the Superbowl: Illusion of Control Over an Uncontrollable Event

At one point during their January 21st NFL playoff game against the Colts, the Patriots were winning by a score of 21 to 6. The Colts won the game by four points. How could the score change so drastically? What happened? If you were a Patriots fan watching the team while they were winning, and you suddenly had the thought, “There’s no way they could lose now,” did you inadvertently contribute to the game’s unexpected outcome? In their angst, many New England football fans might have wondered if their actions or thoughts during the game somehow influenced the sad and abrupt ending to the Patriots’ season.

Clinicians use the phrase magical thinking when referring to the belief that thoughts can influence outcomes. This belief is common in patients experiencing psychotic states. However, in small amounts, magical thinking can occur in just about anyone. Magical thinking often involves the illusion of control: a belief in our ability to influence events over which we have no control, no matter how irrational we know we are being by holding such a belief. It is magical thinking that explains our feeling of responsibility for our favored sports team winning or losing.

Pronin, Wegner, and McCarthy (2006) examined magical thinking within the context of the 39th Super Bowl, when the Philadelphia Eagles narrowly lost to the New England Patriots. The researchers surveyed 58 football fans: 16 Patriot fans and 39 Eagles fans. The fans had just finished watching the game on a big screen TV in their student center. The researchers asked them how much time they spent focusing on the game while watching and for which team they rooted. They also asked participants to rate their perceived feelings of control - how responsible they felt for the outcome of the game and whether they tried to influence the outcome of the game. Researchers combined these two variables into a single variable, “perceived control.” The researchers hypothesized that the amount of time spent focusing on the game would positively correlate with the degree of perceived control.

As shown in Figure 1, the more time fans reported focusing on the game, the more control they felt over its outcome, r(56) = .40, p < .01. The regression lines plotted in Figure 1 illustrate that the relationship was similar for both the fans whose team won (r = .49) and the fans whose team lost (r = .44). Though time focusing correlated significantly with control felt, the overall amount of control fans felt for the outcome was not particularly high; even those fans in the top 25% of time spent focusing on the game perceived themselves as having controlled the outcome only “very slightly” or “a little bit” (M = 2.48; 2 = very slightly and 3 = a little bit for the responsible and influence questions that comprise “perceived control”).

Fig. 1 Perceived Control Over Outcome in Winning and Losing Fans

There are a few limitations to this study. The survey is retrospective, meaning the participants had to remember how much they thought about the game, instead of rating their attention in real time, which could have led to recall bias. Furthermore, the study is correlational, which creates the possibility that those who happened to feel more control for the outcome might have perceived themselves as having focused more on the game.

This study demonstrates a possible link between people’s investment in an outcome (e.g., focusing on the game) and the control they feel over that outcome. The fans in the study were watching the game on television, fifty miles from the location of the game, yet still felt some control. Perhaps fans at the game focus on it more and feel even more control over the outcome. Casino gamblers physically interact with their games of chance – comparable to being on the stadium field. This context lends itself to a high amount of focus, and because, according to this study, focus on the game relates to feeling of control for both positive and negative outcomes, casino gamblers might be vulnerable to feelings of control over wining and losing money during the course of the game. This illusion of control over a gambling outcome might lead gamblers to believe that they can change losses into wins or perpetuate a winning streak by behaving in a particular way (e.g., using a lucky charm, only playing certain machines, etc.) Future research is necessary to see whether Pronin et al.’s findings replicate within a gambling setting where money is at stake. By testing the level of focus on a game, researchers can monitor how that focus relates to resulting feelings of control and how both variables influence gaming decisions.

What do you think? Comments on this article can be addressed to Leslie Bosworth.

References

Pronin, E., Wegner, D., McCarthy, K., Rodriguez, S. (2006). Everyday Magical Powers: The Role of Apparent Mental Causation in the Overestimation of Personal Influence. Journal of Personality and Social Psychology, (91)2, 218-231.

The WAGER Vol. 12(1) - Where the Chips Fall: Another Look at Disordered Gambling and Other Psychiatric Disorders

Past research has shown that people with gambling problems are at higher risk for comorbid disorders, particularly substance use disorders, than the rest of the population (see WAGER 10(12)), and that people with psychiatric disorders are at higher risk than others for gambling problems (Crockford & el-Guebaly, 1998; Shaffer, Hall, & Vander Bilt, 1999). However, this research is constrained by small samples and questionable measures. This week’s WAGER focuses on a recent study of pathological gambling among a large sample of psychiatric outpatients using a structured diagnostic interview.

Zimmerman, Chelminski, and Young (2006) measured psychiatric disorders among a sample of 1,709 people seeking treatment as outpatients at a psychiatric clinic in Rhode Island. Patients in the sample either carried health insurance or were able to afford services at the clinic. Sixty-two percent of participants were female and 87% were White. Interviewers assessed participants for all DSM Axis I disorders using the Structured Clinical Interview for DSM-IV (SCID) and a DSM-IV-based module for pathological gambling (PG). Table 1 displays the prevalence rates of the assessed disorders and their comorbidity with PG.

Table 1. Prevalence Rates of Psychiatric Disorders and Lifetime Comorbidity with Pathological Gambling Among Psychiatric Outpatients (N = 1,709). (Adapted from Zimmerman et al., 2006.)


Zimmerman and colleagues found that 2.3% of the outpatient sample qualified for a lifetime diagnosis of PG. As Table 1 shows, PGs were more likely to have experienced bipolar disorder, social phobias, alcohol use disorders, and other impulse control disorders in their lifetime than the rest of the sample. PGs also experienced significantly more comorbidity during their lifetime than others in the sample, averaging 4.7 disorders. Twelve outpatients (0.7%) had current diagnoses of PG, but only three were seeking treatment through the clinic for PG.

These results indicate that lifetime PG rates are slightly elevated among psychiatric outpatients compared to rates found in the general population (i.e., 1.35%-1.85%: Shaffer et al., 1999), but that current rates are not (i.e., 0.7% compared to 0.90-1.38%: Shaffer et al., 1999). However, psychiatric outpatients who qualify for PG have significantly greater comorbidity than other outpatients.

There are a few limitations to the current study. The sample was located in a single geographic area. Because the clinic required insurance or out-of-pocket expense, the clinic population had higher SES than many people with psychiatric disorders. Both of these factors limit the generalizability of the findings. Finally, the clinic did not specialize in substance use disorders, which are known to be highly comorbid with PG; it is plausible that the comorbidity pattern would be different in a population with primary diagnoses of substance use disorders.

The findings from this study confirm the comorbidity of PG with other psychiatric disorders, but provide some evidence that PG is not particularly elevated among psychiatric samples compared to the rest of the population. Future research needs to begin to examine the temporal patterns of PG and other psychiatric disorders, identifying how much these disorders co-occur in time and whether certain disorders tend to precede or follow PG.

What do you think? Comments on this article can be addressed to Sarah Nelson.

References

Crockford, D. N., & el-Guebaly, N. (1998). Psychiatric comorbidity in pathological gambling: A critical review. Canadian Journal of Psychiatry, 43(1), 43-50.

Shaffer, H. J., Hall, M. N., & Vander Bilt, J. (1999). Estimating the prevalence of disordered gambling behavior in the United States and Canada: A research synthesis. American Journal of Public Health, 89(9), 1369-1376.

Zimmerman, M., Chelminski, I., & Young, D. (2006). Prevalence and diagnostic correlates of DSM-IV pathological gambling in psychatric outpatients. Journal of Gambling Studies, 22, 255-262.

The WAGER Vol. 11(10) - What about the Youth? Revisiting Suicidality and Gambling

People with gambling problems are likely to suffer both financially and emotionally. In particular, studies have suggested that the severity of problem gambling is related to suicidality (Ledgerwood, Steinberg, Wu, & Potenza, 2005). The relationship between suicide and gambling is a complicated one, however. The WAGER has published several reviews of this topic. The recent two part series examined the methodological issues associated with assessing the relationship between gambling and suicidality (WAGER 10(8), 10(9)). However, the WAGER has not considered the relationship between youth gambling and suicidality. This relationship is particularly important to examine since both suicidality and problem gambling have been reported to be more prevalent among adolescents than adults (Dervic et al., 2006; Zangeneh, 2005). This week’s WAGER looks at a longitudinal study that closely examines youth gambling and suicidality (Feigelman, Gorman, & Lesieur, 2006).

Feigelman et al. used data from The National Longitudinal Survey of Adolescent Health. This survey included three waves. The investigators conducted the first wave between April and December 1995 with 18,910 children enrolled in grades 7 through 12. The second wave followed up a year later with 13,570 participants from the first wave. The third wave occurred six years after the second wave with 14,322 of the original participants. The participation rate was 80 % for Wave 1, 72 % for Wave 2, and 75% for Wave 3. In addition to multiple demographic and psychosocial variables, the participants completed two items about suicidality (i.e., “During the past 12 months have you ever seriously thought about committing suicide?” and “During the past 12 months how many times have you actually attempted suicide?”).

Participants also completed four items derived from DSM-IV criteria measuring lifetime gambling problems at Wave 3. Participants who endorsed any of the gambling problem items were classified as at-risk for lifetime gambling problems. The investigators measured the relationship between suicidality and gambling problems by conducting logistic regression analyses. The analyses set suicidality measures as the dependent variables, and at-risk for gambling problems and gender as independent variables.

Figure 1: Suicide Thoughts Among Adolescents At-risk for Lifetime Gambling Problems(Adapted from Feigelman, Gorman, & Lesieur, 2006)



Figure 2: Suicide Attempts Among Adolescents At-risk for Lifetime Gambling Problems (Adapted from Feigelman, Gorman, & Lesieur, 2006)

Approximately 2% of the participants were at risk for lifetime gambling problems. The suicide attempt rate within the full sample ranged from 1.5% to 3.7% across waves, while the rate of suicidal thoughts ranged from 6.0% to 13.2%. The logistic regressions showed that problems with gambling were not directly related to suicidality at any wave (Wald .2s ranged from 0.00 to 0.47). However, as seen in Figures 1 and 2, female participants who were at-risk for gambling problems in Wave 3 (i.e., when they were between the ages of 20 and 24) were significantly more likely to think about or attempt suicide at Wave 2 (i.e., when they were in high school) compared with females who evidenced no risk for gambling problems. Male participants at-risk for gambling problems did not differ from males with no risk for gambling problems in their rate of suicidal thinking or attempts. Surprisingly, Feigelman, Gorman, and Lesieur (2006) found that male adolescents at-risk for gambling problems were more likely to be depressed than other males at all three waves; however, females at-risk for gambling problems were more likely to be depressed than other females only at Wave 3. Further analyses showed that differences in depression among females did not account for the observed relationship between at-risk for gambling problems and suicidality at Wave 2.

There are three salient limitations associated with this study. One limitation of this study is that the researchers only assessed the lifetime gambling measure during Wave 3. Participants might not have remembered accurately their past gambling behavior. Assessing gambling at a single time point makes it impossible to determine precisely how gambling and suicidality relate (i.e., whether one precedes or causes the other). Another limitation is that the investigators used a gambling measure based on only partial DSM-IV criteria. This limits their ability to compare their assessment of gambling disorder to other studies. Finally, as the authors noted, 96 participants died during the course of the study, but the cause of death was not available. It is possible that some of these missing cases represented successful suicide attempts. Thus, it is important to consider if these suicides were gambling related and or if other factors played a role.

Contrary to conventional wisdom, the current study found that gambling problems in general is not significantly related to suicidality among adolescents. However, the researchers did find a significant relationship between Wave 2 suicidality and lifetime gambling measured at wave 3 for female participants compared to the other groups. These results suggest that females who are suicidal might be more at risk for lifetime gambling problems; however, females with gambling problems are not at an increased risk for suicide. Future research needs to examine these discrepancies when examining the relationship between at-risk for gambling problems and suicidality across time; future studies also need to identify factors such as age that might influence this relationship. These findings can then better help both suicide intervention programs and gambling treatment programs to determine and help those most vulnerable to this potentially deadly interaction.

What do you think? Comments on this article can be addressed to Sarbani Hazra.

References

Dervic, K., Gould, M. S., Lenz, G., Kleinman, M., Akkaya-Kalayci, T., Velting, D., et al. (2006). Youth Suicide Risk Factors and Attitudes in New York and Vienna: A Cross-Cultural Comparison. Suicide and Life-Threatening Behavior, 36(5), 539-552.

Feigelman, W., Gorman, B. S., & Lesieur, H. (2006). Examining the Relationship Between At-Risk Gambling and Suicidality in a National Representative Sample of Young Adults. Suicide and Life-Threatening Behavior, 36(4), 396­408.

Ledgerwood, D. M., Steinberg, M. A., Wu, R., & Potenza, M. N. (2005). Self-reported gambling-related suicidality among gambling helpline callers. Psychol Addict Behav, 19(2), 175-183.

Zangeneh, M. (2005). Suicide and gambling. AeJAMH (Australian e-Journal for the Advancement of Mental Health), 4(1), 1-3.

The WAGER Vol. 11(9) - Three different paths to improvement; but, is there a difference?

Pathological gambling (PG) is associated with significant negative financial, psychosocial and health consequences (Petry, Ammerman, Bohl, Doersch, Gay, Kadden, Molina, & Steinberg, 2006). Current research estimates the prevalence of lifetime PG to be about 1.5% among U.S. adults (Shaffer, Hall, & Vander Bilt, 1999). Despite the prevalence and associated sequelae of gambling disorders, little is known about the effectiveness of current treatments for PG (Petry et al., 2006). This week’s WAGER reviews a study that evaluates the relative efficacy of three interventions for reducing gambling among treatment-seeking pathological gamblers.

Petry et al. (2006) recruited pathological gamblers (N=231) using media announcements between 1998 and 2002. The inclusion criteria for participants were: 18 years of age and older, a diagnosis of pathological gambling (DSM-IV), had gambled during the past 2 months, and could read at a 5th grade level. Participants were randomized into three groups: (a) referral to Gamblers Anonymous (GA) (n=63), (b) GA referral plus a cognitive-behavioral (CB) workbook (n=84), or (c) GA referral plus 8 sessions of individual CB therapy (n=84). Gambling and related problems were assessed at baseline, 1-month (i.e., during treatment), 2-months (i.e., post-treatment), and at 6 and 12-month follow-ups. Researchers used a module adapted from the Structured Clinical Interview for Pathological Gambling (SCIPG) (Grant, Steinberg, Kim, Rounsaville, & Potenza, 2004) to assess pathological gambling, the South Oaks Gambling Survey (SOGS) to assess gambling problems at baseline and throughout follow-up, the Addiction Severity Index (ASI) to assess other addictive disorders, and the timeline follow-back method to assess frequency and intensity of past gambling behaviors. Investigators used analysis of variance and chi-square procedures to test for statistical significance of differences across the three treatment groups.

Researchers found statistically significant differences between GA and both CB conditions at 1-month and 2-months after baseline. They reported significant improvement in number of days gambled, number of consecutive non-heavy gambling days (<$5/day), SOGS scores, and abstinence for those in the CB conditions when compared to the GA referral condition. However, at 12 months, there was no significant difference between conditions in the proportions of participants who abstained, substantially reduced, somewhat reduced, or had no change in their gambling behavior (.2 (6) = 6.25, p = .39, N=178) (See Figure 1). Furthermore, at the 12-month follow-up the percentages of participants classified as abstinent or having substantially reduced gambling were 60.5%, 60.0%, and 65.7% in GA referral, CB workbook, and CB therapy conditions, respectively (see Figure 1). The relatively high levels of success obtained regardless of treatment modality are impressive, considering that 41.3% of participants in the GA condition never attended any GA meetings and 28.9 % of participants in the CB workbook condition never completed any chapters.

Figure 1. Participants Reporting Abstinence or Substantially Reduced Gambling

* TLFB data for 6-month followup was unavailable.
*GA = Gamber's Anonymous; CB = Cognitive Behavioral

The findings of this paper support the idea that gambling disorders are dynamic and, therefore, change without regard to type of treatment (including natural recovery). This change could be caused by either the treatments themselves or other factors (e.g., people simply stopping, running out of money, or being incarcerated). Though certain therapies might have immediate short-term effects, there currently is little evidence that the long-term effects differ between GA, individual therapy, or natural recovery.
One limitation of this study is that the number of sessions was not the same across all therapy groups; therefore, any observed differences between groups might have been due to frequency of involvement and not to the type of therapy. Furthermore, these findings are only generalizable to pathological gamblers who present for treatment and not all pathological gamblers, thereby limiting these results to only a portion of this population.

Despite the limitations and negligible long-term differences among conditions, the findings of this study are important. The research suggests that intensive therapy can have immediate positive effects, but that those effects diminish over time. It also suggests that disordered gamblers can significantly improve their behavior with minimal intervention. In terms of developing effective treatments, this study suggests that developing brief interventions for gambling disorders might be a cost-effective treatment strategy. More clinical trials are necessary to evaluate this issue.

What do you think? Comments on this article can be addressed to Juan Molina.

References

Grant, J.E., Steinberg, M.A., Kim, S.W., Rounsaville, B.J., & Potenza, M.N. (2004). Preliminary validity and reliability testing of a structured clinical interview for pathological gambling. Psychiatry Research, 128, 79-88.

Petry, N.M., Ammerman, Y., Bohl, J., Doersch, A., Gay, H., Kadden, R., Molina, C., & Steinberg, K. (2006). Cognitive-Behavioral Therapy for Pathological Gamblers. Journal of Consulting and Clinical Psychology, 74(3), 555-567.

Shaffer, H.J., Hall, M.N., & Vander Bilt, J. (1999). Estimating the prevalence of disordered gambling behavior in the United States and Canada: A research synthesis. American Journal of Public Health, 89(9), 1369-1376.

Shaffer, H.J., LaBrie, R.A., & LaPlante, D.A. (2004). Laying the Foundation for Quantifying Regional Exposure to Social Phenomena: Considering the Case of Legalized Gambling as a Public Health Toxin. Psychology of Addictive Behaviors, 18(1), 40–48.

The WAGER Vol. 11(8) - Eyes on the Prize: Do lottery incentives promote gambling?

The possibility of co-occurring gambling-related disorders among substance abusing patients can influence treatment plans for recovery from substance abuse and vice versa. The use of contingency management (CM)1, an approach found to decrease the use of stimulants among users, is an effective treatment strategy. Some have argued, however, that the use of CM includes engaging patients in a prize lottery which could potentially increase gambling rates among individuals in substance abuse treatment programs (Gerstein et al., 1999; Petry, 2005; Welte et al., 2001; all as cited in Petry et al., 2006). The concern underlying this debate is a phenomenon called “addiction hopping,” where abuse of one psychoactive substance or behavior is replaced with abuse of another (Blume, 1994; Cepik, Arikan, Boratav, & Isik, 1995; Conner, Stein, Longshore, & Stacy, 1999). In this week’s WAGER, we examine a study conducted by Petry et al. (2006), which describes the changes in gambling behavior after prize-based CM in the treatment of substance abusers.

Petry et al. (2006) followed participants to track the gambling rates among stimulant abusers (N=803) in outpatient programs of eight non-methadone clinics (N=415) and six methadone clinics (N=388) and compared gambling rates of patients assigned randomly to 12 weeks of standard care (SC) with prize-based contingency management to the rates of patients receiving SC without CM. The study inclusion criteria required participants to report current stimulant use and/or provide a urine sample that was positive for stimulants. All participants provided informed consent before taking part in the study. Participants were to be excluded if they were recovering from a gambling problem, however, the investigators did not exclude any participants for this reason. To obtain baseline gambling rates, investigators administered an evaluation in which demographic and substance use questions were asked, as well as two gambling questions: “How many days have you gambled in the past month (include days you even bought lottery tickets)?” and “How much money have you spent in total on gambling in the past month?” (p. 270). Participants completed this evaluation at intake, at the first and third months of treatment, and three months after treatment (month six). In the prize-based CM condition, individuals who produced a urine sample negative for stimulants and alcohol were eligible to draw chips from a container entitling them to rewards ranging from a compliment but no prize to a television or stereo.

Petry et al. reported “no significant differences at baseline were noted between those assigned to prize and standard conditions (data not shown)” (p. 270). The study grouped participants who reported gambling at any interview (including baseline) as gamblers and compared the baseline demographics and substance use and abuse characteristics of gamblers to non-gamblers within methadone and non-methadone sites. Among non-methadone patients, gamblers were older, more likely to be males and have a diagnosis of current alcohol abuse or dependence. Among methadone patients, being African American was associated with gambling. The authors tested the differences between the SC and CM treatment groups by examining the proportion of the total sample who reported gambling across interviews. This analyses showed no significant difference between treatment groups over time at either type of sites. The investigators concluded that, “These data provide support for the safety of prize-based CM" (p. 272).

However, a re-analysis of the study results indicates that concluding that CM has no influence on gambling might be premature. We tested the difference in the proportion of gamblers (i.e., the global classification of gambling status using reported gambling at all time points) between SC and CM groups (see Table 1). For non-methadone patients, the difference in the direction of increased gambling among CM patients was statistically significant (X2 = 2.90, d.f. (1), p < .05). The difference for methadone patients was not significant.

Table 1. Distribution of gamblers at any time during the study period and non-gamblers by study groups (adapted from Petry et al. table 1).


* Petry et al. reported column percentages showing, for example, the proportion of non-gambling methadone patients in each treatment group. That particular data was incorrectly reported (i.e., the proportions did not add to 100%) in Petry et al. However, by assuming that all other entries were correct and knowing the marginal totals, we were able to correct the entries using simple arithmetic.

We should note that patients who dropped out before the first follow-up interview were defined by the gambling they reported at baseline. Assigning gambling status using pre-experimental data for dropouts signals another concern. The test of differences in proportion between treatment groups over time were based on proportions calculated for all possible subjects recruited at baseline (N=803); however, the reported retention rates indicated a marked drop-out rate particularly among non-methadone patients. The authors reported follow-up retention rates for Month 1, Month 3 and Month 6 among non-methadone clinic participants to be 78.1, 70.0, and 58.8% and among methadone clinic participants to be 91.1, 83.5, and 77.3%. Using the total sample as the denominator is the equivalent of assigning subjects with missing information to the category of non-gamblers. This biases the results in the direction of finding no increase in gambling. Clinical studies of outcomes and safety use the more cautious strategy of assuming “the worst” outcome for participants who drop out, in this case assigning them to the gambling category. This strategy biases the results against the hypothesis and is a more conservative approach. In doing so, researchers can assure that the observed differences are the least possible differences thus overcoming concern for the effect of drop-outs and missing information on the analyses.

Petry et al. did not report the actual data points used in their analysis but showed the proportion of gamblers by time as a Figure. To illustrate the impact of different ways of dealing with missing observations, we interpolated the figure from Petry et al. and generated another set of outcomes using a moderate correction for the missing observations. The moderate strategy assigned the drop-outs to gambling and non-gambling groups in the same proportion as the actual reports. Figures 1 and 2 illustrate the outcomes for both types of assignment. Consistent with our analysis of the global gambling status, the CM patients in non-methadone clinics have more gamblers than SC patients at all time points.

Despite these limitations, Petry et al.’s study addresses an important question. The study reported no “addiction hopping” to gambling among substance users as a consequence of exposure to CM; we might conclude from this article that prize-based CM does not necessarily lead to increased gambling among stimulant users. Alternative analyses indicate a different story. This review provided the BASIS with an opportunity to reinforce in its readers the need for critical consideration of the effect of sample attrition on findings. More research is necessary to reconcile these different findings. Finally, because people that reported a history of problem gambling were not included in the study, we do not know how CM affects people who abuse substances and who might be more at risk for addiction hopping. The concern that people with existing gambling disorders might be stimulated to gamble by exposure to CM remains. Future research will be necessary to investigate this matter.

What do you think? Comments on this article can be addressed to Erinn Walsh.

Notes

1. Contingency management commonly uses prize-redeemable vouchers to reward negative urine samples (Budney et al., 2000; Higgins et al., 2000, 2003; Rawson et al., 2002; all as cited in Petry et al., 2006). This technique is financially expensive.

References

Blume, S.B. (1994). Pathological gambling and switching addictions: Report of a case. Journal of Gambling Studies, 10(1), 87-96.

Cepik, A., Arikan, Z., Boratav, C., & Isik, E. (1995). Bulimia in a male alcoholic: A symptom substitution in alcoholism. International Journal of Eating Disorders, 17(2), 201-204.

Conner, B., Stein, J., Longshore, D. & Stacy, A. (1999). Associations between drug abuse treatment and cigarette use: Evidence of substance replacement. Experimental and Clinical Psychopharmacology, 7(1), 64-71.

Petry, N.M., Kolodner, K.B., Li, R., Peirce, J.M., Roll, J.M., Stitzer, M.L. et al. (2006). Prize-based contingency management does not increase gambling. Drug and Alcohol Dependence, 83, 269-273.

The WAGER Vol. 11(7) - Nalmefene: Examining a New Treatment for Pathological Gambling

There are a number of different approaches to the treatment of pathological gambling. Some examples include educational interventions (WAGER 11(4)), talk therapy, 12-step programs, s