Research Update

When a new opportunity to gamble – whether a casino, the lottery or other forms of gambling – comes into a community, assumptions swirl around about whether or not the rate of disordered gambling will increase. Does exposure to gambling opportunities pose a risk to our health and increase the rate of gambling problems in a community?

The debut of the fifth edition of the American Psychiatric Association’s (APA) Diagnostic and Statistical Manual of Mental Disorders (DSM) at the upcoming APA annual meeting might be regarded as the year’s most anticipated event in the mental health field. Its release has been covered in the media, including the Washington Post and The New York Times, and experts have weighed in with their view of how this publication will impact clinicians, researchers and those diagnosed with mental disorders. However, an initiative underway for several years at the National Institute of Mental Health (NIMH) could have more far-reaching effects on the diagnosis and treatment of mental health disorders and addiction.

In the spirit of end-of-the-year highlights lists, the NCRG staff perused the approximately 500 peer-reviewed publications released in 2012 to determine the most noteworthy studies in the field of gambling disorders and addiction.

Narrowing our list of 10 publications was a challenging task. Therefore, we decided to highlight articles that may predict future trends in research. We salute these authors and all of the investigators who are working to understand gambling disorders.

Do people with gambling disorders think differently than those without? The answer is yes and no. One might assume that a person who is diagnosed with a gambling disorder must predictably and consistently make harmful choices. While this thought is true, research on decision making has shown that all humans make consistent and predictable cognitive errors (Tversky & Kahneman, 1974). Thus, finding and exploring the differences in cognitive errors made by people with and without a gambling disorder may offer valuable insights into understanding how gambling disorders impact the decision-making process. Correcting these cognitive distortions is also an important element in several common treatments for pathological gambling, and it has been theorized that merely teaching probability and reasoning skills may be able to prevent pathological gambling before it starts (though research has not yet supported this theory).

For college students, fall can be a time where they place bets on college football games and arrange their lineup in their fantasy football leagues. Researchers have found that about 75 percent of college students have gambled in the past year and about 6 percent have experienced problems with their gambling (Barnes, Welte, Hoffman, & Tidwell, 2010).  One recent study has started to address this issue by putting to the test two promising treatment methods for college students experiencing gambling problems in a randomized controlled trial (RCT) (Larimer et al., 2011).

This study is of special interest to us at the NCRG because we recently funded one of the researchers to develop an online version of a personalized feedback intervention, one of the treatments tested in this article, for our web-based resource about gambling on college campuses: www.CollegeGambling.org. 

What if you could examine a group of seven-year-olds and predict which of them were more likely to someday develop a gambling disorder? A recent study in the journal Addiction attempts to do just that by analyzing the association between impulsive behavior at age seven and the development of problem gambling by adulthood. The authors found that, compared to their non-impulsive counterparts, children who exhibited impulsive behaviors at age seven were 3.09 times more likely to report problem gambling behavior in later years. Read on for more details about this investigation, the first to predict gambling problems over a 30-year span.

To understand how many people in a given area have a gambling problem, we must first understand how gambling disorders are defined and measured. We addressed some of the foundational questions of definition and measurement in the NCRG’s "Gambling Disorders Explained" post earlier this year. Now that the definitions have been outlined, we will examine two common methods used to discuss diseases in a population: prevalence and incidence.

What is a “social network analysis” of gambling disorders and responsible gaming? The phrase might evoke images of Mark Zuckerberg in a hooded sweatshirt, or the Oscar-winning film about the creation of Facebook. In reality, a social network analysis (SNA) is a method that researchers use to study how social connections effect behaviors, such as problem or pathological gambling.  

How many people have a gambling problem? That is the simple question posed by prevalence studies on gambling disorders. However, media reports and scientific articles reveal that this is not a simple issue. This Gambling Disorders 360˚ post is the first in a series called “Gambling Disorders Explained.” We are including these types of posts to help simplify and increase understanding of the different types of studies that exist about gambling disorders, as well as explain contributing factors to various research outcomes about this disorder.   

The urge to gamble can be a powerful force among individuals with a gambling disorder and often precipitates relapse among those who are trying to reduce or quit gambling. Researchers are aware of these urges due to studies using self-reported data, but the field of research needs to have more objective data regarding the biological factors at play. There is now evidence of the neurobiological underpinnings of urges thanks to an innovative brain imaging study from the NCRG Center for Excellence in Gambling Research at Yale University. The study used functional Magnetic Resonance Imaging (fMRI) to examine the responses to sad, happy and gambling scenarios among 10 men with pathological gambling (PG) and a control group of 11 men without gambling problems (Balodis, I.M., Lacadie, C.M., & Potenza, M.N., 2011).