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Risk Factors for Gambling Disorder: A Systematic Review

• Review Paper
• Open access
• Published: 08 March 2023
• Volume 39 , pages 483–511, ( 2023 )

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• Diana Moreira   ORCID: orcid.org/0000-0002-8257-5785 1 , 2 , 3 , 4 ,
• Andreia Azeredo 3 &
• Paulo Dias 1 , 2  

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Gambling disorder is a common and problematic behavioral disorder associated with depression, substance abuse, domestic violence, bankruptcy, and high suicide rates. In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), pathological gambling was renamed “gambling disorder” and moved to the Substance-Related and Addiction Disorders chapter to acknowledge that research suggests that pathological gambling and alcohol and drug addiction are related. Therefore, this paper provides a systematic review of risk factors for gambling disorder. Systematic searches of EBSCO, PubMed, and Web of Science identified 33 records that met study inclusion criteria. A revised study acknowledges as risk factors for developing/maintaining a gambling disorder being a single young male, or married for less than 5 years, living alone, having a poor education, and struggling financially.

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For most people, gambling is just an infrequent leisure activity that does not put their lives in danger (Wood & Griffiths, 2015 ). However, for a small rate of the world population, approximately between 0.12 and 5.8% (Calado & Griffiths, 2016 ), pathological gambling (PG) is a behavioral disorder. This disorder is defined as an inability to control gambling behavior itself (American Psychiatric Association [APA], 2013 ), leading to serious health consequences, and financial and legal problems, and representing a risk factor for aggressive behavior (Black, 2022 ). In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), PG was renamed Gambling Disorder and moved to the Substance-Related and Addiction Disorders chapter to acknowledge that PG is associated with alcohol and drug addiction (Black & Grant, 2014 ).

Custer ( 1985 ) describes PG as a multistage disease with different stages of gain, loss, and distress, while the DSM-5 (APA, 2013 ) describes PG as chronic and progressive. Recent work has shown that PG’s progression is more nuanced and, for most, has its ups and downs. Most players gradually shifted to lower levels of gaming activity, and most experienced spontaneous periods of remission. Research also shows that people who gamble recreationally (or do not gamble at all) are less likely to develop more rigorous levels of gambling activity. Still, some at-risk individuals may experience stressors that push them toward a gambling addiction (Black et al., 2017 ; LaPlante et al., 2008 ).

Despite the social and economic toll, there is very little data on predictors of PG progression. Follow-up studies are often small, underpowered, and consist primarily of treatment samples. For example, Hodgins and Peden ( 2005 ) re-interviewed 40 PG patients after an average of 40 months. Most tried to stop or reduce gambling, but more than 80% remained problem gamblers. The presence of emotional or substance use disorders was associated with poorer outcomes. Goudriaan et al. ( 2008 ) compared 24 PG patients who attended a treatment center with 22 who did not and found that relapsed patients performed worse on disinhibition and decision-making measures. Furthermore, Oei and Gordon ( 2008 ) assessed 75 Australian Gamblers Anonymous attendees to assess psychosocial predictors of abstinence and relapse. Those achieving abstinence were more involved in Gamblers Anonymous and reported better social support. More recently, research has investigated the course of gambling disorder in a sample of the general population. In the Quinte study of gambling and problem gambling, Williams et al. ( 2015 ) followed 4,121 randomly selected adults for 5 years to assess problematic behavior. They found that being a current problem gambler was the best predictor of future problem gambling. Experiencing “big wins” was also a strong predictor, as was greater gambling intensity.

Several studies have shown a high prevalence of personality disorder (PD) among those with PG, many of which focusing on the association between antisocial personality disorder and gambling (Pietrzak & Petry, 2005 ; Slutske et al., 2001 ). On the other hand, Steel and Blaszczynski ( 1998 ) observed that almost 53% of pathological gamblers have non-antisocial personality disorder. Other research papers have looked at the co-morbidity of PG with other PD. A recent meta-analysis highlighted that almost half of pathological gamblers show diagnostic criteria for a personality disorder. The majority of these were Cluster B disorders, such as borderline personality disorder, histrionic personality disorder, and narcissistic personality disorder. Other studies looked at comorbidity between PG and disorders from other clusters. There is a consistent comorbidity between PG and paranoid and schizoid personality disorders in Cluster A and with avoidant and obsessive–compulsive personality disorder in Cluster C.

Furthermore, several studies have focused on the overlap between gambling and substance use and have consistently observed significant positive associations between gambling, problem gambling, and alcohol use (Bhullar et al., 2012 ; Engwall et al., 2004 ; Goudriaan et al., 2009 ; Huang et al., 2011 ; LaBrie et al., 2003 ; Martens et al., 2009 ; Martin et al., 2014 ; Stuhldreher et al., 2007 ; Vitaro et al., 2001 ). Gambling is also significantly and positively associated with marijuana and other drug use (Engwall et al., 2004 ; Goudriaan et al., 2009 ; Huang et al., 2011 ; LaBrie et al., 2003 ; Lynch et al., 2004 ; Stuhldreher et al., 2007 ).

The concept of risk implies the concept of hazard and is associated with a high probability of adverse outcomes (Lupton, 1999 ). That is, risk exposes people to danger and potentially harmful consequences (Werner, 1993 ). However, risk varies throughout life: it varies according to life circumstances and varies from individual to individual (Cowan et al., 1996 ). Based on a literature review, Ciarrocchi ( 2001 ) described the following risk factors: age, gender, and family background. Pathological gamblers frequently gambled from an early age, suggesting that youth is a risk factor for problem gambling. Also, they are usually male and have relatives who are pathological gamblers (e.g., Cavalera et al., 2018). Regarding family background, some studies have found close relatives with gambling problems, especially parents, to be risk factors for gambling disorder (e.g., Vachon et al, 2004 ). Kessler et al. ( 2008 ) describe several risk factors for gambling disorder: male sex, low educational and socioeconomic levels, and unemployment. After a literature review, Johansson et al., ( 2009a , 2009b ) found that the following groups of risk factors were most frequently reported: (1) demographic variables (under 29; male); (2) cognitive distortions (misperception, illusion of control); (3) sensory characteristics (e.g., (4) reinforcement programs (e.g., operant conditioning); (5) delinquency (e.g., illegal behavior). Regarding older adults, Subramaniam et al. ( 2015 ) conducted a study of gamblers aged 60 or older and found that pathological gamblers were more likely to be single or divorced/separated and gambled to improve their emotional state compared to a control group and to compensate for their inability to perform activities of which they were previously capable.

Additionally, the coronavirus disease (COVID-19 pandemic) forced governments to adopt measures such as staying at home and practicing social distancing (Mazza et al., 2020 ). More adverse measures were also implemented, such as general or regional lockdowns. These stringent measures, associated with reduced social support, economic crises and unemployment, fear of the disease, increased time with the partner and reduced availability of health services, can significantly contribute to the increase of stress in an already strenuous relationship, precipitating or exacerbating gambling problems (Economou et al., 2019 ; Jiménez-Murcia et al., 2014 ; Olason et al., 2017 ). In fact, historically, in economic crises, when people experienced stress due to, for example, isolation, gambling activity per se increased, and so did gambling problems (Economou et al., 2019 ; Jiménez-Murcia et al., 2014 ; Olason et al., 2017 ), but recent studies on potential changes in gambling activity during the COVID-19 pandemic have reported different changes in behavior (Brodeur et al., 2021 ). One possible explanation might be the restrictions in place in the field of study, along with differences in study populations. Auer et al. ( 2020 ) and Lindner et al., ( 2020 ) found a substantial decrease in overall gambling activity, especially in gambling, where there were far fewer betting opportunities because of cancelled or postponed sports events such as football leagues.

Many studies have been dedicated to studying risk factors for the development/maintenance of gambling disorder. However, no study has systematically reviewed them to compile them. Therefore, this systematic review aims to explore what are the risk factors for the development/maintenance of gambling disorder. Particularly important if you can see a difference in the pattern between the pre-pandemic and the pandemic crisis.

Search Strategy

Studies were identified through search on EBSCO, PubMed, and Web of Science. The reference lists of the selected studies were also reviewed to identify other relevant studies (manual searching). The search equation in EBSCO was:

TI (gambling).

 AND TI (“contributing factor*”

 OR predictor*

 OR vulnerabilit*

 OR outcome*

 OR barrier*

 OR treatment*).

(gambling[Title]).

 AND (“contributing factor*”[Title].

 OR predictor*[Title]

 OR caus*[Title]

 OR vulnerabilit*[Title]

 OR outcome*[Title]

 OR chang*[Title]

 OR barrier*[Title]

 OR risk[Title]

 OR seek*[Title]

 OR treatment*[Title]).

And in Web of Science:

(TI = (gambling)).

 AND TI=(“contributing factor*”

The search was limited from the year 2016 and linguistic factors (Portuguese, English, Spanish, or French).

Study Selection

We had four inclusion criteria and built four corresponding exclusion criteria in response. We wanted population over 18 years old, so we excluded children and teenagers. We wanted only empirical studies, so we excluded all case studies, book chapters, theoretical essays, and systematic reviews (with or without meta-analyses). We only wanted studies involving problem or pathological gambling, so we excluded studies that did not include either of those two. Also, we wanted studies involving risk factors associated with gambling problems, so we excluded studies that did not include it. And we only wanted studies published in the last 6 years (since 2016), so we excluded the others.

The studies were selected by two independent reviewers (DM and AA), based on their titles and abstracts, according to recommendations of PRISMA guidelines (Moher et al., 2009 ).

The agreement index in the study selection process was assessed with Cohen’s Kappa and revealed almost perfect agreement,  K  = 0.98,  p  < 0.001 (Landis & Koch, 1977 ). The disagreements among reviewers were discussed and resolved by consensus.

Identification and Screening

Our database searches have retrieved 1,294 studies published between 2016 and 2023. After removing duplicates, the search outcome was reduced to to 629 unique studies. Afterwards, we examined the abstracts and excluded another 498 articles based on wrong publication type ( n  = 73), wrong theme ( n  = 335), wrong population ( n  = 72), or wrong outcome variable ( n  = 18). After full text analysis, 105 articles were eliminated, based the following criteria: wrong publication type ( n  = 13), wrong theme ( n  = 8), wrong population ( n  = 27), wrong outcome variable ( n  = 57) (Fig.  1 ). A total of 33 articles were included (seven from manual searching and 26 from the three databases). The objectives, sample ( N , age, % male), and conclusions were extracted from each study.

Flowchart of literature review process

Several studies have indicated different risk factors associated with gambling problems. At personal level, gender differences are clear and are mostly men the high-risk gamblers (Çakıcı et al., 2015 ; Çakıcı et al., 2021 ; Cunha et al., 2017 ; De Pasquale et al., 2018 ; Hing et al., 2016b ; Hing & Russell, 2020 ; Volberg et al., 2017 ), young and single (Buth et al., 2017 ; Çakıcı et al., 2015 ; Çakıcı et al., 2021 ; Hing et al., 2016a ; Hing et al., 2016b ; Hing & Russell, 2020 ; Jiménez-Murcia et al., 2020 ; Volberg et al., 2017 ). These gamblers live alone and have been married less than 5 years (Çakıcı et al., 2021 ). In terms of education, they tend to be more educated (Buth et al., 2017 ; Çakıcı et al., 2015 ; Çakıcı et al., 2021 ; Hing et al., 2016a ; Hing et al., 2016b ; Hing & Russell, 2020 ; Jiménez-Murcia et al., 2020 ; Volberg et al., 2017 ), despite some studies relate to a low level of formal education (Buth et al., 2017 ; Cavalera et al., 2017 ; Cunha et al., 2017 ; Hing et al., 2016a ; Volberg et al., 2017 ). In terms of occupation, studies found high-risk gamblers are working or studying full-time (Buth et al., 2017 ; Çakıcı et al., 2015 ; Çakıcı et al., 2021 ; Hing et al., 2016b ; Hing & Russell, 2020 ; Jiménez-Murcia et al., 2020 ; Volberg et al., 2017 ), or unemployed (Hing et al., 2016a ), have financial difficulties (Cowlishaw et al., 2016 ). At familiar level, usually grew up either in a single-parent home or with parents who had addiction issues (Buth et al., 2017 ; Cavalera et al., 2017 ). However, a study by Browne et al. ( 2019 ) sought to measure and assess 25 known risk factors for gambling-related harm. It concluded that sociodemographic risk factors did not demonstrate a direct role in the development of gambling harm, when other factors were controlled (Browne et al., 2019 ) (Table 1 ).

Physical and mental health are affected by gambling disorder (Black & Allen, 2021 ; Butler et al., 2019 ; Buth et al., 2017 ; Cowlishaw et al., 2016 ; Dennis et al., 2017 ), related to both psychiatric comorbidities (Bergamini, 2018 ), such as depression (Black & Allen, 2021 ; Dufour et al., 2019 ; Landreat et al., 2020 ; Rodriguez-Monguio et al., 2017 ; Volberg et al., 2017 ), anxiety (Landreat et al., 2020 ; Medeiros et al., 2016 ; Rodriguez-Monguio et al., 2017 ), and mood disorders (Rodriguez-Monguio et al., 2017 ), and substance use disorders (Bergamini, 2018 ; Cowlishaw et al., 2016 ; Rodriguez-Monguio et al., 2017 ; Wong et al., 2017 ), including excessive alcohol consumption (Browne et al., 2019 ; Hing & Russell, 2020 ). However, another study found that troublesome gambling and several of its mental health correlates—depression, anxiety, and stress—were not associated with troubling video game use (Biegun et al., 2020 ).

Regarding psychological risk factors, impulsivity was a significant risk factor (Browne et al., 2019 ; Dufour et al., 2019 ; Flórez et al., 2016 ; Gori et al., 2021 ; Jiménez-Murcia et al., 2020 ), demonstrating that active gamblers have more cognitive impulsivity and explicit gambling cognition than inactive gamblers. Also, Wong et al. ( 2017 ) found that negative psychological states (i.e., stress) significantly moderated the relationship between gambling cognitions and gambling severity. Participants who reported a higher level of stress had more stable and serious gambling problems than those who reported a lower level of stress, regardless of their level of gambling-related cognitions (Black & Allen, 2021 ; Jiménez-Murcia et al., 2020 ; Wong et al., 2017 ). Pathological gambling risk was positively correlated with dissociative experiences: depersonalization and derealization, absorption and imaginative involvement, and passive influence (De Pasquale et al., 2018 ). Also, alexithymia increases the risk of developing a gambling disorder (Bibby & Ross, 2017 ; Gori et al., 2021 ), and mediates the association between insecure attachment and dissociation (Gori et al., 2021 ). The results show a clear difference for the loss-chasing behavior (Bibby & Ross, 2017 ).

The analysis of gambling characteristics identified three distinct clinical traits of the gamblers: early and short-term onset (EOSC) (group 1), early and long-term onset (EOLC) (group 2), and late and short-term onset (LOSC) (Group 3) (Landreat et al., 2020 ). The incidence of gambling problems and the severity of gambling were higher in the EOSC group than in the other two groups. However, the onset age does not explain the gambling trajectories alone: the two clusters associated with the early onset age showed two distinct gambling trajectories, either a short-term evolution (~ 10 years) or a long-term evolution of the cluster. EOSC (~ 23 years) for the EOLC cluster. The EOLC cluster has a long history of gambling (35.4 years), they spend most money on gambling, with only 53.6% stopping gambling for at least a month. This cluster has a significantly higher preference for online gambling than other clusters. Although EOLC gamblers lived with their partners in most of the cases, they reported the lowest levels of family and social support related to gambling problems. An important feature was the absence of premorbid features of lifelong psychopathology before the onset of gambling problems. Most of LOSC gamblers preferred “pure” gambling (here understood as mere games of chance, as opposed to games that combine skill and chance). Women constituted the majority of the LOSC cluster, where game trajectories were the shortest observed in the study (Landreat et al., 2020 ).

Pathological gambling increases with the frequency (Cavalera et al., 2017 ; Hing et al., 2016b ; Hing & Russell, 2020 ) and the diversity of the games (Cavalera et al., 2017 ; Jiménez-Murcia et al., 2020 ). Pathological gamblers engaged in a higher range of games of chance, and showed more impulsive responses towards gambling opportunities, including betting on live action games, individual bets, electronic gaming machines, scratch cards or bingo, table games, racing, sports or lotteries and winning non-social games (Hing et al., 2016a ).

Furthermore, the main proximal predictors for high-risk gambling in electronic gaming machines (EGM) are higher desires, higher levels of misperceptions, higher session spend, longer sessions, separate EGM games, and EGM games in more locations (Hing & Russell, 2020 ). Normative influences from media advertising and significant others were also associated with a higher risk of problem gambling (Hing et al., 2016b ).

A study that analyzed risk factors in online gaming concluded that more frequent gambling in online EGMs, substance use while gambling, and greater psychological distress were more frequent risk factors. Specifically, in an online sports betting group and an online racing betting group, researchers found that participants were mostly male, young, spoke a language other than English, were under greater psychological stress and showed more negative attitudes towards the game (Hing et al., 2017 ). However, sports betting gamblers had financial difficulties, while risk factors for online race betting gamblers included betting more often on races, engaging in more forms of gambling, self-reporting as a semi-professional/professional gambler, and used illicit drugs during the game (Hing et al., 2017 ).

Furthermore, moderate/highly severe gamblers were more likely to have a poor diet, engaged less in physical activities and had a poor general health than gamblers without problems. Also, tobacco use is associated with low and moderate/highly severe gambling. Low-severity gambling, opposing to moderate/highly severe gambling, was significantly associated with binge drinking and increased alcohol consumption. Unhealthy behaviors did tend to group together, and there was a scaled relationship between the severity of gambling problems and the likelihood of reporting at least two unhealthy behaviors. Compared to problem-free gamblers, low-severity gamblers were approximately twice as likely to have low mental well-being, and moderate/high-severity players were three times more likely to have low mental well-being (Butler et al., 2019 ).

To identify gambling trajectories in poker players, a latent class growth analysis was carried out over three years. Three gambling problem trajectories were identified, comprising a decreasing trajectory (1st: non-problematic-diminutive), a stable trajectory (2nd: low-risk-stable), and an increasing trajectory (3rd: problematic gamblers-increasing). The Internet as the main form of poker and the number of games played were associated with risk trajectories. Depression symptoms were significant predictors of the third trajectory, while impulsivity predicted the second trajectory. This study shows that the risk remains low over the years for most poker players. However, vulnerable poker players at the start of the study remain on a problematic growing trajectory (Dufour et al., 2019 ).

Regarding gender differentiation, studies have shown differences between the empirical groupings of men and women on different sociodemographic and clinical measures. In men, the number of DSM-5 criteria for disordered gambling (DG) reached the highest relative importance. This was followed by the degree of cognitive bias and the number of gambling activities. In women, the number of gambling activities reached the highest relative importance for grouping, followed by the number of DSM-5 criteria for PG. The relevance of the grouping was achieved by the cognitive bias (Jiménez-Murcia et al., 2020 ).

Women showed a preference for easy bets (easy bets are considered safer, therefore, with a greater chance of winning), electronic gambling machines, scratch cards or bingo for reasons other than socializing, earning money, or for general entertainment (Hing et al., 2016a ). Women also reported greater problem severity and shorter problem duration, greater pain, and lower quality of life than men (Delfabbro et al., 2017 ; Kim et al., 2016 ). Men prefer to bet on EGMs, table games, races, sports, or lotteries and win non-social games (Hing et al., 2016a ), and were more likely to exhibit aggressive behavior towards gaming equipment (Delfabbro et al., 2017 ). Men differed more between problem gamblers and non-problem gamblers, either through signs of emotional distress or trying to hide their presence in the game room from others. Among women, signs of anger, decreased care and attempts to obtain credit were the most prominent indicators (Delfabbro et al., 2017 ).

The risk of developing pathological gambling was higher for men with less education and less adaptive psychorelational skills. On the other hand, women with higher levels of education and more adapted psychorelational functioning were more likely to become pathological gamblers. Notwithstanding, the odds of being a pathological non-gambler (anything other than a pathological gambler) were higher for women with a high educational level and more adaptive psychorelational functioning (Cunha et al., 2017 ).

Risk Factors for Increased Online Gambling During COVID-19

During 2020/21 almost one-quarter of online gamblers increased their gambling during lockdown (Bellringer & Garrett, 2021 ; Fluharty et al., 2022 ; Swanton et al., 2021 ), with this most likely to be on overseas gambling sites, instant scratch card gambling and Lotto (Bellringer & Garrett, 2021 ; Price et al., 2022 ). The sociodemographic risk factor for increased online gambling was higher education (Bellringer & Garrett, 2021 ), or low education (Fluharty et al., 2022 ), and financial difficulties related to COVID (Price et al., 2022 ; Swanton et al., 2021 ).

The studies indicate a link between change in online gambling involvement during COVID-19 and increased mental health problems (Price et al., 2022 ), including stress from boredom (Fluharty et al., 2022 ), and higher levels of depression and anxiety (Fluharty et al., 2022 ; Price et al., 2022 ).

Behavioral risk factors included being a current low risk/moderate risk/problem gambler, a previously hazardous alcohol drinker (i.e., excessive) or past participation in free-to-play gambling-type games (Bellringer & Garrett, 2021 ), and alcohol consumption (Fluharty et al., 2022 ; Swanton et al., 2021 ). Financial well-being showed strong negative associations with problem gambling and psychological distress (Swanton et al., 2021 ).

As lockdown restrictions eased, ethnic minority individuals who were current smokers and were less educated were more likely to continue gambling more than usual (Fluharty et al., 2022 ).

With this systematic review, we aimed at exploring what are the risk factors for the development/maintenance of gambling disorder. We also searched the literature for information on differences between pre-pandemic gambling patterns and gambling patterns today. A total of 33 studies examined risk factors associated with gambling problems in adults.

Studies, with mixed samples, have shown several risk factors associated with risk problems for problem or pathological gamblers, namely being male, young, single or married less than 5 years, living alone, having a low level of education, and having financial difficulties.

As for relationships, pathological gamblers have greater difficulties in family and social relationships than non-players (Cowlishaw et al., 2016 ; Landreat et al., 2020 ). And they even increase the risk of gambling when they grew up with a single parent (Buth et al., 2017 ) or parents with addiction problems (Buth et al., 2017 ; Cavalera et al., 2017 ; Hing et al., 2017 ).

About health, there is a consensus that gambling addiction decreases quality of life, a reflection of worse mental health (Buth et al., 2017 ; Butler et al., 2019 ; Cowlishaw et al., 2016 ; Dennis et al., 2017 ; Delfabbro et al., 2017 ). Studies have shown a comorbidity of gambling problems with higher levels of stress (Hing et al., 2017 ; Wong et al., 2017 ), higher levels of impulsivity (Browne et al., 2019 ; Dufour et al., 2019 ; Gori et al., 2021 ; Jiménez-Murcia et al., 2020 ; Flórez et al., 2016 ), cognitive distortions (Black & Allen, 2021 ; De Pasquale et al., 2018 ), and various pathologies, namely, anxiety (Fluharty et al., 2022 ; Landreat et al., 2020 ; Medeiros et al., 2016 ; Rodriguez-Monguio et al., 2017 ), schizophrenia (Bergamini, 2018 ), bipolar disorder (Bergamini, 2018 ), depression (Bergamini, 2018 ; Black & Allen, 2021 ; Dufour et al., 2019 ; Fluharty et al., 2022 ; Landreat et al., 2020 ), alexithymia (Bibby & Ross, 2017 ; Gori et al., 2021 ), mood disorders (Rodriguez-Monguio et al., 2017 ), and substance use disorders (Bergamini, 2018 ; Buth et al., 2017 ; Butler et al., 2019 ; Browne et al., 2019 ; Cowlishaw et al., 2016 ; Flórez et al., 2016 ; Fluharty et al., 2022 ; Hing & Russell, 2020 ; Hing et al., 2017 ; Rodriguez-Monguio et al., 2017 ).

As for the type of game, gamblers who played more than one game, and had longer gambling sessions, were at greater risk of problem gambling (Cavalera et al., 2017 ; Hing et al., 2016a ; Hing & Russell, 2020 ; Jiménez-Murcia et al., 2020 ).

However, two studies presented different data (Biegun et al., 2020 ; Çakıcı et al., 2015 ). Biegun et al. ( 2020 ), did not find an association between problem gambling and various mental health correlates, such as depression, anxiety, and stress. In another study, players had higher levels of education and were employed, contrary to data found so far. However, it is necessary to bear in mind that the study was developed in Cyprus and, as the authors themselves mention, it is a country with sociocultural characteristics, such as a history of colonization, socioeconomic problems, and high unemployment (Çakıcı et al., 2015 ), which may justify that only people with income can become addicted to gambling.

With the COVID-19 pandemic, online gamblers have increased their gambling (Bellringer & Garrett, 2021 ), aggravating the psychological and social consequences for people with problematic gambling behaviors (Håkansson et al., 2020 ; Yayha & Khawaja, 2020 ). The authors highlighted the removal of protective factors, including structured daily life (Yayha & Khawaja, 2020 ), boredom (Fluharty et al., 2022 ; Lindner et al., 2020 ), depression and anxiety (Fluharty et al., 2022 ), as well a financial deprivation (Price, 2020; Swanton et al., 2021 ), as the main reasons for the increase in gambling problems during the COVID-19 pandemic. It is well known that the daily lives of many people have been substantially altered, with a high degree of homeschooling for school children and students (Tejedor et al., 2021 ), also with likely negative effects for young people and their families (Thorell et al., 2021 ). Likewise, restrictions related to COVID-19 and changes in the lives of many people have led to significant job insecurity, unemployment, and financial problems, as well as fear of illness and mortality, which has increased emotional distress (Shakil et al., 2021 ; Swanton et al., 2021 ). Researchers have expressed concerns that COVID-19 would have consequences for the mental health (Holmes et al., 2020 ; Zheng et al., 2021 ), as well as substance use disorders, and it is important to adapt treatment during the pandemic. (Marsden et al., 2020 ). The increase in the incidence and prevalence of behavioral addictions and the relevance of the early onset of the problem of gambling disorder, with its serious consequences, make it necessary to better understand these problems to develop and adapt prevention and treatment programs to the specific needs of according to sex and age. Furthermore, understanding gender-related differences is of great importance in treating behavioral addictions.

The growing availability of gambling in recent decades, a low social knowledge about gambling disorders, and a perception of gambling more in terms of moral weakness than a psychological/psychiatric disorder have an impact on the social acceptance of gambling behaviors (e.g., Hing et al., 2015 ; Petry & Blanco, 2013 ; St-Pierre et al., 2014 ).

This systematic review presents limitations. As in all systematic reviews, there is the risk of reporting bias. As only studies published in identifiable sources were included, unpublished studies may be more likely to not have significant results, thus indicating the absence of risk factors in the involvement in problematic or pathological gambling that we have analyzed. For this reason, we had no constraints regarding geographic and linguistic criteria. Also, the adherence to the PRISMA guidelines, including definition of accurate inclusion and exclusion criteria, the use of independent reviewers, as well as the efforts to diminish publication bias, strengthen this systematic review and better elucidate about risk factors in the involvement in problematic or pathological gambling. Another limitation of this study is the little literature on the post-COVID pathological gambling, which does not allow us to draw conclusions from comparisons. Future research would benefit from making comparisons, not just across gender, but also across culture. Researchers should further explore and understand how cultural environments influence the development of problematic gambling.

Treatment providers must consider the specificities of people with gambling disorders. Therefore, a strong educational/training background for therapists and other professionals, considering the problem of gambling disorders in the diagnosis, a better adaptation of the contents of therapeutic programs, and the creation of materials used in therapy adapted to the patient’s needs, would be very much advisable. It would also be helpful to establish therapeutic groups, ideally with at least a couple of patients with gambling disorders.

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Moreira, D., Azeredo, A. & Dias, P. Risk Factors for Gambling Disorder: A Systematic Review. J Gambl Stud 39 , 483–511 (2023). https://doi.org/10.1007/s10899-023-10195-1

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Psychosocial Determinants of Gambling Addiction: A Comprehensive Review

3 Pages Posted: 6 May 2024

Christopher Goodin

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Date Written: May 2, 2024

Gambling addiction, also known as pathological gambling or gambling disorder, is a complex behavioral addiction with significant psychosocial determinants. This comprehensive review synthesizes existing literature to elucidate the multifaceted interplay between psychosocial factors and the development and maintenance of gambling addiction. The review examines various psychological, social, and environmental determinants implicated in the etiology of gambling addiction, including cognitive distortions, impulsivity, social influences, and accessibility to gambling opportunities. Furthermore, the review discusses the implications of these determinants for prevention, intervention, and treatment strategies aimed at addressing gambling addiction.

Keywords: Gambling addiction, Pathological gambling, Psychosocial determinants, Cognitive distortions, Impulsivity, Social influences

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How gambling affects the brain and who is most vulnerable to addiction

Once confined mostly to casinos concentrated in Las Vegas and Atlantic City, access to gambling has expanded dramatically, including among children

Vol. 54 No. 5 Print version: page 62

• Personality
• Video Games

It has never been easier to place a bet. Once confined mostly to casinos concentrated in Las Vegas and Atlantic City, gambling has expanded to include ready access to lotteries and online games and video games with gambling elements for adults and children.

Sports betting is now legal in 37 states plus Washington, DC, with six more considering legislation, according to American Gaming Association data from early 2023. People can gamble around the clock from anywhere and, increasingly, at many ages, including teenagers and even young children who are well below the legal age for gambling.

As access to gambling has expanded, psychologists and other experts have become concerned not just that more people will give it a try, but that more will develop gambling problems. And while it is still too soon to know what the long-term effects will be, evidence is growing to suggest that young people, especially boys and men, are among those particularly vulnerable to gambling addiction—the same demographic most often participating in the newest forms of gambling: sports betting and video game-based gambling.

People in their early 20s are the fastest-growing group of gamblers, according to recent research. And many kids are starting younger than that. Nearly two-thirds of adolescents, ages 12 to 18, said they had gambled or played gambling-like games in the previous year, according to a 2018 Canadian survey of more than 38,000 youth funded by the government of British Columbia ( Understanding the Odds , McCreary Centre Society, 2021 [PDF, 1.1MB] ). Starting young carries a relatively high burden of psychological distress and increased chances of developing problems.

Researchers are now working to refine their understanding of the psychological principles that underlie the drive to gamble and the neurological underpinnings of what happens in the brains of gamblers who struggle to stop. Counter to simplistic assumptions about the role that the neurotransmitter dopamine plays in addictions ( Nutt, D. J., et al., Nature Reviews Neuroscience , Vol. 16, No. 5, 2015 ), research is showing variations in the volume and activity of certain areas of the brain related to learning, stress management, and rewards processing that might contribute to problematic gambling.

Understanding what makes certain people vulnerable to developing problems could ultimately lead to better strategies for prevention and treatment, and also elucidate the evolving health impacts of gambling, the consequences of starting young, and even the role that the government should play in addressing those issues.

As it stands, the National Institutes of Health has agencies dedicated to problem alcohol use and drug use, but there are no official efforts aimed at problem gambling, and there are no federal regulations against advertisements for sports betting, said social worker Lia Nower, JD, PhD, director of the Center for Gambling Studies at Rutgers University in New Jersey. That means kids can see ads, often featuring their sports heroes promoting gambling, at any time of day or night. “It’s the wild, wild west with regard to gambling,” Nower said.

Examining the risks

Most adults and adolescents in the United States have placed some type of bet, and most do it without problems. But a significant subset of people who start gambling go on to develop gambling disorder, defined in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) as a persistent, recurrent pattern of gambling that is associated with substantial distress or impairment.

Gambling problems, previously called pathological gambling, were considered an impulse control disorder until 2013, when the DSM-5 classified them as an addictive disorder. That made gambling addiction the first, and so far the only, defined behavioral addiction in the clinical section of DSM-5 (with some hints that video gaming disorder might ultimately follow, experts say). Like addictions to alcohol and drugs, gambling addictions are characterized by an increasing tolerance that requires more gambling as time goes on to feel satisfied. People with the disorder can also experience withdrawal that causes irritability when they try to quit.

Over the last 20 years or so, researchers have refined their understanding of how common gambling addictions are and who is most vulnerable. Among adults, the estimated proportion of people with a problem ranges from 0.4% to 2%, depending on the study and country. Rates rise for people with other addictions and conditions. About 4% of people being treated for substance use also have gambling disorder, as do nearly 7% of psychiatric inpatients and up to 7% of people with Parkinson’s disease. An estimated 96% of people with gambling problems have at least one other psychiatric disorder. Substance use disorders, impulse-control disorders, mood disorders, and anxiety disorders are particularly common among people with gambling problems ( Potenza, M. N., et al., Nature Reviews Disease Primers , Vol. 5, No. 51, 2019 ).

Vulnerability is high in people with low incomes who have more to gain with a big win, added psychologist Shane Kraus, PhD, director of the Behavioral Addictions Lab at the University of Nevada, Las Vegas. Young people, especially boys and men, are another susceptible group. Up to 5% of adolescents and young adults who gamble develop a disorder. And men outnumber women at a ratio of about 2 to 1 among people with gambling addictions, although there are a growing number of women with the disorder.

Despite concerns, scientists have yet to document a consistent rise in the rates of gambling problems in recent years, said Jeffrey Derevensky, PhD, a psychologist and director of the International Centre for Youth Gambling Problems and High-Risk Behaviours at McGill University. Still, because more people now have access to gambling, evidence suggests that overall numbers of problems appear to have risen, Derevensky said. After Ohio legalized sports betting, for example, the number of daily calls to the state’s gambling helpline rose from 20 to 48, according to the Ohio Casino Control Commission. Other states have reported similar trends.

As evidence accumulates, it is important to examine the risks without overreacting before the data are in, said Marc Potenza, PhD, MD, director of Yale University’s Center of Excellence in Gambling Research. When casinos enter a region, he said, the area may experience a transient bump in gambling problems followed by a return to normal. Given how quickly gambling is evolving with digital technologies, only time will tell what their impact will be. “We don’t want to be overly sensationalistic, but we do wish to be proactive in understanding and addressing possible consequences of legalized gambling expansion,” he said.

From gaming to gambling

After years of studying the psychological effects of video game violence, psychologist James Sauer, PhD, a senior lecturer at the University of Tasmania in Australia, took notice when Belgium became the first country to ban a feature called loot boxes in video games in 2018. Loot boxes are digital containers that players can buy for a small amount of money. Once purchased, the box might reveal a special skin or weapon that enhances a character’s looks or gives a player a competitive advantage. Or it might be worthless.

On a Skype call after the news broke, Sauer, a psychological scientist and coexecutive director of the International Media Psychology Laboratory, talked with his collaborator, psychological scientist Aaron Drummond, PhD, of Massey University in New Zealand, about Belgium’s decision. Because loot boxes represent a financial risk with an unknown reward, Belgian policymakers had categorized them as a form of gambling, and those policymakers were not the only ones. Countries and states that have passed or considered regulations on loot boxes include Australia, the Netherlands, and Hawaii. But those regulations were contentious.

Sauer and Drummond discussed the need for more science to guide the debate. “We were trying to think about how we might contribute something sensible to a discussion about whether these in-game reward mechanisms should or should not be viewed as a form of gambling,” Sauer said.

To fill the evidence gap, the researchers watched online videos of players opening loot boxes in 22 popular and recently released games that had been rated by the Entertainment Software Ratings Board as appropriate for people ages 17 and younger. Nearly half of the games met the definition for gambling, the researchers reported in 2018, including Madden NFL 18 , Assassin’s Creed Origins , FIFA 18 , and Call of Duty: Infinite Warfare ( Nature Human Behaviour , Vol. 2, 2018 ). Among the criteria for qualifying as gambling was an exchange of real money for valuable goods with an unknown outcome determined at least partly by chance. Purchased objects had value that gave an advantage in the game and sometimes could be sold or traded to others for real money.

Loot boxes tap into the same psychological principles that draw people to slot machines, Sauer said. They may deliver a big payoff, but payoffs come at random intervals. Unlike rewards given after every repetition of a behavior, this type of variable ratio reinforcement, or intermittent reinforcement, exploits a cognitive distortion that makes a player or gambler view each loss as one step closer to a win and can lead to very rapid adoption of a behavior that can then be hard to extinguish, Sauer said. Animals exhibit the same patterns. “They feel sure that the reward is coming, but they can’t know when, so they keep repeating the behavior,” he said. “They continue even as rewards become less and less frequent and even stop entirely.”

After establishing that loot boxes, which generate billions of dollars in revenue for video game companies, are often in fact a type of gambling, studies by Sauer’s group and others since then have shown that people who spend more on loot boxes are often at higher risk of developing gambling problems, and that the connection is strongest in adolescence. Scientists are now working to untangle the question of whether buying loot boxes can cause gambling addictions, and at least some evidence supports this kind of gateway idea.

In one survey of 1,102 adults in the United Kingdom, about 20% of gamblers said that loot boxes were their first introduction to gambling and that their experiences with the game rewards made them think that other forms of gambling could be fun, according to a 2022 study ( Spicer, S. G., et al., Addictive Behaviors , Vol. 131, No. 107327, 2022 ). More than 80% of them had started buying loot boxes before they were 18. More recently, Canadian researchers surveyed hundreds of young adult video gamers at two time points, 6 months apart. Among those who were not gamblers when the study started, dozens went on to gamble over the course of the study, they reported in 2023, suggesting that loot boxes had opened the gambling floodgates ( Brooks, G. A., & Clark, L., Computers in Human Behavior , Vol. 141, No. 107605, 2023 ).

But the relationship can also go the other way. People who already gambled, the Canadian researchers found, spent more on loot boxes. And in the U.K. research, about 20% of people who started out with other types of gambling migrated to loot boxes—the same proportion that went in the other direction. Figuring out how loot boxes and gambling behavior influence each other remains a work in progress. “We just don’t have the data yet to understand the long-term consequences,” Sauer said.

Also contentious is the question of how loot boxes affect mental health. Sauer’s group has found a link between spending on loot boxes and severe psychological distress ( Scientific Reports , Vol. 12, No. 16128, 2022 ), while other research has failed to find the same association. Because kids are increasingly being exposed to gambling, it is an important question to sort through. “Some researchers have argued,” Sauer said, “that if we don’t want kids engaging with bona fide gambling behaviors, maybe we want to be wary about kids engaging with these...gambling-like reward mechanisms.”

Early exposure

Loot boxes are not the only avenue to gambling for kids. Online games that simulate gambling without financial risk are often available to very young children, said Derevensky, who once watched a young girl play a slot machine game on a tablet installed in an airport waiting area. She was earning points, not real money, and loving it. “She’s winning, and she’s saying to her dad, ‘I can’t wait until I play it for real,’” he said. “She must’ve been no more than 6 years old.”

By adolescence, about 40% of people have played simulated gambling games, studies show. These games often involve more winning than their real-world equivalents, Derevensky said. And that playful introduction without financial stakes can spark an interest. Work by his group and others has shown that teens who play simulated gambling games for points are at higher risk of having gambling problems later on ( Hing, N., et al., International Journal of Environmental Research and Public Health , Vol. 19, No. 17, 2022 ).

Seeing parents, siblings, or other members of the household gamble also normalizes gambling for kids, making them more likely to engage in gambling and other risky behaviors, including alcohol and drug use, Nower has found in her research ( Addictive Behaviors , Vol. 135, No. 107460, 2022 ). And the earlier kids get exposed to gambling through online games and other avenues, studies suggest, the more severe their gambling problems are likely to be later on ( Rahman, A. S., et al., Journal of Psychiatric Research , Vol. 46, No. 5, 2012 ).

“Kids as young as preschool are being bombarded with requests to buy things in video games,” Nower said. “A lot of kids move from betting on loot boxes in video games to playing social casino games that are free and then triage them to pay sites. You can’t really tell gambling from video gaming anymore. There’s so much overlap.”

The brain of a problem gambler

To understand why early exposure makes a difference, and why a subset of people develop gambling addictions, some scientists have been looking to the brain.

Studies have linked gambling disorders to variations in a variety of brain regions, particularly the striatum and prefrontal cortex, which are involved in reward processing, social and emotional problems, stress, and more. Some of these differences may be attributable to genetics. Twin studies and modeling work suggest that genes explain half or more of individual differences with gambling problems, specifically.

In people with gambling disorders as well as substance use disorders, a meta-analysis found that several studies showed less activity in the ventral striatum while anticipating monetary rewards ( Luijten, M., et al., JAMA Psychiatry , Vol. 74, No. 4, 2017 ). Along with other findings, those results suggest that this part of the brain contributes to impulsive behaviors for people with gambling problems.

Among other emerging insights, people with gambling problems also have smaller volumes in their amygdala and hippocampus, two regions related to emotional learning and stress regulation. Brain research might help explain why teenagers are particularly susceptible to gambling, Potenza said, including the observation that different parts of the brain mature at different rates in ways that predispose teenagers to gambling and other risk-taking behaviors. The prefrontal cortex, which regulates impulsivity and decision-making, is particularly late to develop, especially in boys.

Parsing out the details could lead to new treatments, Potenza said. For example, he and colleagues stimulated the prefrontal cortex of people with problematic gaming behavior and found improvements in their ability to regulate cravings and emotions ( European Neuropsychopharmacology , Vol. 36, 2020 ). The U.S. Food and Drug Administration has begun approving neuromodulatory approaches for using targeted brain stimulation to treat psychiatric conditions, including addictions, that could eventually help people with gambling problems, Potenza said.

New strategies for treatment would be welcome, experts say, as gambling is a particularly tricky addiction to treat, in part because it is easy to hide. As many as 90% or more of people with gambling problems never seek help ( Bijker, R., et al., Addiction , Vol. 117, No. 12, 2022 ).

For now, cognitive behavioral therapy is the most common form of treatment for gambling addiction, Nower said, and identifying pathways can tailor therapy to particular needs. She has proposed three main pathways that can lead to gambling problems ( Addiction , Vol. 117, No. 7, 2022 ). For one group of people, habitual gambling pushes them to chase wins until they develop a problem. A second group comes from a history of trauma, abuse, or neglect, and gambling offers an escape from stress, depression, and anxiety. A third group may have antisocial or impulsive personalities with risk-taking behaviors.

Betting on the game

For young adults who have grown up with video games and online gambling games, sports betting is the newest frontier—for both gamblers and researchers interested in understanding the consequences of early exposure to gambling.

Now legal in many states, the activity has exploded in popularity. An estimated 50 million people were expected to bet some $16 billion on the Super Bowl this year, according to the American Gaming Association, more than double the amount wagered the year before. (Official numbers are not yet available and are usually an underestimate because of “off the books” betting, Nower said.) At its peak, according to news reports, the betting platform FanDuel reported taking 50,000 bets per minute. Billions more were expected to be bet on March Madness.

Sports bettors trend young: The fastest-growing group of sports gamblers are between 21 and 24 years old, according to an analysis by Nower’s group of data from New Jersey, which legalized sports gambling in 2018. Compared with other kinds of gambling, the in-game betting offered during sports games is highly dependent on impulsivity, Nower said. There are opportunities to place bets during the game on everything from who will win the coin toss to which quarterback will throw 100 yards first to how long the national anthem will last. And impulsivity is particularly common in younger people and among sports fans caught up in the emotion of a game, Nower said.

Researchers are still collecting data to see if sports betting is causing a true surge in gambling problems, said Kraus, who is working on a longitudinal study of sports bettors that is following about 4,000 people over a year to see who is most likely to go from betting on a game to having problems with gambling. His group just collected their third wave of data and will be writing up a paper on their results in the coming months. “We’re going to be riding on this issue for years,” he said.

Early signs from Nower’s research in New Jersey suggest that people who engage in sports betting appear to develop gambling problems at particularly high rates and are at higher risk for mental health and substance use problems compared with other kinds of gamblers. About 14% of sports bettors reported thoughts of suicide and 10% said they had made a suicide attempt, she and colleagues found in one New Jersey study.

“Risk-takers who like action can get really involved in sports wagering,” Nower said. “Because of gambling on mobile phones and tablets, there’s no real way to keep children from gambling on their parents’, friends’, or siblings’ accounts. And they’re being bombarded with all these advertisements. This is a recipe for problems among a lot of young people.”

It takes time for a gambling problem to develop, and simple steps can interrupt the progression for many people, Kraus said. That might include placing a limit on how much they are going to spend or setting an alarm to remind them how long they have been gambling.

Education before people try gambling would help, Derevensky said, and plenty of prevention programs exist, including interactive video games designed by his group. But kids do not often get access to them. Teachers are not monitoring lunch tables for gambling activity, Nower said. And administrators are not screening for problems. Derevensky recommends that parents talk with kids about loot boxes and other gambling games and explain the powerful psychological phenomena that make them appealing.

“We educate our kids in our school systems about alcohol use, drug use, drinking and driving, and unprotected sex,” Derevensky said. “It’s very difficult to find jurisdictions and school boards that have gambling prevention programs.”

Further reading

Sports betting around the world: A systematic review Etuk, R., et al., Journal of Behavioral Addictions , 2022

The migration between gaming and gambling: Our current knowledge Derevensky, J. L., et al., Pediatric Research and Child Health , 2021

The intergenerational transmission of gambling and other addictive behaviors: Implications of the mediating effects of cross-addiction frequency and problems Nower, L., et al., Addictive Behaviors , 2022

National Problem Gambling Helpline

Gamblers Anonymous

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• Published: 04 February 2021

The association between gambling and financial, social and health outcomes in big financial data

• Naomi Muggleton   ORCID: orcid.org/0000-0002-6462-3237 1 , 2 , 3 ,
• Paula Parpart 2 , 3 , 4 ,
• Philip Newall   ORCID: orcid.org/0000-0002-1660-9254 5 , 6 ,
• David Leake 3 ,
• John Gathergood   ORCID: orcid.org/0000-0003-0067-8324 7 &
• Neil Stewart   ORCID: orcid.org/0000-0002-2202-018X 2  

Nature Human Behaviour volume  5 ,  pages 319–326 ( 2021 ) Cite this article

• Human behaviour
• Social policy

Gambling is an ordinary pastime for some people, but is associated with addiction and harmful outcomes for others. Evidence of these harms is limited to small-sample, cross-sectional self-reports, such as prevalence surveys. We examine the association between gambling as a proportion of monthly income and 31 financial, social and health outcomes using anonymous data provided by a UK retail bank, aggregated for up to 6.5 million individuals over up to 7 years. Gambling is associated with higher financial distress and lower financial inclusion and planning, and with negative lifestyle, health, well-being and leisure outcomes. Gambling is associated with higher rates of future unemployment and physical disability and, at the highest levels, with substantially increased mortality. Gambling is persistent over time, growing over the sample period, and has higher negative associations among the heaviest gamblers. Our findings inform the debate over the relationship between gambling and life experiences across the population.

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The data that support the findings of this study are available from LBG but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the authors upon reasonable request and with permission of LBG.

Code availability

Data were extracted from LBG databases using Teradata SQL Assistant (v.15.10.1.9). Data analysis was conducted using R (v.3.4.4). The SQL code that supports the analysis is commercially sensitive and is therefore not publicly available. The code is available from the authors upon reasonable request and with permission of LBG. The R code that supports this analysis can be found at github.com/nmuggleton/gambling_related_harm . Commercially sensitive code has been redacted. This should not affect the interpretability of the code.

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Acknowledgements

We thank A. Trendl and H. Wardle for comments on an earlier draft of this manuscript. We thank R. Burton, Z. Clarke, C. Henn, J. Marsden, M. Regan, C. Sharpe and M. Smolar from Public Health England and L. Balla, L. Cole, K. King, P. Rangeley, H. Rhodes, C. Rogers and D. Taylor from the Gambling Commission for providing feedback on a presentation of this work. We thank A. Akerkar, D. Collins, T. Davies, D. Eales, E. Fitzhugh, P. Jefferson, T. Bo Kim, M. King, A. Lazarou, M. Lien and G. Sanders for their assistance. We thank the Customer Vulnerability team, with whom we worked as part of their ongoing strategy to help vulnerable customers. We acknowledge funding from LBG, who also provided us with the data but had no other role in study design, analysis, decision to publish or preparation of the manuscript. The views and opinions expressed are those of the authors and do not necessarily reflect the views of LBG, its affiliates or its employees. We also acknowledge funding from Economic and Social Research Council (ESRC) grants nos. ES/P008976/1 and ES/N018192/1. The ESRC had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Contributions

P.P. and P.N. proposed the initial concept. All authors contributed to the design of the analysis and the interpretation of the results. J.G. and N.S. wrote the initial draft; all authors contributed to the revision. N.M. and P.P. constructed variables and N.M. prepared all figures and tables. D.L. established collaboration with LBG. D.L., J.G. and N.S. secured funding for the research. P.N. conducted a review of the existing literature.

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N.M. was previously, and D.L. is currently, an employee of LBG. P.P. was previously a contractor at LBG. They do not, however, have any direct or indirect interest in revenues accrued from the gambling industry. P.N. was a special advisor to the House of Lords Select Committee Enquiry on the Social and Economic Impact of the Gambling Industry. In the last 3 years, P.N. has contributed to research projects funded by GambleAware, Gambling Research Australia, NSW Responsible Gambling Fund and the Victorian Responsible Gambling Foundation. In 2019, P.N. received travel and accommodation funding from the Spanish Federation of Rehabilitated Gamblers and in 2020 received an open access fee grant from Gambling Research Exchange Ontario. All other authors have no competing interests.

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Muggleton, N., Parpart, P., Newall, P. et al. The association between gambling and financial, social and health outcomes in big financial data. Nat Hum Behav 5 , 319–326 (2021). https://doi.org/10.1038/s41562-020-01045-w

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• Published: 28 July 2022

What to target in cognitive behavioral treatment for gambling disorder—A qualitative study of clinically relevant behaviors

• Olof Molander   ORCID: orcid.org/0000-0001-5348-051X 1 , 2 ,
• Jonas Ramnerö 1 , 2 ,
• Johan Bjureberg 1 , 2 &
• Anne H. Berman 1 , 2 , 3  

BMC Psychiatry volume  22 , Article number:  510 ( 2022 ) Cite this article

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From a clinical perspective, knowledge of the psychological processes involved in maintaining gambling disorder has been lacking. This qualitative study formulated hypotheses on how gambling disorder is maintained by identifying clinically relevant behaviors at an individual level, as a means to guide the development of new cognitive behavioral interventions.

Six individuals from a treatment study, diagnosed with gambling disorder and with diverse symptom profiles of psychiatric comorbidity, were recruited. Participants were interviewed using an in-depth semi-structured functional interview and completed self-report measures assessing gambling behavior.

Functional analysis was used as a theoretical framework for a thematic analysis, which yielded the following categories: 1) antecedents that may increase or decrease gambling; 2) experiences accompanying gambling; 3) control strategies; 4) consequences of gambling behavior; and 5) events terminating gambling behavior. Few differences were identified in relation to symptom profiles of psychiatric comorbidity, although some gamblers did not report experiencing abstinence when not being able to gamble.

Conclusions

Gambling is a secluded activity mainly triggered by access to money. Positive and negative emotions could be both antecedents and functions of gambling behavior. Avoidance-based strategies used to control gambling might result in a failure to learn to control gambling behavior. Anticipation, selective attention, and chasing could be important reinforcers, which should be addressed in new developments in cognitive behavioral treatment for gambling disorder.

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Introduction

Gambling, an activity “where something of value is risked on the outcome of an event when the probability of winning or losing is less than certain” [ 1 ], is a behavior that has generated increased interest in research and clinical practice. Gambling has been called a “pure” addiction from a behavioral perspective [ 2 ], in that it lacks any form of involvement from external chemical agents, and it was the first such state acknowledged as an addiction disorder. With the introduction of the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; [ 3 ]), gambling was equated with substance use as an addiction. Gambling disorder (GD, previously called pathological gambling), includes behaviorally-based criteria such as loss of control, chasing losses, increased tolerance and gambling as an escape from aversive experiences. The past year prevalence of problem gambling , meaning gambling leading to any negative consequences, varies across countries between 0.3% and 5.3% in the general population [ 4 ]. Both problem gambling and GD are associated with severe negative consequences in important life domains such as finances, wellbeing, relationships and poorer mental and physical health, including higher rates of suicide ideation and attempts, for both the person with gambling problems and their significant others [ 5 , 6 , 7 ]. Furthermore, problem gambling and GD are highly comorbid with other psychiatric disorders [ 8 , 9 ]. Taken together, these recent developments indicate that increasing understanding of gambling as a behavior is a pivotal task, through basic research that can inform treatment development.

The phenomenon of learning and maintenance of unhealthy gambling habits has elicited a variety of attempts at explanation. It has been argued that gambling behavior has an intuitive fit to learning theories, in that gambling involves behavior under close control of rewards [ 10 ]. The phenomenon of gambling has been studied experimentally, with the investigation of several behavioral processes such as delay and probability discounting [ 11 , 12 , 13 ], reinforcement without actual winning [ 14 , 15 , 16 ], and rule-governed behavior [ 17 ]. Models of distinct gambling-related vulnerabilities have also been proposed. The Pathways model [ 18 ], suggests three subtypes manifesting impaired control over problematic gambling behavior: (1) Behaviorally conditioned gamblers who gamble due to learning processes such as conditioning and habituation; (2) emotionally vulnerable gamblers who gamble in order to relieve aversive experiences; and (3) impulsive/antisocial gamblers who gamble due to impulsive traits, substance use and antisocial behavioral tendencies. The Pathways model has gained increased prominence in the gambling field (e.g., [ 19 , 20 , 21 , 22 ]), but research has not shown whether the subtypes manifest different clinically relevant behaviors.

Treatment research on GD is a field still in its infancy. Currently, the only evidence-based treatments for GD are cognitive behavioral treatments (CBT). Clinical trials have shown CBT to be effective for reducing gambling behavior and related problems, but have failed to demonstrate differences between various treatment approaches (e.g., cognitive therapy, behavioral therapy, CBT and motivational interviewing), as well as between treatment and active control conditions [ 23 , 24 , 25 , 26 ]. Most current CBT approaches for gambling comprise a broad mixture of general CBT interventions found effective for other psychiatric conditions (for a review of treatment components, see [ 27 ]), but lack a solid theoretical base concerning the critical question of why gambling may persist despite obvious negative consequences [ 28 ].

Learning theory has served as a key inspiration for developing many psychological treatment models. However, behavioral treatment models and interventions for gambling have a less clear relation to basic experimental studies. On the other hand, experimental studies rarely involve clinical subjects or the natural environmental contingencies where the problematic gambling behavior occurs [ 29 ]. Behavioral principles are generally studied under strict observation and experimental control. In the prototypical clinical situation, i.e., talk therapy, the behaviors at hand are verbal descriptions of behavior given by the client, which may seem like a detour from a learning perspective. However, it could be argued that these narratives, in themselves, deserve attention as important data. Or as formulated by Foxall [ 30 ]:

“The testimony that people give us about their intentions, plans, hopes, worries, thoughts and feelings is by far the most important source of information we have about them” (p. 112).

A few existing qualitative studies were identified that examined gamblers’ subjective experiences in relation to their gambling, In her book, Addiction by design [ 31 ], the anthropologist Natasha Schüll interviewed members of Gambling Anonymous about their experiences of gambling. One striking feature in these subjective testimonies is that the role of winning money is downplayed as a motivating factor. Instead, a trancelike state that occurs with repetitive gambling, referred to as “the zone”, is more central. The zone is a state where the daily worries and concerns fade away in an almost dissociative manner. An interview study with a specific group of gamblers with schizophrenia found that they gambled specifically as a means to engage in a social activity, but also that their psychotic symptoms led to greater involvement in their gambling experience [ 32 ]. Finally, Hodgins & El‐Guebaly [ 33 ], interviewed recovered gamblers and found that they reported mainly emotional and financial reasons for quitting. Furthermore, the most endorsed actions taken in order to quit gambling were stimulus-control strategies, i.e., limiting access to gambling by avoiding gambling milieus or restricting access to money, and engaging in new, alternative activities. Starting with interviewing the afflicted is a clinically sensible strategy [ 34 , 35 ], but it is also in line with the American Psychological Association (APA) presidential task force on evidence-based practice in psychology [ 36 ] that advocated the use of multiple types of research evidence that can contribute to effective psychological practice, ranging from clinical observation and qualitative research to broad-scale randomized controlled trials.

In the present study we interviewed individuals with GD regarding their own perceptions of the functions of their gambling behavior, as part of our aim to develop a treatment model based on functional analysis of gambling behavior [ 28 ]. The gambling subtypes from the Pathways Model [ 18 ] were used to ensure a diverse sample of participants with GD. Self-report measures and a functional assessment interview were used to identify clinically relevant behaviors and formulate hypotheses concerning the maintenance of GD on an individual level, as a preparatory step to guide clinical interventions.

Participants

Theory-based clinical sampling was used in this qualitative exploratory study. Treatment seeking individuals with GD and other psychiatric comorbidities were purposively selected by a clinical psychologist from a separate CBT study at the Stockholm Center for Dependency Disorders [ 37 ], as representative for the gambling types delineated in the Pathways Model [ 18 ]. Participants were recruited to this study after completing all treatment sessions. To be included in our study the participant had to: (1) be identified as one of the gambling subtypes according to the Gambling Pathway Questionnaire (GPQ; [ 38 ]), (b) show a total score of >= 3 on the Problem Gambling Severity Index (PGSI; [ 39 ]), (c) be 18-85 years old, (d) read and write Swedish. Prior to inclusion in the original treatment study, participants were screened and assessed for GD and psychiatric comorbidity with the Structured Clinical Interview for Gambling Disorder (SCI-GD; [ 40 ]) and the Mini International Neuropsychiatric Interview version 7 (MINI-7; [ 41 ]). Although GD was not originally defined as an inclusion criterion, all participants in the study fulfilled GD diagnostic criteria. After six participant interviews, the representative clinical material was deemed sufficient, and no further interviews were conducted. No participants dropped out. Table 1 shows an overview of participant characteristics.

The Functional Assessment Interview [ 42 ] is an open-ended semi-structured assessment instrument for clinical behavior analysis. For the purpose of this study the Functional Assessment Interview was adapted for gambling (FAI-G). To test the feasibility of the adapted interview, it was first piloted with one participant (not included in the study). After the pilot interview, the interview was modified further and shortened to focus on key features of gambling behavior. The final FAI-G interview (see Supplementary material 1 ) consisted of the following sections: Topography of gambling behavior, Antecedents of gambling behavior, Experiences when not being able to gamble, Physiological responses, Strategies to control and/or continue gambling, Experiences during gambling and function of gambling behavior, Terminating events of gambling behavior, and Behaviors with similar functions to gambling.

Self-report measures consisted of the Gambling Pathways Questionnaire (GPQ; [ 38 ], a 48-item self-report measure for assessing etiological gambling types according to the Pathways Model [ 18 ]; the revised version of Gambling Functional Assessment (GFA-R; [ 43 ]), a 16-item self-report measure developed to assess whether gambling behavior is maintained by positive reinforcement or escape; and lastly, the Centre for Addiction and Mental Health Inventory of Gambling Situations (CAMH-IGS; [ 44 ]), a 63-item self-report measure developed to assess high risk situations in the last 12 months that may have led to gambling behavior.

Participants were recruited from a separate treatment study. After completing an informed consent form and self-report measures online, they were contacted to arrange a face-to face FAI-G interview at a location of their own choosing. Three interviews were conducted at the Stockholm Center for Dependency Disorders clinic, two at the Center for Psychiatry Research, and one in the participant’s home. All FAI-G interviews except the pilot interview (not included) were carried out by the author OM, who took field notes. The interviews were audio recorded and subsequently transcribed and pseudonymized using a study id number. The interviews lasted between 60 and 90 minutes. Participants were given two movie vouchers as compensation.

Data analysis

Qualitative analysis.

Functional analysis as a theoretical framework (e.g., [ 45 ]) was used to review and analyze the transcribed FAI-G interviews. In the first step, two raters (authors OM and JB), independently reviewed and coded each FAI-G section in the transcribed interviews into short sentences or phrases. The raters, blinded to GPQ scores, also made independent clinical assessments regarding gambling type according to the Pathways Model [ 18 ], based on each participants FAI-G responses. In the next phase, the coded sentences and phrases were condensed further and coded into single words or short phrases. Any sentence or phrase bearing individual meaning and coded by either of the raters was added into a data pool, which also included information on the FAI-G section and participant id number. After this, each coded word or short phrase in the data pool was reviewed and categorized using theoretical thematic analysis [ 46 ]. Functional analytic themes were chosen that best described the most important concepts highlighted by the participants under each FAI-G section. The categorization and interpretation were done by authors OM and JR. Frequencies of endorsed constructs and phrases were summarized for all participants, as well as for each clinically assessed Pathways subtype [ 18 ]. In the last step, the results were triangulated among all authors. Results were reported in alignment with the Consolidated Criteria for Reporting Qualitative Research (COREQ) 32-item checklist [ 47 ].

Researchers’ competence

The researchers had complementary competences within different disciplines of clinical psychology. Author OM is a clinical psychologist, PhD, and researcher with experience of CBT development. Author JR is a clinical psychologist, PhD, and associate professor, with expertise in behavioral analysis and CBT. Author JB is a clinical psychologist, PhD, and researcher with experience of emotion regulation and CBT development. Author AHB is a clinical psychologist, PhD, and professor, with expertise in addiction. Variation in coding between raters were highlighted and discussed in detail, safeguarding that all perspectives were vocalized before consensus was reached. We are satisfied that this process ensured credibility and trustworthiness in interpretation and analysis.

Quantitative analysis

Descriptive statistics were used to present participant characteristics. Measure scores (GPQ, GFA-R, CAMH-IGS) were calculated for individual participants, as well as means and standard deviations for clinically assessed Pathways subtypes. Unweighted Cohen's κ for two raters [ 48 ] was used to calculate inter-rater reliability regarding Pathways subtypes by assessor 1 (first author) and assessor 2 (third author), as well as between clinical assessments and GPQ score. Quantitative analyses were performed using R Studio version 1.1.456 [ 49 ].

The mean participant age was 34 years (Sd=9.12), with 2/6 women. Casino online was the most frequent gambling type, played by 4/6. On average, participants reported onset of gambling problems 6 years and 7 months prior to inclusion. The mean number of fulfilled GD diagnostic criteria was 7 (Sd=1.72). Participants had an average of 1.7 additional DSM-5 psychiatric diagnoses, where anxiety disorders were most common. See Table 1 for individual participant characteristics, and Table 2 for self-report measures assessing gambling behavior.

Gambling Pathways subtype assessments

Two participants were clinically assessed as conditioned (Behaviorally Conditioned Subtype 1), two as emotional (Emotionally Vulnerable Subtype 2) and two as impulsive (Antisocial, Impulsive Risk-taking Subtype 3). Compared to clinical assessment, GPQ differently identified one conditioned participant as impulsive, one emotional as conditioned, and one emotional as conditioned. Perfect agreement was achieved for clinical assessments of Pathway gambling subtype between assessors 1 and 2, κ = 1, z = 3.46, p = 0.000. Agreement between clinical assessments and GPQ result was fair, κ = 0.25, z = 0.866, p = 0.386. The clinician-assessed Pathway subtypes were used for analysis of the results.

Self-reported gambling behavior

Self-report measures indicated that gambling as a function of negative reinforcement was more common among clinically assessed emotional and impulsive gamblers, compared to conditioned gamblers. Similar results were found for positive reinforcement, but not for impulsive gamblers (see Table 2 ). In a similar vein, three antecedent high-risk situations for gambling behavior were above the clinical CAMH-IGS cut-off score, irrespective of clinically assessed gambling Pathway subtype: Negative emotions, urges and temptations, and winning and chasing.

Functional assessment of gambling behavior

Coding of participant FAI-G interviews yielded 330 phrases, of which 258 were unique. Eight phrases were categorized as “Other: Not categorizable”, and were excluded from analysis. The thematic analysis yielded 23 functional analytic themes, within the FAI-G sections. See Table 3 for examples of coding and categorization, and Table 4 for frequency of these functional themes, as coded in the participants’ interviews.

Antecedents of gambling behavior

Antecedents refer to events that occur prior to gambling behavior, that may increase or decrease the actual behavior.

All participants reported emotional events that were perceived to increase the likelihood of gambling. Emotional events were coded irrespective of their descriptive value into one theme (i.e., emotion), as the distinction between positive and negative emotional valence was far from clear cut. Thus, emotional antecedents could be described in positive terms, as when participants expressed that they often gambled after “feeling good” or “satisfied in life”. Indeed, all participants expressed that they could experience a positive emotional state of anticipation, excitement or exhilaration prior to gambling. Some, but not all, participants described negatively valued emotional antecedents. All but one participant expressed that negative emotions triggered their gambling, for example feeling “bored”, ”anxious”, “worried”, “stressed”, “sad”, or ”restless”. Others reported pre-gambling rumination, for example thinking that they ought not be gambling, or being displeased with relationships or other areas in their life. Overall, however, few participants expressed that specific gambling-related thoughts triggered their gambling, and when they did, it was often in conjunction with a positive emotional experience. Only two participants described thinking of gambling losses as a trigger for gambling. Emotional events were also reported as antecedents that could decrease the likelihood of gambling. Half of the participants described positive emotions, such as “feeling good”, “happy” or “life going in the right direction”. Two participants described that when being in a negative state, such as feeling “down”, “depressed”, “hopelessness”, or “seeing no opportunities”, they seldom gambled. Thus, emotional events could be considered as a functional theme in understanding conditions that govern gambling, whether positive or negative, and whether they increased or decreased the likelihood of gambling.

Another prominent pattern was that all participants reported that available resources (i.e., access to money) were a critical antecedent condition. For example, participant 3 described a monthly pattern where he gambled using all his salary as soon as the amount was transferred to his bank account. From there on, he lived without money for a couple of weeks feeling pretty good at not gambling and often thinking that he did not want to gamble again. However, as soon as the new salary was transferred to his bank account, he started to gamble online again until the salary was spent, often gambling the whole night long.

Social antecedents were also described by all participants. Social stimuli that were reported to increase gambling were mainly being alone (absence), while decreased gambling was mainly associated with being in contact with others (presence). However, exceptions to gambling alone were noted, for example when friends suggested gambling. Time of day was reported by all but one participant as an antecedent that might increase (mainly evenings) or decrease (mainly daytime and nights) the likelihood of gambling. In the same vein, specific locations were noted by all participants as antecedents that would increase gambling (e.g., “home”, “in my room”, “at public transportations”, or “at work”), but only 3 participants reported locations that were associated with decreased likelihood of gambling (e.g., “outside home” or “outside bedroom”).

A majority of the participants described specific preceding behaviors that either would increase or decrease the likelihood of gambling. Typical activities that would facilitate gambling included for example “browsing gambling Facebook groups”, “ruminating”, or “reading gambling statistics”. Behavior that influenced in the opposing direction typically had the character of competing responses or activities (i.e., behaviors incongruent with gambling). Further, three themes of antecedents were identified with sole functions of increasing gambling. Two participants reported specific discriminative stimuli; that is, events that would clearly signal the availability of reinforcers following gambling behaviors (e.g., gambling commercials). Losses were reported by two persons as antecedents that would increase the likelihood of gambling. Also, use of substances (alcohol and prescribed drugs) was reported by two persons.

Experiences when not being able to gamble

Participants were asked to describe their experiences of not being able to gamble. Two main functional themes were identified. One theme identified was frustrative non-reward, for example “frustration” and “irritation” or “can’t focus”. The second theme concerned the more common response which was to describe an essentially non-problematic response, such as “no anxiety or depression”, “I can interrupt gambling”, or “I can focus on other things”.

Accompanying responses

The participants were asked to identify physiological responses that would occur regularly when gambling. Three participants described positive or negative emotional arousal-related responses, such as “itchy fingers”, “pumping”,”endorphin-kick”, “fear in the body”, “excitement”, or “itchy body”. The other three did not report any such responses.

Strategies to control or continue gambling

Loss of control is a key criterion for GD [ 3 ]. The participants were asked to describe their attempts or strategies for controlling their gambling behavior. The main functional theme described concerned avoidance-based strategies, such as not owning a smartphone or a bank card reader, handing over control over their economy to significant others, blocking gambling accounts or credit cards, or extracting money in cash to prevent themselves from gambling.

“During a one-year period I handed over my finances to my brother. I also got help with budget and… making calls and so forth. I didn’t gamble for...surely one and a half years. Everything was great, but then I got it back [control over my finances]. After that I started to gamble again pretty fast.” (Participant 3)

The other strategies for controlling gambling were labeled either social strategies, for example scheduling non-gambling activities with friends, or telling their friends they had gambling problems and prohibiting them from lending them money; or monetary-based strategies, such as ceasing to borrow money for gambling, depositing only small sums in gambling accounts, or saving money to cover other minimum living expenses.

In contrast, gambling also involves using a variety of strategies that serve the opposite function: deliberate planning that enables or facilitates gambling. These responses were categorized either under the heading of enabling or securing resources, or as different kinds of planned and deliberate behaviors, for example taking out loans to gamble or cover other expenses, waiting for salary, selling possessions, lying or gambling to win, or to win back money.

Consequences of gambling

Participants were asked to describe the experiences and events that would either accompany or occur subsequent to their gambling behavior, in order to identify the possible reinforcing properties of gambling and its contextual factors. These were identified as either different emotional properties or tangible reinforcers (i.e., money).

“There were different stages. (...) First it felt like a big development for me, that I had found something (...) it was a feeling of great… a good feeling. I was happy with myself and felt I was going somewhere. Then, when the winnings were replaced with losses, and the bettings became wilder (...) I remembered my first feeling, that I had won (...) In the next step I betted more aggressively, to, sort of like, catch up to what I potentially thought I should have won (...). It became a straitjacket pretty fast when the losses mounted up and I started to chase them.” (Participant 6)

Overall, participants described that they experienced a range of emotional states while they gambled; these we categorized as positive (Emotional positive). Common descriptions were “excitement”, “kicks”, “euphoria”, “satisfaction”, or feelings of “being in control”, “being on the right path”, “invincibility”, or “growing ego”. Another functional theme was that gambling was described as serving a function to avoid aversive emotional experiences, for example “getting a break”, “escaping reality”, avoiding “responsibility”, “social interaction”, “boredom”, or “bad conscience”, or avoiding hard thoughts of “debts”, “betrayal against family” or “social failures”. All but one participant described that anxiety was fulfilling different functions in their gambling experiences. By gambling, participants avoided symptoms of anxiety, for example post-mortem ruminations on social situations, or post-traumatic memories. Interestingly, anxiety was also described as a part of the gambling activity in itself. Participants described that gambling “is a mixture between excitement and anxiety”, and “relieves anxiety in the short term, but increases it in the long term”, or “relieves gambling-related anxiety in the short term”, but it was also described as “a relief when the money's gone”, and there being a point where “it gets calm in the head”.

“Nowadays I see my gambling as a form of deliberate self-harm. Uumm … Because now I don’t gamble to… I know that I can win a lot of money. But if I win a lot of money, I will use it to gamble anyway. I rarely gamble to win, I only gamble to shield myself from reality.“ (Participant 4)

A third functional theme, “the zone”, involved participants’ experience of a state of selective attention, or focus, while they gambled. This state was described mainly in positive terms as “focus”, “being able to concentrate”, “entering a bubble”, or “all thoughts on gambling”, and was often associated with a feeling of escaping reality (sometimes also avoiding aversive thoughts or feelings), tunnel vision, lost perception of time, as well as continuing to gamble until all money were gone. For example, Participant 3 expressed that:

“I get totally stuck. I know situations where I gambled for, what I perceived as half an hour, fifteen minutes, twenty minutes. But instead… well, one and a half hours had gone by. What? I sort of like lose perception of time (...) when I win and… perceive that it goes well, and later, when you click and click, then... Well, out of money. But it went well fifteen minutes ago (...) Often when I gamble, I feel best.”

As expected, money was identified as a tangible reinforcer for gambling behavior. However, while all participants described emotional consequences, only four of them explicitly reported money as an important consequence. Overall, participants described that winning was associated with “a great feeling”, “a kick” or “euphoria”, but also that winnings resulted in “feelings of unreality”, and that the money they won lost its value and became “just numbers on the account”. Two participants described that they “chased wins”, “chased absent wins”, or “demanded absent wins” when they gambled. Participant 1 expressed that he knew he could not win money by gambling, but that these thoughts were “blocked in the brain somehow” while he gambled. Overall, participants described that losses during gambling were associated with feelings of “anger”, “frustration”, “anxiety” and “a lust for revenge”. Half of the participants described that they continued to gamble to “win back money” or “chase losses”.

Terminating events of gambling behavior

The participants were asked to identify circumstances that would terminate a period of gambling behavior. We identified two broad functional themes. The first was depleted resources, which included running out of money. All participants reported this. But it could also be physical or temporal resources, such as continuing to gamble until becoming exhausted and falling asleep or running out of time.

The second theme mainly consisted of different behaviors that served the function of terminating gambling. For example, participant 5 described that gambling sessions usually ended according to her pre-decided plan. Notably, only participant 6 described a specific time as a terminating event for gambling. This participant was the only one who gambled on the stock market (day trading) which was not accessible around the clock, as were other gambling types played online by the remaining participants.

Behaviors with functions similar to gambling

Finally, participants were asked to report other behaviors that they had engaged in, that resembled the experience of gambling. Four participants described various behaviors and activities, i.e., computer games, other games, sex, deliberate self-harm, and work tasks. However, we decided that these behaviors were too disparate to constitute a functional theme and they were therefore excluded from the thematic analysis.

Pathways Model subtypes

When comparing the clinically assessed Pathway subtypes, few clear differences in the functional properties of gambling were found. Instead, a more general gambling pattern was identified which seemed to include all participants, irrespective of subtype categorization. However, two differences were noted. First, the only participants who described frustrative non-reward responses were the two participants clinically assessed as emotionally vulnerable. Secondly, somewhat surprisingly, three participants, assessed as emotionally vulnerable and impulsive Pathway subtypes, described behaviors that enabled them to stop gambling. For example, participant 2, assessed as an impulsive gambler, described that he could stop gambling while still having money in his account if:

“(...) someone wants to go out and do something fun. If something else is happening, not just going out to drink beers. To go bowling, we go and do this, do you want to come and bathe in the sauna or go swimming. To do things.”

This study used a functional assessment interview and self-report measures to identify clinically relevant behaviors and formulate hypotheses on the maintenance of GD on an individual level, as a preparatory step for guiding clinical interventions. The Pathways model subtypes [ 18 ] were used to obtain a diverse sample of participants with GD. The study was driven by an overarching interest in clarifying the functional aspects of gambling, from a subjective participant perspective.

When investigating the context of gambling behavior, a striking feature was that study participants often reported commonplace antecedents, such as being alone, time of the day (e.g., evenings), and being at home. The most prominent antecedent reported, indeed, was access to money. This suggests that gambling could be viewed as a secluded activity, mainly triggered by access to money. For our participants, who mainly gambled online, it was possible to gamble everywhere and at any time, with the only hindrance that money was not equally available. Depleted financial resources were, consequently, described as the main terminating event of gambling by the participants. Some also described physical exhaustion or running out of time. Loss of control is often regarded as a defining feature of GD [ 3 , 18 ]. Our results indicates that it could be more complicated. All participants in this study described that they had used stimulus control strategies in order to refrain from gambling. While stimulus control strategies are endorsed among recovered gamblers [ 33 ], and typically employed as a first intervention in many treatment protocols [ 27 ], they are essentially avoidance-based strategies. One drawback with such strategies may be that the person fails to learn control of behavior in the presence of the antecedents that tend to result in gambling behavior.

Gambling is often described as an escape from negative emotions and aversive experiences [ 3 , 18 ]. Our results indeed indicated emotional antecedents for gambling. However, the link to negative emotions was not exclusive. Some participants described that positive emotions preceded their gambling, and others that negative emotions did so. Conversely, some participants expressed that positive emotions decreased the possibility for them to gamble, and others that negative ones did so. However, it should be noted that all participants expressed that they experienced an emotional state of expectancy prior to gambling. Gambling-related physiological arousal and subjective excitement is consistent with the theoretical Pathways Model [ 18 ], and has been examined in several experimental studies e.g., [ 50 , 51 , 52 ]). For example, Rockloff and Greer [ 53 ] concluded that high arousal can increase subsequent gambling behavior among at-risk players, as long as the arousal is not perceived as a negative emotion. Thus, future etiological and treatment models may consider affective antecedents regardless of valence.

The participants’ descriptions of the relationship between gambling and winning or losing money were not unanimous. While all participants but one scored above clinical cut-off at “Winning and Chasing” on the CAMH-IGS, only four of them explicitly reported money as an important consequence of gambling. Two participants described that they chased wins, and three participants that they continued to gamble to win back money they lost. The gambling activity itself was also described in relation to emotional events, where placing a bet was associated with excitement, winning with euphoria and a kick, and losing with anxiety and a lust for revenge; findings that are in line with a functional magnetic resonance imaging study by Campbell-Meiklejohn et al. [ 54 ]. Chasing, in particular chasing losses, has been proposed as a key symptom of GD [ 55 ], although experimental studies investigating this phenomenon seem rare [ 29 ]. Our results suggest that tangible reinforcers; i.e., money, might be important for gambling behavior, but probably do not account for the whole clinical picture of GD.

A more striking feature in the participants’ narratives was that they all reported a positive state of selective attention, or focus, while they gambled. While this “zone” typically is not part of existing gambling treatment protocols [ 27 ], nor of the Pathways Model [ 18 ], it is not a novel finding. As previously noted, Schüll [ 31 ], downplayed winning money as a motivating factor, and instead described the slot machine as a “zone”, where events occurring outside the gambling experience become less relevant to gamblers, as they grow completely absorbed by the game. Similarly, Dixon et al. [ 56 ] coined the expression “dark flow”, a flow-like state which has been investigated in experimental studies and found to be associated with multiline slot gambling and GD [ 56 , 57 ]. Findings from the present study are in line with the presumption that this state might be an important reinforcer for gambling behavior.

Inherent in the idea of an addiction lies the idea of craving, coupled with experiencing abstinence when access to the drug is hindered. In parallel with this, abstinence is a diagnostic criterion of GD [ 3 ]. Somewhat surprisingly, two participants in this study reported not being able to gamble as entirely non-problematic, i.e., other activities enabled them to stop gambling fairly easily despite having access to money. They experienced no negative symptoms, such as anxiety, depression or concentration problems. It should be noted that both participants were assessed as impulsive gamblers according to the Pathways Model [ 18 ], which may indicate a unique feature of this theme.

This study had several strengths. Gambling has been investigated in previous qualitative studies, but not from a clinical perspective. As previously noted, this is a sensible strategy, as treatment interventions ideally should emanate from ideographic models. Interviewing “sufferers” is often conducted to identify hypotheses of maintenance for problem behaviors, when developing novel CBT (34,35, personal communication Edna Foa). These qualitative and clinical based assessment procedures are, however, rarely published as formal systematic studies. The current paper is thus an important exception in the clinical treatment literature. This study used theory-based clinical sampling. Participants from a CBT study were purposely selected by a clinical psychologist. This ensured both richness of data and that participants were familiar with the behavioral constructs in the FAI-G interview and self-report measures.

Limitations of the study included the lack of validation of results and conclusions by reporting them back to the participants. Also, we did not use a predefined procedure to assess whether saturation was reached. However, we found that code saturation was achieved following recruitment of six participants, in line with findings by Henning et al. [ 58 ], who have studied the saturation process and found that over 80% of coding can be expected after six interviews. The first interviews generated a rich range of coding, and for the purposes of this study six participant sufficed. The use of a semi-structured interview format, based upon a predefined theoretical framework, delimited possible conclusions in the thematic analysis, and created difficulties, for example, in differentiating themes from constructs. Also, theoretical (i.e., functional, and behavioral) terms were used throughout the data collection, which might have hindered the participants’ understanding of the questions being asked. However, as the participants had undergone a recent cognitive behavioral treatment study [ 37 ], where individual clinical behavioral analyses were continuously performed, they were familiar with the theoretical constructs employed in the current study. Overall, the study was a preparatory step for developing a CBT treatment protocol, so other methodological approaches would probably have been of less clinical relevance.

With regards to the functional aspects of gambling, this study has merits from a heuristic perspective since it identified several potential processes which might be clinically relevant for GD, but typically have not been part of gambling treatment protocols (e.g., [ 27 ]). In terms of clinical implications, a treatment model and an internet-based cognitive behavioral protocol was developed, based on the results of the study. The treatment was disseminated into routine addiction care and is currently being evaluated in a feasibility study (see 28 for a study protocol). The interviews and results of the current study were completed before the treatment development and feasibility study was initiated.

In sum, the aim of the current study was to assess the subjective functions of gambling, within a diverse sample of participants with GD, ultimately with the goal of informing treatment development. The considerations could be important to address in future CBT models and treatment protocols for GD. First, access to money might be a critical antecedent for GD, and we question the use of avoidance-based control strategies in treatment if the objective is to achieve long-term control over gambling behavior. Secondly, treatment needs to address both negative and positive antecedent emotions for gambling behavior (e.g., anticipation), and not only negatively reinforced gambling behavior. Third, the gambling activity in itself seems to include emotional functions. In particular, an absorbing experience of selective attention during gambling might be an important reinforcer, and should accordingly be addressed in CBT protocols. Finally, gamblers in the impulsive subtype did not report experiencing abstinence symptoms when not being able to gamble, despite presence of critical antecedents, such as access to money. Future clinical studies could investigate this phenomenon further, using targeted interventions, such as behavior replacement.

Subjective functions of gambling behavior were identified among a sample of participants with gambling disorder, as a means to guide new developments in cognitive behavioral interventions.

Access to money might be a critical antecedent for gambling and should be addressed using non-avoidance interventions.

Treatment should address positive and negative emotions both as potential antecedents and functions of gambling behavior.

Anticipation, selective attention, and chasing might be important reinforcers for gambling.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Abbreviations

Anne H Berman, last author

The American Psychological Association

Cognitive Behavioral Treatment

The Centre for Addiction and Mental Health Inventory of Gambling Situations

The Consolidated Criteria for Reporting Qualitative Research

The Diagnostic and Statistical Manual of Mental Disorders, 5th edition.

The Functional Assessment Interview

Gambling Disorder

The Gambling Functional Assessment

The Gambling Pathway Questionnaire

Johan Bjureberg, third author

Jonas Ramnerö, second author

The Problem Gambling Severity Index

The Structured Clinical Interview for Gambling Disorder, version 7

Olof Molander, first author

Responding to and Reducing Gambling Problems Studies

The Structured Clinical Interview for Gambling Disorder

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Acknowledgements

The authors would like to thank the participants in the study. The authors would also like to thank Viktor Månsson at the Center for Psychiatry Research who recruited the participants and conducted the FAI-G pilot interview, as well as the editor and the reviewers.

Open access funding provided by Karolinska Institutet. The study was carried out within the frame of the “Responding to and Reducing Gambling Problems Studies” (REGAPS program grant), financed by the Swedish Research Council for Health, Working Life and Welfare (Forte), grant number 2016–07091; as well as development funds from the Stockholm Health Care Services, Stockholm County Council, for identification and treatment of problem gambling. The funding sources had no role in study design, data handling, writing, or submission of the article.

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Author OM conducted all interviews, except the pilot interview. OM and JB reviewed and coded the transcribed interviews. Author OM and JR did the categorization and interpretation and wrote the first draft manuscript. Author AHB provided expert knowledge in qualitative methods. All authors contributed to the process of finalizing the manuscript. The authors read and approved the final manuscript.

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Functional assessment interview for gambling (FAI-G).

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Molander, O., Ramnerö, J., Bjureberg, J. et al. What to target in cognitive behavioral treatment for gambling disorder—A qualitative study of clinically relevant behaviors. BMC Psychiatry 22 , 510 (2022). https://doi.org/10.1186/s12888-022-04152-2

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BRIEF RESEARCH REPORT article

Gambling behavior and risk factors in preadolescent students: a cross sectional study.

• Department of Psychology, Niccolò Cusano University, Rome, Italy

Although gambling was initially characterized as a specific phenomenon of adulthood, the progressive lowering of the age of onset, combined with earlier and increased access to the game, led researchers to study the younger population as well. According to the literature, those who develop a gambling addiction in adulthood begin to play significantly before than those who play without developing a real disorder. In this perspective, the main hypothesis of the study was that the phenomenon of gambling behavior in this younger population is already associated with specific characteristics that could lead to identify risk factors. In this paper, are reported the results of an exploratory survey on an Italian sample of 2,734 preadolescents, aged between 11 and 14 years, who replied to a self-report structured questionnaire developed ad hoc . Firstly, data analysis highlighted an association between the gambling behavior and individual or ecological factors, as well as a statistically significant difference in the perception of gambling between preadolescent, who play games of chance, and the others. Similarly, the binomial logistic regression performed to ascertain the effects of seven key variables on the likelihood that participants gambled with money showed a statistically significant effect for six of them. The relevant findings of this first study address a literature gap and suggest the need to investigate the preadolescent as a cohort in which it identifies predictive factors of gambling behavior in order to design effective and structured preventive interventions.

Introduction

In recent years, addiction has undergone changes both in terms of choice of the so-called substance and for the age groups involved ( Echeburúa and de Corral Gargallo, 1999 ; Griffiths, 2000 ). Although addiction is a condition associated to substance abuse disorder, it also determines other conducts that can significantly affect the lifestyle of subjects ( Schulte and Hser, 2013 ).

In the last edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) ( American Psychiatric Association, 2013 ), the pathological gambling behavior has been conceptualized differently than in previous editions, as a result of a series of empirical evidence indicating the commonality of some clinical and neurobiological correlates between pathological gambling and substance use disorders ( Rash et al., 2016 ). The new classification into the “ Substance-Related and Addictive Disorders ” category supports the model of behavioral addictions in which people may be compulsively and dysfunctionally engaged in behaviors that do not involve exogenous drug administration, and these conducts can be conceptualized within an addiction framework as different expressions of the same underlying syndrome ( Shaffer et al., 2004 ).

Despite the fact that in many countries gambling is forbidden to minors, in recent years, there has been a marked increase in this behavior among younger people so that from surveys conducted in different cultural contexts it emerges that a percentage between 60 and 99% of boys and between 12 and 20 years have gambled at least once ( Splevins et al., 2010 ). The increasing number of children and underaged youth participating in games of chance for recreation and entertainment is attributable to the legalization, normalization, and proliferation of gambling opportunities/activities ( Hurt et al., 2008 ).

Several studies have shown that the percentage of young people who gamble in a pathological way is significant and even greater than the percentage of adult pathological gamblers ( Blinn-Pike et al., 2010 ). Using the definitions of at-risk and problem gambler that directly refer to the diagnostic criteria for pathological gambling, the review of Splevins et al. (2010) showed that a percentage of adolescents between 2 and 9% can be classified within the category of problem gamblers, while between 10 and 18% are adolescents who can be considered at-risk gamblers.

The first comprehensive review on problematic gambling in Italy noted a lack of large-scale epidemiological studies and of a national observatory regarding this issue ( Croce et al., 2009 ). More recent studies regarding the Italian national context are now available. A survey carried out with 2,853 students aged between 13 and 20 years showed that 7% of adolescents interviewed were classified as pathological gamblers ( Villella et al., 2011 ), while the study conducted by Donati et al. (2013) indicated that 17% of adolescents showed problematic gambling behaviors.

As far as ecological factors are concerned, the crucial role of family and play behavior of friends has been widely documented. In particular, a strong association between parents’ and children’s gambling behavior has emerged ( Hardoon et al., 2004 ), and it has been highlighted that the spread of gambling in the group of friends influences the practice of gambling among adolescents ( Gupta and Derevensky, 1998 ).

Traditionally, gambling in youth was considered as related to poor academic achievement, truancy, criminal involvement, and delinquency. More recently, investigators have examined the relationship between gambling and delinquent behaviors among adolescents in a systematic way, shifting the understanding beyond the explanation that delinquency associated with problem gambling is merely financially motivated by gambling losses ( Kryszajtys et al., 2018 ). This suggests that young players may have more general problems of conduct than specific criminal behavior.

Conversely, in relation to poor academic achievement, it has been highlighted that problem gambling in adolescence affect students’ performance mainly by reducing the time spent in studying ( Allami et al., 2018 ).

Although the phenomenon of gambling has been widely analyzed in the adult population and there are numerous studies on the adolescent population, the data in the literature suggest that gambling may be a phenomenon already present in preadolescence and needs to be analyzed. In fact, the lowering of the age of onset of problematic behaviors related to pathological gambling raises a question about the presence of gambling in preadolescents, as more exposed to the use of the Internet, smartphones, and tablets as tools that could encourage this type of conduct. A series of studies ( Shaffer and Hall, 2001 ; Vitaro et al., 2004 ; Winters et al., 2005 ; Kessler et al., 2008 ) have highlighted how adult pathological players started playing significantly earlier from a non-pathological player’s chronological point of view.

Nevertheless, it has been seen in the literature as, within the population of those who start playing before the age of 15, only 25% maintain the same frequency of play even in adulthood ( Vitaro et al., 2004 ; Delfabbro et al., 2009 , 2014 ).

In the review by Volberg and colleagues, it was shown how teenagers tend to prefer social and intimate games, such as card games and sports betting, while only a small percentage of teenagers are involved in illegal age gambling activities ( Volberg et al., 2010 ).

Pathological and problem players seem to be more involved in machine gambling (such as slot machines and poker machines), non-strategy games (such as bingo and lottery or super jackpot), and online games; they play in different contexts such as the Internet, school, and dedicated rooms ( Rahman et al., 2012 ; Yip et al., 2015 ).

It has been seen that online gambling is particularly attractive for young people due to its extreme accessibility, the large number of events dedicated to gambling, accessibility from the point of view of the economic share invested, and the multisensory experience and high level of involvement reported by young people ( Brezing et al., 2010 ; King et al., 2010 ).

Considering what is present in the literature, it is evident that the phenomenon of pathological gambling in adulthood is linked to a series of risk factors already present in adolescence. At the same time, the progressive lowering of the age at the beginning, which has been seen to be one of the main risk factors, makes it necessary to analyze the presence of the phenomenon of gambling in preadolescents, an analysis that at this time cannot count on the support of validated tools and questionnaires.

Considering that young people spend part of their time playing, it is necessary to distinguish between what is considered a game and what is considered gambling, even if not in a pathological way.

According to King et al., “gaming is principally defined by its interactivity, skill-based play, and contextual indicators of progression and success. In contrast, gambling is defined by betting and wagering mechanics, predominantly chance-determined outcomes, and monetization features that involve risk and payout to the player” ( King et al., 2015 ).

Primarily, the objective of this study is to verify the presence, the possible extent, and the characteristics of the phenomenon of gambling as defined before in a population of preadolescents (percentage, distribution by gender) to see if the population of preadolescent players shows the same characteristics as those found in larger populations at the age level (adolescents and adults). Secondly, the study aims to verify any differences in the perception of the game between those who play and those who do not, in order to identify additional specific characteristics.

In addition, on the basis of what is highlighted in the literature with respect to the risk factors detected in adults and adolescents, the study aims to assess whether and which of these factors can be predictive of the phenomenon of preadolescent gambling.

Finally, always in line with the identification of possible prodromal factors of gambling, the study wants to analyze the differences with respect to the types of games preferred by preadolescent players to assess any similarity with what emerged in the adolescent population.

In addition, the study aims to verify whether preadolescent players show the same game-level preferences highlighted in the literature as risk factors for the development of a real game disorder ( Rahman et al., 2012 ; Yip et al., 2015 ).

Materials and Methods

The investigation followed the Ethical Standards of the 1994 Declaration of Helsinki, and the study was approved by the Departmental Research Authorization Committee of Niccolò Cusano University and the Italian Ministry of Labour and Social Policy. In a prospective study of gambling perception, behavior, and risk factors, youth aged 11 to 14 years were recruited from 47 schools situated in 18 regions of Italy. The respondents’ survey was composed by 2,734 preadolescents (1,256 female and 1,452 male), enrolled in the 6, 7, and 8 grades across all national areas (18 provinces out of 20 Italian regions).

The administration of the survey was approved by the school boards of all the institutes involved, and all parents signed the informed consent and authorization to process personal data of their children. The self-report questionnaire was proposed and filled out in the classroom during school time.

The complete questionnaire developed ad hoc by the authors for the survey is composed of 19 items, 6 related to demographic characteristics of the sample and the remaining tighter focused on gambling behaviors and information related to the context of the subject. An excerpt of all the analyzed questionnaire items is provided in the appendix to facilitate the understanding of the Likert scale administered (see Supplementary Data Sheet 4 ).

After data screening, which excluded incomplete/invalid questionnaires, the sample presented the following characteristics: gender, 1,312 male (53%) and 1,163 female (47%); nationality, 93% Italian and 7% others; age: M = 12.36, SD = 0.95, distributed in 11 years old n = 541 (21.9%), 12 years old n = 803 (32.4%), 13 years old n = 841 (34.0%), and 14 years old n = 290 (11.7%).

Gamblers were defined as individuals who showed gambling behaviors in the previous year, classified as the ones who answered “yes” to the question “In the last twelve months did you game and gamble money playing any game?”

In the first sets of analysis, data were examined to determine whether there was an association between the gambling behavior and individual or ecological factors measured on nominal, continuous, or ordinal scales. Variable dependence was assessed as appropriate using chi-square for nominal variables, t -test for comparing groups on two continuous variables (e.g., age), or the sound nonparametric Mann-Whitney U test to confront two ordinal variables (e.g., Likert 5/4-point scale from fully agree to fully disagree). The decision to apply nonparametric tests was made considering the correlational research design of the survey and the non-previously validated questionnaire as the tool for collecting data. Moreover, the utilization of nonparametric analysis gives the most accurate estimates of significance in case of non-normal data distributions and variables of intrinsic ordinal nature as the ones obtained from Likert items in the questionnaire ( Laake et al., 2015 ).

For the same reason, a Friedman test was run to determine if there were differences in the playing rates of gamers concerning different games of chance, because this nonparametric test determines if there are differences between more than two variables measured on ordinal scales, e.g., when the answers to the questionnaire items are a rank ( Conover, 1999 ). The different categories of game taken into account were “videopoker, slot machine e video slot,” “lotto, lottery and superjackpot,” “Scratch card,” “Sport bets,” and “Daily fantasy sports.”

The second set of analyses examined the probability of being in the category “gamblers” of the dependent variable given the set of relevant independent variables already identified in base of preliminary analysis results and substantive literature support. More specifically, the following variables measured by the questionnaire were analyzed: gender, inappropriate school behavior, parent with gambling behavior, and troubles with parent – videogame-related and gambling-related. In this perspective, model selection in the multivariate logistic regression is aimed to the understanding of possible causes, knowing that certain variables did not explain much of the variation in gambling could suggest that they are probably not important causes of the variation in predicted variable. Moreover, introduction of too many variables could not only violate the parsimony principle but also produce numerically unstable estimates due to overfitting ( Rothman et al., 2008 ).

Individual characteristics of participants who gambled (gamblers) versus participants who did not gamble (nongamblers) are shown in Supplementary Table S1 .

Gamblers were more likely males, older, and showed a higher record of inappropriate behavior at school in the past. Moreover, the parents of these students presented a higher proportion of gambling behavior and family conflicts related to playing videogames or gambling. As shown in Supplementary Table S2 , the two groups also differed significantly on the variable “online gambling without money.”

Subsequently, several Mann-Whitney U tests were run to determine if there were differences in the perception of many gambling’s facets (measured through self-report scores) between gamblers and nongamblers. To analyze the perception of the game and any differences between players and nonplayers have been isolated four variables measured through the following items: “loosing money because of gambling,” “becoming rich through gambling,” “gambling is funny,” “gambling is an exciting activity.” The distributions of the perception scores for gamers and not gamers on these four items were similar, as assessed by visual inspection. Median perception of gambling as a risk was statistically significantly lower in gamblers (3) than in nongamblers (4), U = 344, z = −4.59, p < 0.001, as well as the difference between median perception scores of gambling as an habit was statistically significantly lower in gamblers (3) than in nongamblers (4); U = 357, z = −3.48, p < 0.001. Statistically significant differences were also found between the median perception scores of gamblers and nongamblers on the variable “ losing money because of gambling ” [lower in gamblers (3) than in nongamblers (4); U = 327, z = −6.27, p < 0.001] and “ becoming rich through gambling ” [higher in gamblers (2) than in nongamblers (1); U = 519, z = 9.879, p < 0.001].

Differently, on two similar items regarding the perception of gambling as an entertaining activity and as an exciting activity, the distributions for gamblers and nongamblers were not similar, as assessed by visual inspection. One of the two items concerned the perception of gambling as an entertaining activity; the Mann-Whitney U test revealed that scores for gamblers (mean rank = 1.8) were significantly higher than for nongamblers (mean rank = 1.14; U = 608, z = 17.52, p < 0.001). The last item concerned the perception of gambling as an exciting activity; the Mann-Whitney U test revealed that scores for gamblers (mean rank = 1.7) were significantly higher than for nongamblers (mean rank = 1.16; U = 569, z = 14.23, p < 0.001).

For this reason, a Friedman test was run to determine if there were differences in the playing rates of gamers concerning different games of chance, because this nonparametric test determine if there are differences between more than two variables measured on ordinal scale, i.e., when the answers to the questionnaire items are a rank ( Conover, 1999 ). The students who stated to have gambled money in the previous 12 months were asked in the following question about the frequency they played different group of games.

Pairwise comparisons were performed ( IBM Corporation Released, 2017 ) with a Bonferroni correction for multiple comparisons. Gambling/playing rate was statistically significantly different in the five groups of games, χ 2 (4) = 226.693, p < 0.0005. The values of post hoc analysis are presented in Supplementary Table S2 , and the Pairwise Friedman’s comparisons revealed relevant statistically significant differences in playing rates of gamers. In fact, the category of game of chance constituted by “videopoker, slot machine e video slot” (mean rank = 2.46) is preferred to all other kinds of game of chance, except “lotto, lottery and superjackpot” (mean rank = 2.50). In the case of “Lotto, lottery, SuperJackpot,” this category of game of chance is preferred to “Scratch card” (mean rank = 3.30) in a statistically significant way, but it is also statistically less played in comparison to “Sport bets” (mean rank = 3.35) and “Daily fantasy sports” (mean rank = 3.40). None of the remaining differences were statistically significant.

Regarding the second set of analyses, Supplementary Table S3 provides the model used in the binomial logistic regression performed to ascertain the effects of key variables on the likelihood that participants played game of chance with money. The logistic regression model was statistically significant, χ 2 (7) = 326, p < 0.001. The model explained 23.0% (Nagelkerke R 2 ) of the variance in the predicted variable (gambling behavior) and demonstrated a percentage accuracy in classification (PAC) equal to 86.6%. Sensitivity was 22.5%, specificity was 97.6%, positive predictive value was 62.2%, and negative predictive value was 87.9%. Of the seven predictor variables only six were statistically significant: gender, inappropriate school behavior, parents with gambling behavior, troubles with parents – videogames related, online gambling without money, and age (as shown in Supplementary Table S3 ). Analysis showed that male had 2.96 times higher odds to be gamers than females (OR = 0.337; 95% CI 0.248–0.458), and increasing age was associated with an increased likelihood of gambling behavior. Also, inappropriate school behavior (OR = 1.859; 95% CI 1.395–2.477), parents with gambling behavior (OR = 3.836; 95% CI 2.871–5.125), troubles with parents – videogames related (OR = 1.285; 95% CI.510–3.236), and online gambling without money (OR = 2.297; 95% CI 1.681–3.139) increased the likelihood of gambling. By contrast, the “Troubles with parents – gambling related” variable was not statistically significant, probably because of the extremely unbalanced case ratio between the two modalities.

The first objective of this study was to evaluate the presence or absence and the consequent extent of the phenomenon of gambling in a population of preadolescents and to understand which factors are associated to the progressive lowering of the age of onset.

Consistently with the literature on the adult and adolescent population, the evidence presented thus far supports the idea that even in the preadolescent population players tend to be predominantly males ( Hurt et al., 2008 ; Splevins et al., 2010 ; Villella et al., 2011 ; Dowling et al., 2017 ).

One of the more significant findings to emerge from this study is that players of game of chance have a significantly different perception of the game than nonplayers, i.e., they see the game as “less risky” and perceive less risk of losing money through the game. In addition, confirming this “altered” perception, they show higher values than nonplayers in the perception of being able to become rich through the game ( Hurt et al., 2008 ; Dowling et al., 2017 ). Gamblers have a perception of the game as exciting and fun, a tendency which increases with age. This pattern seems to confirm what is expressed in the literature regarding the theme of sensation seeking and its connection with the development of gambling disease ( Dickson et al., 2002 , 2008 ; Hardoon and Derevensky, 2002 ; Messerlian et al., 2007 ; Blinn-Pike et al., 2010 ; Shead et al., 2010 ; Ariyabuddhiphongs, 2011 ; Lussier et al., 2014 ).

Even more importantly, some possible predictive factors of gambling emerged among the variables analyzed: thus, the phenomenon of gambling was associated with problems of school conduct, problems with parents related to the use of video games and, interestingly, also to the presence of parents who are gamers.

Since there are no validated tools in the literature for the diagnosis of preadolescent gambling, the analyses were conducted on those who were “gamblers” according to what was previously stated. It is therefore of particular relevance that the sample of preadolescent gamblers shows descriptive characteristics and predictive factors similar to those highlighted by the literature on adolescent gamblers with a diagnosis of gambling.

In this sense, the analysis of the most frequently used game types is particularly important.

With respect to the game categories analyzed, with the exception of “Lotto, lottery, SuperJackpot,” the category that is most frequently chosen by the sample of gamblers is that of “videopoker, slot machine e video slot.”

These data are of particular relevance considering that some studies in the literature have shown that adult pathological players have shown in previous ages a strong preference for these types of games. Although it is necessary to investigate with further studies the reasons underlying the choice of this type of game by preadolescents, this fact suggests that the phenomenon of preadolescent gambling has a number of aspects and characteristics common to those identified by the literature in the analysis of the precursors of pathological gambling.

There are some issues to take under consideration in framing the present results. Regarding the sample, although the numerous participants and the geographical representativeness of the population, the sample was not randomly selected. Therefore, we cannot exclude that subjects were unbalanced on unobserved, causally relevant concomitants. Although the methodology allows prediction, it should be noted that causality cannot be established from this survey, because the research design does not properly establish temporal sequence. In addition, only self-report measures and not thoroughly validated scales were used, as the objective of this study was to conduct an exploratory survey on the characteristics of the phenomenon, and there were some dichotomous variable with uneven case ratios. Furthermore, some constructs related to gambling behavior (e.g., impulsivity) and neurocognitive functioning were not analyzed in designing this first study; although in the wider research program, it is intended to explore also these factors.

Notwithstanding these limitations, the present study makes some noteworthy contributions to the understanding of the phenomenon of gambling and its characteristics in a population (preadolescents) which is still not very explored in the literature.

In particular, one significant finding is that the lowering of the age has not substantially changed what has been established in the literature with respect to the phenomenon in adolescents: the characteristics of players in terms of gender are substantially unchanged in the comparison between adolescents and preadolescents.

Moreover, from the analyses carried out, it appears that those that the literature has highlighted as risk factors of gambling in adolescence and adulthood are already present in younger players and may be predictive factors of gambling conduct already in preadolescence.

The data show, moreover, that the perception of gambling for those who play is significantly different from those who do not play, and specifically on aspects related to attractiveness, the low perception of risk and the possibility of getting rich easily. Finally, even with respect to an analysis carried out on different types of games, what emerged from the literature as additional risk factors for adolescents and adults is already present in preadolescence.

The findings of this study focus on the need to investigate the preadolescent age group in order to identify specific predictive factors of gambling in order to structure effective and structured preventive interventions and the parallel need to structure a standardized tool for the diagnosis of gambling in this specific population.

Data Availability

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The study was carried out according to the principles of the 2012–2013 Helsinki Declaration. Written informed consent to participate in the study was obtained from the parents of all children. The study was approved by the IRB of the Department of Psychology of Niccolò Cusano University of Rome.

Author Contributions

NV and GF designed and performed the design of the study and conducted the literature searches. CD, MC, and GP provided the acquisition of the data, while FM undertook the statistical analyses. NV, CP, and FM wrote the first draft of the manuscript. All authors significantly participated in interpreting the results, revising the manuscript, and approved its final version.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/article/10.3389/fpsyg.2019.01287/full#supplementary-material

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Keywords: gambling, risk factors, preadolescence, addiction, prevention

Citation: Vegni N, Melchiori FM, D’Ardia C, Prestano C, Canu M, Piergiovanni G and Di Filippo G (2019) Gambling Behavior and Risk Factors in Preadolescent Students: A Cross Sectional Study. Front. Psychol . 10:1287. doi: 10.3389/fpsyg.2019.01287

Received: 15 February 2019; Accepted: 16 May 2019; Published: 12 June 2019.

Reviewed by:

Copyright © 2019 Vegni, Melchiori, D’Ardia, Prestano, Canu, Piergiovanni and Di Filippo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nicoletta Vegni, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Researchers pinpoint behaviors underlying gambling addiction

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Before putting $20 down on the table, audit your mental health, researchers from the Institute of Behavioral Science suggest.

Gambling activities are more readily available than ever, but the availability could play into potential problem gambling and addiction based off one’s genetics, according to new research from the University of Colorado Boulder. 

In a study published in Addictive Behaviors , the researchers found that individual’s genetics, psychiatric diagnoses and behaviors influence the frequency in which a they gamble, the specific activities they participate in, and the probability that they will develop problems with gambling.

Gambling addiction affects roughly two million people per year and yet much about what causes the addiction to arise is relatively unknown given the complexity of the data. This new research, though, provides some insight on the relationship of genetics and addiction.

"The types of gambling that you do and your current mental health matters, and how much you gamble all depends on whether you develop problematic outcomes from it," Spencer Huggett, (PhDPsych’19), a postdoctoral fellow at Emory University and an author on the paper, said.

“Certain people are more prone to develop problems gambling and/or to engage in certain types of gambling than others,” he said.

Huggett and Evan Winiger (PhDPsych’21), the study’s co-author and a postdoctoral fellow at Anschutz Medical Campus, were roommates as they both pursued their doctorates in behavioral, psychiatric and statistical genetics. Winiger studied cannabis and Huggett, studied cocaine. Through living under the same roof, scientific, technical and philosophical conversations on addiction and genetics ensued. One of these conversations led them to asking questions about gambling and its addictive properties. 

“We hypothesized that there’s going to be some common feature to all types of gambling from playing poker and betting on slot machines to buying lottery tickets and day trading in the stock market. Although we did not think this would fully recapitulate the complexities and nuances across all forms of gambling,”. Huggett said. “We thus set out to study clusters of gambling behavior — particularly those involving an element of ‘skill’ — to investigate and characterize the developmental pathways of gambling behavior.” 

Evan Winiger is the study’s co-author and a postdoctoral fellow at Anschutz Medical Campus researching cannabis and sleep.

To assess these potential phenomena, they utilized the Institute of Behavioral Genetics’ library of complex datasets and pulled the large twin and sibling sets. The sibling sample was selected based on externalizing behaviors, and the twin sample provided a general population overview. They used multi-dimensional statistical techniques on a sample of 2,116 twins and 619 siblings to understand the structure, typology and etiology of gambling frequency.

“This study is a genetically informed evaluation of different gambling profiles,” Winiger said. “There’s some research out there trying to categorize different kinds of gamblers, and our study is kind of another approach showing this might be a different way to look at these different subgroups as well as how certain classes or subgroups might correlate with various mental health or substance use.”

Their study identifies four gambling subtypes distinguished by their gambling behavioral profiles (or how often they gambled). According to the study, the gambling subtypes with the highest rates of psychiatric disorders had approximately two to six times higher rates of problem gambling than those with lower rates of mental illness. Genetics play an important role in the development of gambling behavior, the researchers said, noting that the gambling subtypes with highest rates of problem gambling were strongly predicted by genetic factors. The individual’s mental health, genetic risk plus their gambling behavioral profiles determined whether or not problematic gambling behaviors would arise, the researchers found. 

The study also found that individuals participating in common gambling activities such as betting on slots, playing dice and buying lottery tickets were more likely to lead to problem gambling than gambling with a perceived element of skill gambling such as day trading and playing pool for money.

Huggett and Winiger applied the Pathways Model, an established model within gambling research that determines problem and pathological gamblers, which defines three possible pathways that individuals begin to experience problems with gambling. The three pathways are behaviorally conditioned problem gamblers, emotionally vulnerable problem gamblers, and antisocial impulsivity problem gamblers. 

“What we really wanted to understand was, ‘is there a profile of certain gambling activities that clusters into broader mental health subtypes?’” Huggett said “We did find evidence that this was the case. Certain types of gamblers based off of the activities that they prefer tended to mimic some of these more popular pathways to gambling addiction.” 

In the discussion of the study, the researchers mention that their examination of personality disorders and gambling should be approached with caution due to the wide spectrum of gambling activities and behaviors. This study does, though, supports the connection between genetics to personality disorders and gambling addiction.

“This is an extremely big pie of mental illness and gambling and the thing that we did was the smallest little sliver,” Huggett said. “We wanted to shed light in that pie so we can have a better understanding and hopefully use this information to tailor more proactive approaches and potentially tailored treatment profiles to the individual.”

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Gambling Disorder and Other Behavioral Addictions: Recognition and Treatment

Addiction professionals and the public are recognizing that certain nonsubstance behaviors—such as gambling, Internet use, video-game playing, sex, eating, and shopping—bear resemblance to alcohol and drug dependence. Growing evidence suggests that these behaviors warrant consideration as nonsubstance or “behavioral” addictions and has led to the newly introduced diagnostic category “Substance-Related and Addictive Disorders” in DSM-5. At present, only gambling disorder has been placed in this category, with insufficient data for other proposed behavioral addictions to justify their inclusion. This review summarizes recent advances in our understanding of behavioral addictions, describes treatment considerations, and addresses future directions. Current evidence points to overlaps between behavioral and substance-related addictions in phenomenology, epidemiology, comorbidity, neurobiological mechanisms, genetic contributions, responses to treatments, and prevention efforts. Differences also exist. Recognizing behavioral addictions and developing appropriate diagnostic criteria are important in order to increase awareness of these disorders and to further prevention and treatment strategies.

Addiction has been proposed to have several defining components: (1) continued engagement in a behavior despite adverse consequences, (2) diminished self-control over engagement in the behavior, (3) compulsive engagement in the behavior, and (4) an appetitive urge or craving state prior to engaging in the behavior. 1 – 3 Although, for a period of time, the term addiction was almost exclusively used to refer to excessive and interfering patterns of alcohol and drug use, the Latin word ( addicere ) from which it derived did not originally have this import. 4 Researchers and others have recently recognized that certain behaviors resemble alcohol and drug dependence, and they have developed data indicating that these behaviors warrant consideration as nonsubstance or “behavioral” addictions. 1 , 5 , 6 The concept remains controversial. Excessive engagement in behaviors such as gambling, Internet use, video-game playing, sex, eating, and shopping may represent addictions. 7 A significant minority of individuals who show such excessive behavior display habitual or compulsive engagement. 8 , 9

Several converging lines of evidence show an overlap between these conditions and substance dependence in terms of clinical expression (e.g., craving, tolerance, withdrawal symptoms), comorbidity, neurobiological profile, heritability, and treatment. 9 , 10 Moreover, behavioral and substance addictions share many features in natural history, phenomenology, and adverse consequences. Both forms of addiction typically have onsets in adolescence or young adulthood, with higher rates observed in these age groups than among older adults. 11 Both forms of addiction have natural histories that may exhibit chronic and relapsing patterns, and in both forms, many people recover on their own without formal treatment. 12

Much remains to be understood, however, in the relatively novel field of behavioral addictions. Additionally, wide gaps exist between research advances and their application in practice or public policy settings. This lag is due, in part, to the public perception of behavioral addictions. Whereas drug abuse has well-known and severe negative consequences, those associated with behavioral addictions (e.g., dysfunction within the family unit, 13 , 14 incarceration, 15 early school dropouts, 16 financial troubles 17 , 18 ) are often overlooked despite tremendous implications for public health. Moreover, because engagement in some behaviors with addictive potential is normative and adaptive, individuals who transition to maladaptive patterns of engagement may be considered weak willed and be stigmatized. Thus, research, prevention, and treatment efforts must be furthered, and educational efforts enhanced.

DSM-5 CONSIDERATIONS

Establishing nomenclature and criteria for behavioral addictions will enhance our capacity to recognize and define their presence. In the recently released fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), 19 a major modification is the reclassification of pathological gambling (renamed “disordered gambling”) from the “Impulse Control Disorders Not Elsewhere Classified” category into the new “Substance-Related and Addictive Disorders” category. The new term and category, and their location in the new manual, lend additional credence to the concept of behavioral addictions; people may be compulsively and dysfunctionally engaged in behaviors that do not involve exogenous drug administration, and these behaviors can be conceptualized within an addiction framework as different expressions of the same underlying syndrome. 2 Although disordered gambling is the only addictive disorder that is included in the main section of DSM-5, several other conditions have been included in Section III—the part of DSM-5 in which conditions that require further study are located. In particular, the DSM-5 work group has flagged “Internet gaming disorder” as a possible candidate for future inclusion in the addictions category. Although the inclusion of this disorder in the provisional diagnosis section of DSM-5 represents an important advance, the conflation of problematic Internet use and problematic gaming may prove unhelpful; the result may be gaps in research on problematic Internet use that is unrelated to gaming (e.g., social networking) or on problematic gaming that is unrelated to Internet use. 20

This review will highlight the recent neurobiological, genetic, and treatment findings on behavioral addictions. An emphasis will be placed on disordered gambling since it is arguably the best-studied behavioral addiction to date. Other behavioral addictions, despite being less well studied, have been receiving considerable attention from researchers and clinicians and will also be discussed in this review. We will then discuss the similarities and differences between behavioral and substance-related addictions.

A literature search was conducted using the PubMed database for articles in English pertaining to behavioral addictions. Case reports and studies with insufficient statistical information were excluded from this review. Because of the overlapping terms used to describe each condition, search items included the many different names found in the literature. For example, searches were made for “Internet addiction,” “compulsive Internet use,” and “problematic Internet use.” It is noteworthy that the sample sizes in most of the studies cited in this review are small and that the criteria used to define diagnoses vary between studies. These methodological differences should be considered when interpreting the findings.

PHENOMENOLOGY AND EPIDEMIOLOGY

Disordered gambling can include frequent preoccupations with gambling, gambling with greater amounts of money to receive the same level of desired experience (tolerance), repeated unsuccessful efforts to control or stop gambling, restlessness or irritability when trying to stop gambling (withdrawal), and the interference of gambling in major areas of life functioning. Criteria also include gambling to escape from a dysphoric state, gambling to regain recent gambling-related losses (“chasing” losses), lying in significant relationships about gambling, and relying on others to fund gambling. One major change in the DSM-5’s clinical description of gambling disorders is that it eliminated the requirement that a person engage in illegal activities to finance gambling. 19 Additionally, the threshold of inclusionary criteria was reduced from 5 of 10 to 4 of 9; this new threshold is thought to improve the classification accuracy and reduce the rate of false negatives. However, the contrast in the thresholds for gambling disorder (4 of 9 criteria) and substance use disorders (SUDs; 2 of 11 criteria) will likely underestimate the relative prevalence and impact of gambling disorder. Epidemiological studies that have employed screening instruments like the South Oaks Gambling Screen 21 have frequently generated higher prevalence estimates than have those employing DSM criteria. 20 , 22 , 23 Meta-analytic data suggest that prevalence of past-year adult disordered gambling is between 0.1% to 2.7%. 24 The estimated proportion of disordered gamblers among college students appears higher, estimated in one study at 7.89%. 25

Definitions of other behavioral addictions have often used DSM criteria for disordered gambling as a blueprint. 26 , 27 For example, Young’s Diagnostic Questionnaire 28 proposes the following criteria for Internet addiction: withdrawal, tolerance, preoccupation with the Internet, longer than intended time spent on the Internet, risk to significant relationships or employment relating to Internet use, lying about Internet use, and repeated, unsuccessful attempts to stop Internet use. However, sample and measurement differences, coupled with the lack of universally agreed-upon diagnostic criteria, may contribute to variable prevalence estimates for Internet addiction. Estimates for adolescents have ranged from 4.0% to 19.1%, and for adults, from 0.7% to 18.3%. 29 Similarly, a range of prevalence estimates (with criteria mostly based on those for disordered gambling) have been reported for problematic video-game playing among adolescent populations (4.2%–20.0%), with adult estimates (11.9%) also falling in that range. 29

CO-OCCURRING DISORDERS

Data from the U.S. National Comorbidity Survey Replication—a U.S.-based community survey with 9282 respondents—reported that 0.6% of respondents met criteria for lifetime disordered gambling (2.3% reported at least one inclusionary criterion); of those, 96% met critieria for at least one other lifetime psychiatric diagnosis, and 49% had been treated for another mental illness. 30 High co-occurrence rates between behavioral and substance addictions have been observed; a recent meta-analysis suggest a mean co-occurrence of 57.5% between disordered gambling and substance addiction. 24 Among individuals with SUDs, the odds of disordered gambling were elevated almost threefold. 31 Conversely, the odds for an alcohol use disorder increased roughly fourfold when disordered gambling was present. 32 Clinical samples of other behavioral addictions suggest that co-occurrence with SUDs is common. 33 In a study of 2453 college students, individuals meeting the criteria for Internet addiction were roughly twice as likely to report harmful alcohol use, after controlling for gender, age, and depression. 34 Taken together, these findings suggest that behavioral addictions may share a common pathophysiology with SUDs. 10

Disordered gambling also frequently co-occurs with various psychiatric conditions, including impulse-control, mood, anxiety, and personality disorders. 8 , 23 , 35 , 36 It has been suggested that mood and anxiety disorders precede gambling problems, 30 which may manifest as a maladaptive coping mechanism. 37 Longitudinal studies suggest, however, that disordered gambling is associated with incident (new onset) mood disorders, anxiety disorders, and SUDs, 38 with incident SUDs being moderated by gender. 39 Additionally, both incident medical disorders and incident mental health disorders are related to disordered gambling, particularly among older adults. 39 , 40 The presence or absence of specific co-occurring conditions is important to consider when selecting treatment strategies. 41

DATA LINKING BEHAVIORAL AND SUBSTANCE ADDICTIONS

Especially relevant to addictions are aspects of motivation, reward processing, and decision making. 42 – 44 These features represent potential endophenotypes, or intermediate phenotypes, that could be pursued in biological investigations across a spectrum of substance- and non-substance-related addictive disorders and may serve as possible markers for prevention and treatment efforts. 45

Personality

Individuals with behavioral and substance addictions score high on self-report measures of impulsivity and sensation seeking, and generally low on measures of harm avoidance. 46 , 47 Some data indicate, however, that individuals with Internet addiction, problematic video-game playing, or disordered gambling may exhibit high levels of harm avoidance, 29 , 48 suggesting important individual differences among people with addictions. The extent to which behavioral tendencies like harm avoidance may shift (e.g., over time) or differ (e.g., according to geographic region or other factors) warrants additional research.

Other research suggests that aspects of compulsivity are typically higher among individuals with behavioral addictions. 31 , 49 Consequently, some conceptualize behavioral addictions along an impulsive-compulsive spectrum. 50 Compulsivity represents a tendency to repeatedly perform acts in a habitual manner to prevent perceived negative consequences, though the act itself can lead to negative consequences. 51 While both impulsivity and compulsivity imply impaired impulse control, recent data suggest a more complex relationship between these two constructs as they relate to obsessive-compulsive disorders (OCDs) and behavioral addictions. For example, although groups with disordered gambling or with OCD both score highly on measures of compulsivity, among disordered gamblers these impairments appear limited to poor control over mental activities and to urges and worries about losing control over motor behaviors. 52 By contrast, OCD subjects tend to score poorly across most domains. 53

Neurocognition

Neurocognitive measures of disinhibition and decision making have been positively associated with the severity of problem gambling 54 and may predict relapse of disordered gambling. 55 Similar to individuals with SUDs, individuals with disordered gambling have displayed impairments in risky decision making and in reflection impulsivity in comparison to matched control subjects. 56 Disadvantageous performance on the Iowa Gambling Task, which assesses risk/reward decision making, has been observed among individuals with disordered gambling and alcohol dependence. 57 In contrast, a study of individuals with Internet addiction did not demonstrate such deficits in decision making on the Iowa Gambling Task. 58

Attempts to control or eliminate addictive behaviors may be motivated by immediate reward or the delayed negative consequences of use—that is, temporal or delay discounting. This process may be mediated via diminished top-down control of the prefrontal cortex over subcortical processes promoting motivations to engage in addictive behavior. 59 Individuals with disordered gambling and SUDs display rapid temporal discounting of rewards; in other words, they are more prone to select smaller, earlier rewards than larger ones that come later. 60 , 61 Although some data suggest that abstinent individuals with SUDs perform better (display less delay discounting) than do individuals with current SUDs, other data suggest no significant differences. 60 A recent study suggests that delay discounting did not differ in individuals with disordered gambling pretreatment and one-year posttreatment. 62

Neurochemistry

Dopamine has been implicated in learning, motivation, salience attribution, and the processing of rewards and losses (including their anticipation [reward prediction] and the representation of their values). 63 Given the importance of dopaminergic projections in reward circuits—including projections from the ventral tegmental area to ventral striatum in SUDs 63 —studies on behavioral addictions and related behaviors have focused on investigating dopamine transmission. A recent single-photon emission computed tomography study suggests that dopamine release in the ventral striatum during a motorbike-riding computer game 64 is comparable to that induced by psychostimulant drugs such as amphetamine 65 and methylphenidate. 66 In one small study using positron emission tomography with the tracer [ 11 C]raclopride, dopamine release in the ventral striatum was associated positively with Iowa Gambling Task performance in healthy control subjects but negatively in individuals with disordered gambling, 67 suggesting that dopamine release may be involved in both adaptive and maladaptive decision making. Although a gambling task induced no differences in the magnitude (i.e., [ 11 C]raclopride displacement) between disordered gamblers and controls, among disordered gamblers dopamine release correlated positively with problem-gambling severity 68 and with subjective excitement. 69

Similar to individuals with SUDs, 70 reduced D2/D3 receptor availability in the striatum has been observed in individuals with Internet addiction 71 and in humans 72 and mice 73 , 74 with obesity. For example, obese rats (but not lean rats) had downregulated D2 receptors, and their consumption of palatable food was resistant to disruption by an aversive or punishing condition stimulus. 75 The same study also found that lentivirus-mediated knockdown of striatal D2 receptors accelerated the development of addiction-like reward deficits and the onset of compulsive-like food seeking in rats with access to palatable food, 76 which is suggestive of reward hyposensitivity. Several recent studies have examined this marker among disordered gamblers. 69 , 77 , 78 While no significant between-group differences in D2/D3 receptor availability at resting state was observed, among disordered gamblers dopamine receptor availability was negatively correlated with mood-related impulsivity (“urgency”) within the striatum 77 and positively correlated with problem-gambling severity within the dorsal striatum. 78 The precise role for dopamine in gambling disorder continues to be debated, 79 but a model based on studies in rats and humans suggests different roles for D2, D3, and D4 dopamine receptors, with D3 receptors in the substantia nigra correlating with problem-gambling severity and impulsivity, and linked to greater dopamine release in the dorsal striatum. 78 , 80 – 82

Dopamine receptor agonist medications have been associated with disordered gambling and other behavioral addictions in patients with Parkinson’s disease. 83 – 85 However, other factors (including age at Parkinson’s onset, marital status, and geographic location) independently contribute to the associations between behavioral addictions and Parkinson’s disease, suggesting multiple etiologically contributing domains. 83 Furthermore, drugs with dopamine antagonist properties have not demonstrated efficacy in the treatment of disordered gambling. 86 , 87 These findings, in conjunction with those showing the induction of gambling urges by drugs promoting and blocking D2-like dopamine receptor activity, 88 , 89 have raised questions regarding the centrality of dopamine to disordered gambling. 79 Nonetheless, recent data suggest that dissecting the inputs from D2, D3, and D4 receptors might elucidate dopamine’s role in the pathophysiology of disordered gambling. 80 , 82

Evidence exists for serotonergic involvement in behavioral addictions. Serotonin is implicated in emotions, motivation, decision making, behavioral control, and inhibition of behavior. Dysregulated serotonin functioning may mediate behavioral inhibition and impulsivity in disordered gambling. 8 , 67 , 69 Disordered gambling has been associated with reduced levels of the serotonin metabolite 5-hydroxyindoleacetic acid (5-HIAA) in cerebrospinal fluid. 90 Low levels of platelet monoamine oxidase (MAO) activity (considered a peripheral marker of serotonin activity) among males with disordered gambling 91 , 92 has provided additional support for serotonergic dysfunction. Striatal binding of a ligand with high affinity for the serotonin 1B receptor correlated with problem-gambling severity among individuals with disordered gambling. 93 These findings are consistent with those from challenge studies using meta-chlorophenylpiperazine (m-CPP), a partial agonist with high affinity for the serotonin 1B receptor. These studies observe different biological and behavioral responses in individuals with behavioral or substance addictions (compared to those without) in response to m-CPP. 47

Less is known about the integrity of other neurotransmitter systems in behavioral addictions. A dysregulated hypothalamic-pituitary-adrenal axis and increased levels of noradrenergic moieties have been observed in disordered gambling. 94 Noradrenaline may be involved in the peripheral arousal associated with gambling. 95 , 96 Opioid antagonists (e.g., naltrexone, nalmefene) have demonstrated superiority over placebo in multiple randomized clinical trials. 41 , 97 , 98

Neural systems

Neuroimaging studies suggest shared neurocircuitry (particularly involving frontal and striatal regions) between behavioral and substance addictions. Studies using reward-processing and decision-making tasks have identified important contributions from subcortical (e.g., striatum) and frontal cortical areas, particularly the ventromedial prefrontal cortex (vmPFC). Among disordered gamblers, versus healthy controls, both decreased 99 – 102 and increased vmPFC activity 103 has been reported during simulated gambling and decision-making tasks. Similarly, gambling stimuli has been reported to be associated with both decreased 104 and increased 105 , 106 vmPFC activity in disordered gamblers. The findings from these studies may have been influenced by the specific tasks used, the populations studied, or other factors. 99 , 107 , 108 Relatively greater activation of other frontal and basal ganglia areas, including the amygdala, during high-risk gambling decision making in the Iowa Gambling Task has been observed among disordered gamblers. 103 While data are relatively limited for other behavior addictions, several recent cue-induction studies have demonstrated activation of brain regions associated with drug-cue exposure. Individuals playing World of Warcraft (a massive, multiplayer, online role-playing game) more than 30 hours per week, compared to nonheavy players (playing less than 2 hours per day) displayed significantly greater orbitofrontal, dorsolateral prefrontal, anterior cingulate, and nucleus accumbens activation when exposed to game cues. 109 In a separate study, activation in the medial orbitofrontal cortex, anterior cingulate, and amygdala in response to anticipated receipt of food was positively correlated with food addiction scores. 110

As mentioned previously, the mesolimbic pathway (frequently referred to as the “reward pathway”) from the ventral tegmental area to the nucleus accumbens has been implicated in both substance and behavioral addictions. 111 , 112 Relatively decreased ventral striatal activation has been reported in disordered gamblers during monetary reward anticipation 99 , 100 and simulated gambling. 101 In gambling cue-exposure tasks, disordered gamblers exhibited decreased activation in the ventral 113 and dorsal 114 striatum compared to healthy controls. Moreover, both ventral striatal and vmPFC activity was inversely correlated with problem-gambling severity in problem-gambling subjects during simulated gambling. 101 In seeming contrast to these findings in disordered gambling, a recent functional magnetic resonance imaging study found stronger nucleus accumbens activity among compulsive shoppers (versus controls) during the initial product presentation phase of a multiphase purchasing task. 115

Unlike findings from patients with SUDs, 116 studies involving small samples of disordered gamblers did not display significant volumetric differences in white or gray matter from controls, 117 , 118 suggesting that volumetric differences observed in SUDs may represent possible neurotoxic sequelae of chronic drug use. More recent data using larger samples, however, show smaller amygdalar and hippocampal volumes in individuals with disordered gambling, similar to findings in SUDs. 119 Diffusion tensor imaging findings suggest reduced fractional anisotropy values—indicating reduced white matter integrity—in regions including the corpus callosum in disordered gamblers versus controls. 118 , 120 Research has demonstrated both widespread reduction of fractional anisotropy in major white-matter pathways and abnormal white-matter structure in Internet addiction. 121 However, negative results have also been observed for Internet addiction 122 and hypersexual disorder. 123

Genetics and Family History

Twin studies suggest that genetic factors may contribute more than environmental factors to the overall variance of risk for developing disordered gambling. 124 , 125 Data from the all-male Vietnam Era Twin Registry estimate the heritability of disordered gambling to be 50%–60%, 126 , 127 a statistic comparable to the percentages for substance addictions. 128 A follow-up study of female twins estimated that the proportion of variability in liability for disordered gambling was similar in women and men. 124 , 129 Small family studies of probands with disordered gambling, 130 hypersexual disorder, 131 and compulsive shopping behavior 132 have found that first-degree relatives of the probands had significantly higher lifetime rates of SUDs, depression, and other psychiatric disorders, suggesting genetic relationships among these conditions.

Few molecular genetic studies of behavioral addictions have been conducted. Genetic polymorphisms putatively related to dopamine transmission (e.g., DRD2 Taq1A1, which is in linkage disequilibrium with Ankk1 ) have been associated with disordered gambling 133 , 134 and problematic video-game playing. 135 Other research implicates allelic variant in serotonin transmission genes (e.g., 5HTTLPR and MAO-A ) in disordered gambling 92 , 136 and Internet addiction disorder. 137 These studies, however, typically involved relatively small samples and did not account for potential confounds (e.g., those relating to racial and ethnic differences between groups). A recent genome-wide association study reported that no single nucleotide polymorphism reached genome-wide significance for disordered gambling. 138 Further research is needed to investigate genes and gene-environment interactions that relate to behavioral addictions, with intermediate phenotypes like impulsivity perhaps representing important targets. 45 , 128

Addiction Versus Addictions

The current literature indicates many overlaps between behavioral and substance-related addictions in the domains mentioned above, suggesting that the two sets of disorders may represent different expressions of one “addiction” entity. Nonetheless, differences are also apparent. Although the concept of behavioral addiction appears to be increasingly prominent in the literature, the scientific and empirical evidence remains insufficient for these disorders to be treated as part of one comprehensive, homogenous group. The gaps in our knowledge need to be addressed in order to determine whether behavioral and substance-related addictions represent two different addictions or whether they are different expressions of a core addiction syndrome. Furthermore, separate diagnoses can be clinically useful since individuals may present to practitioners with concerns in specific addiction domains. Nonetheless, the overlaps between the disorders suggest that specific treatments for SUDs may also be beneficial for behavioral addictions.

Treatments for addiction may be divided into three phases. First, a detoxification phase aims to achieve sustained abstinence in a safe manner that reduces immediate withdrawal symptoms (e.g., anxiety, irritability, and emotional instability, which may be present in both behavioral and substance addictions). This first phase may involve medications to assist the transition. The second phase is one of recovery, with emphasis on developing sustained motivation to avoid relapse, learning strategies to cope with cravings, and developing new, healthy patterns of behavior to replace addictive behavior. This phase may involve medications and behavioral treatments. Third, relapse prevention aims to sustain abstinence in the long term. This last phase is perhaps the most difficult to achieve, with waning motivation, the revival of associated learning cues linking hedonic experience to addictive behavior, and temptations that may threaten the recovery process, originating from external (e.g., people, places) and internal (e.g., resumed engagement, stress, interpersonal conflict, symptoms of comorbid mental conditions) cues. Most clinical trials for behavioral addictions have focused on short-term outcomes.

Psychopharmacological Interventions

No medication has received regulatory approval in the United States as a treatment for disordered gambling. However, multiple double-blind, placebo-controlled trials of various pharmacological agents have demonstrated the superiority of active drugs to placebo. 41 , 139

At present, the medications with the strongest empirical support are the opioid receptor antagonists (e.g., naltrexone, nalmefene). These medications have been used in the clinical management of drug- (particularly opiate-) and alcohol-dependent patients for several decades 140 , 141 and have more recently been evaluated for treating disordered gambling and other behavioral addictions. One double-blind study suggested the efficacy of naltrexone in reducing the intensity of urges to gamble, gambling thoughts, and gambling behavior; in particular, individuals reporting higher intensity of gambling urges responded preferentially to treatment. 97 These findings have been replicated in larger, longer studies, 142 and maintenance of positive effects may persist after naltrexone discontinuation. 143 Medication dosage may be an important consideration in achieving improvement. High doses (100–200 mg/day) of naltrexone successfully reduced symtpoms of hypersexual disorder and compulsive shopping disorder; 144 – 146 they recurred, however, following discontinuation. 144 In two large, multicenter trials using double-blind, placebo-controlled designs, only the higher doses of nalmefene (40 mg/day) showing statistically significant differences from placebo in treatment outcome for disordered gambling. 98 , 147 Other data suggest, however, that lower doses (e.g., 50 mg of naltrexone) are sufficient and associated with fewer adverse effects. 142 , 147 Importantly, intensity of pretreatment gambling urges and a familial history of alcoholism have been linked to opioid antagonist treatment outcomes in disordered gambling (with stronger urges at treatment onset and a positive family history of alcoholism each associated with better treatment outcome to naltrexone or nalmefene), suggesting important individual differences with respect to treatment response. 148 The extent to which treatment response might link to specific genetic factors—as has been suggested for alcohol treatment response to naltrexone 149 —warrants additional study.

With respect to food, preclinical research suggested that high doses of the opiate antagonist naloxone increased sugar consumption and opiate-like withdrawal symptoms—including elevated plus maze anxiety, teeth chattering, and head shakes—in sugar-binging rats following a period of abstinence. 150 – 152 These results were not replicated among rats on high-fat diets. 153 The efficacy of opioid antagonists like naltrexone in treating food addiction has yet to be explored in human subjects but merits research attention.

Although selective serotonin reuptake inhibitors (SSRIs) were one of the first medications that were used to treat disordered gambling, controlled clinical trials assessing SSRIs have demonstrated mixed results for both behavioral and substance addictions. 49 Fluvoxamine and paroxetine were reported to be superior to placebo in several trials 154 , 155 but not in others. 156 , 157 Efficacy may differ among behavioral addictions. Citalopram, another SSRI, was found effective in reducing hypersexual disorder symptoms among homosexual and bisexual men 158 but, among individuals with Internet addiction disorder, did not reduce the number of hours spent online or improve global functioning. 159 SSRI treatments remain an active area of investigation, 8 , 41 and further research is needed to assess the potential clinical utilization of SSRIs for disordered gambling and other behavioral addictions.

Glutamatergic treatments have shown mixed promise in small controlled trials. N-acetyl cysteine has shown preliminary efficacy both as a stand-alone agent 160 and in conjunction with behavioral treatment. 161 Topiramate, however, did not show any differences to placebo in treating disordered gambling. 162 Additionally, the results from these and most other pharmacotherapy trials of behavioral addictions are limited because of the trials’ small sample sizes and short-term treatment durations.

Behavioral Treatments

Meta-analyses of psychotherapeutic and behavioral treatment approaches for disordered gambling suggest that they can result in significant improvements. Positive effects can be retained (though to a lesser degree) over follow-ups of up to two years. 163

One approach that has gained empirical support from randomized trials is cognitive behavioral therapy (CBT). This semistructured, problem-oriented approach focuses, in part, on challenging the irrational thought processes and beliefs that are thought to maintain compulsive behaviors. During therapy, patients learn and then implement skills and strategies to change those patterns and interrupt addictive behaviors. 164 , 165 Therapists facilitate the replacement of dysfunctional emotions, behaviors, and cognitive processes through engagement in alternative behaviors and a series of goal-orientated, explicit, systematic procedures. CBT is multifaceted but typically involves keeping a diary of significant events and associated feelings, thoughts, and behaviors; recording cognitions, assumptions, evaluations, and beliefs that may be maladaptive; trying new ways of behaving and reacting (e.g., replacing video-game playing with outdoor activities); and, in the cases of disordered gambling and compulsive shopping, learning techniques to properly manage finances. 166 Such factors are important for initial abstinence but are also essential for relapse prevention. The particular therapeutic techniques that are employed may vary according to the particular type of patient or issue. For example, patients who are having trouble controlling cravings may utilize modules that teach coping strategies specifically for managing cravings. CBT approaches have the strongest evidence base of any of the psychotherapeutic approaches, 167 with a meta-analysis of randomized, controlled trials demonstrating improvement in gambling-related variables after treatment and at follow-ups in problem gamblers. 163 In individuals with Internet addiction, CBT has demonstrated efficacy in reducing time spent online, improving social relationships, increasing engagement in offline activities, and increasing the ability to abstain from problematic Internet use. 168

In addition to psychotherapeutic treatments such as CBT, self-help options are available. Although such options have been found to be beneficial for a range of individuals, they may be especially attractive to those people who do not meet diagnostic criteria for disordered gambling and who find psychotherapeutic intervention too costly or intensive. 169 A recent study suggests that Internet-based programs may help reduce disordered gambling symptoms, including at a three-year follow-up. 170 A popular self-help group based on mutual support is Gambler’s Anonymous (GA). Based on the 12-step model of Alcoholics Anonymous, GA stresses commitment to abstinence, which is facilitated by a support network of more experienced group members (“sponsors”). The steps involve admitting loss of control over gambling behavior; recognizing a higher power that can give strength; examining past errors (with the help of a sponsor or experienced member) and making amends; learning to live a new life with a new code of behavior; and helping and carrying the message to other problem gamblers. 171 Interestingly, individuals with (vs. without) a history of GA attendance were more likely to display higher disordered gambling severity, more years of gambling problems, and larger debts at intake to (other) treatment. 172 GA has been shown to have beneficial effects for attendees with varying degrees of gambling severity; 173 however, attrition rates are often high. 174 The benefits of GA may be increased with adjunctive personalized therapy, and these two approaches, when combined, may be mutually beneficial in promoting continuation of treatment. 175 Meta-analyses indicate other self-help interventions (e.g., self-help workbooks and audiotapes) also demonstrate beneficial effects in disordered gambling and are superior to no treatment or placebo. The positive effects, however, are typically not as strong as those of other empirically tested psychotherapeutic approaches. 163

Brief motivational interviewing or enhancement—even as little as a 15-minute telephone consultation—has not only been demonstrated to be effective but in several studies has been shown to be more effective than other lengthier and more intensive approaches. 176 Motivational interventions center on exploring and resolving a patients’ ambivalence toward change, with the aim of facilitating intrinsic motivation and self-efficacy through dealing with problem behaviors. Such interventions could provide a cost-effective, resource-conserving approach and could be particularly useful in individuals reluctant to engage in prolonged therapy on account of stigma, shame, or financial concerns.

Although the precise neural mechanisms mediating the effects of behavioral and pharmacological treatments are unclear, an improved understanding of them could provide insight into the mechanisms underlying specific therapies and assist in treatment development and in matching treatments and individuals. Many promising facets of treatment have yet to be examined in the context of behavioral addictions. For example, positive family involvement has been shown to be beneficial in the treatment of SUDs 177 and may be similarly helpful in treating behavioral addictions. Additionally, phenotypic heterogeneity exists within each behavioral addiction, and identifying clinically relevant subgroups remains an important endeavor. Testing specific, well-defined behavioral therapies in randomized, controlled trials is also important in validating treatment approaches. Neurocircuitry relating to specific behavioral therapies has been proposed. 178 The incorporation of pre- and posttreatment neuroimaging assessments into clinical trials represents an important next step for testing these hypotheses.

Combined Approaches

While much progress has been made in identifying and developing effective pharmacological and behavioral therapies, no existing treatment is completely effective on its own. Combining complementary treatments may help to address weaknesses in either therapy and may thereby catalyze beneficial treatment outcomes. Initial trials using combined approaches have yielded mixed results, with some positive results reported for disordered gambling. 161

Natural Recovery

Repeated failed attempts to control gambling constitute a diagnostic feature of disordered gambling, which has typically been taken to imply that gambling disorder may be chronic and associated with multiple relapses. New data are challenging this notion, however, as they indicate variability in the trajectories of gambling problems, indicating a more transient, episodic pattern. 1 , 12 , 179 Formal treatment is uncommon (less than 10%) of individuals who meet criteria for disordered gambling seek formal treatment), 180 , 181 the reasons cited for not seeking treatment include denial, shame, and the desire to handle the problem independently. 182 Very little longitudinal research is available on the natural course of disordered gambling, and still less for other behavioral addictions. Some evidence suggests that young adults frequently move in and out of gambling problems. 183 Although few direct, long-term studies of gambling relapse have been conducted, it is reasonable to hypothesize that treatment may be essential for sustained abstinence.

Prevention Strategies

Prevention interventions are important in curbing addictive behaviors. The cost to society of such behaviors could be reduced by introducing and implementing effective educational campaigns that promote community awareness about these behaviors’ potentially deleterious health effects and that alert the medical community to the importance of evaluating and treating behavioral addictions. Policies should promote responsible engagement in these behaviors and improve treatment access. Given the high prevalence of behavioral addictions among youth, 184 school-based prevention programs may be especially beneficial.

OTHER CONSIDERATIONS

Addictions vary. Social acceptability, a substance’s availability, and a behavior’s pervasiveness may represent important considerations for treatment. Each behavioral addiction may represent a heterogeneous construct, with specific subtypes potentially relating differently to psychological processes. Different forms of gambling (e.g., strategic versus nonstrategic, sports betting) and different locations (e.g., casino) may present different risks for developing disordered gambling. 185 , 186 Similarly, different genres of game playing (e.g., massive, multiplayer online role playing, puzzle and strategy, action), different forms of Internet use (e.g., social networking, email, blogging), and different types of food (e.g., sugar, fat) may possess different addictive potentials and engage cognitive, behavioral, and affective systems in distinct manners. Such differences are important to consider, and warrant further research.

CONCLUDING REMARKS

Despite significant advances in research, behavioral addictions remain poorly understood. Our understanding of efficacious, well-tolerated pharmacological and behavioral strategies for behavioral addictions lags significantly behind our understanding of treatments for other major neuropsychiatric disorders. Given the health burden and social impact of these behavioral conditions (e.g., the estimated lifetime cost of disordered gambling in the United States is $53.8 billion), 187 the development and improvement of prevention and treatment strategies are important. The development of health screens and formal diagnostic instruments to assess a full range of behavioral addictions may help reduce the public health burden of these conditions. Additional study in clinical trials of pharmacological and behavioral therapies for behavioral addictions is needed. Continued research may also help identify novel targets for treatment and may assist in identifying relevant individual differences that may be used to guide the selection of therapies. Despite differences, the overlaps between behavioral and substance addictions suggest that comprehensive research on the latter may inform an understanding of the former. Through targeted research efforts based on substance addiction findings, the etiology, treatment, and prevention and policy efforts relating to behavioral addictions will potentially move forward rapidly—reducing, in turn, the public health costs and human impact of these conditions.

Acknowledgments

Supported , in part, by National Institute on Drug Abuse grant nos. P20 DA027844, R01 DA018647, R01 DA035058, and P50 DA09241, National Center for Responsible Gaming, Connecticut State Department of Mental Health and Addictions Services, and Connecticut Mental Health Center (all Dr. Potenza).

Declaration of interests: Potenza has consulted for Lundbeck, Ironwood, Shire, and INSYS pharmaceuticals and RiverMend Health; received research support from Mohegan Sun Casino, Psyadon Pharmaceuticals, and National Center for Responsible Gambling; has participated in surveys, mailings, or telephone consultations related to drug addiction, impulse-control disorders, or other health topics; and has consulted for gambling, legal, and governmental entities on issues related to addictions or impulse-control disorders. The funding agencies did not provide input or comment on the content of the manuscript, which reflects the contributions and thoughts of the authors and not necessarily the views of the funding agencies.

November 1, 2013

How the Brain Gets Addicted to Gambling

Addictive drugs and gambling rewire neural circuits in similar ways

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When Shirley was in her mid-20s she and some friends road-tripped to Las Vegas on a lark. That was the first time she gambled. Around a decade later, while working as an attorney on the East Coast, she would occasionally sojourn in Atlantic City. By her late 40s, however, she was skipping work four times a week to visit newly opened casinos in Connecticut. She played blackjack almost exclusively, often risking thousands of dollars each round—then scrounging under her car seat for 35 cents to pay the toll on the way home. Ultimately, Shirley bet every dime she earned and maxed out multiple credit cards. “I wanted to gamble all the time,” she says. “I loved it—I loved that high I felt.”

In 2001 the law intervened. Shirley was convicted of stealing a great deal of money from her clients and spent two years in prison. Along the way she started attending Gamblers Anonymous meetings, seeing a therapist and remaking her life. “I realized I had become addicted,” she says. “It took me a long time to say I was an addict, but I was, just like any other.”

Ten years ago the idea that someone could become addicted to a habit like gambling the way a person gets hooked on a drug was controversial. Back then, Shirley's counselors never told her she was an addict; she decided that for herself. Now researchers agree that in some cases gambling is a true addiction.

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In the past, the psychiatric community generally regarded pathological gambling as more of a compulsion than an addiction—a behavior primarily motivated by the need to relieve anxiety rather than a craving for intense pleasure. In the 1980s, while updating the Diagnostic and Statistical Manual of Mental Disorders ( DSM ), the American Psychiatric Association (APA) officially classified pathological gambling as an impulse-control disorder—a fuzzy label for a group of somewhat related illnesses that, at the time, included kleptomania, pyromania and trichotillomania (hairpulling). In what has come to be regarded as a landmark decision, the association moved pathological gambling to the addictions chapter in the manual's latest edition, the DSM-5 , published this past May. The decision, which followed 15 years of deliberation, reflects a new understanding of the biology underlying addiction and has already changed the way psychiatrists help people who cannot stop gambling.

More effective treatment is increasingly necessary because gambling is more acceptable and accessible than ever before. Four in five Americans say they have gambled at least once in their lives. With the exception of Hawaii and Utah, every state in the country offers some form of legalized gambling. And today you do not even need to leave your house to gamble—all you need is an Internet connection or a phone. Various surveys have determined that around two million people in the U.S. are addicted to gambling, and for as many as 20 million citizens the habit seriously interferes with work and social life.

Two of a Kind

The APA based its decision on numerous recent studies in psychology, neuroscience and genetics demonstrating that gambling and drug addiction are far more similar than previously realized. Research in the past two decades has dramatically improved neuroscientists' working model of how the brain changes as an addiction develops. In the middle of our cranium, a series of circuits known as the reward system links various scattered brain regions involved in memory, movement, pleasure and motivation. When we engage in an activity that keeps us alive or helps us pass on our genes, neurons in the reward system squirt out a chemical messenger called dopamine, giving us a little wave of satisfaction and encouraging us to make a habit of enjoying hearty meals and romps in the sack. When stimulated by amphetamine, cocaine or other addictive drugs, the reward system disperses up to 10 times more dopamine than usual.

Continuous use of such drugs robs them of their power to induce euphoria. Addictive substances keep the brain so awash in dopamine that it eventually adapts by producing less of the molecule and becoming less responsive to its effects. As a consequence, addicts build up a tolerance to a drug, needing larger and larger amounts to get high. In severe addiction, people also go through withdrawal—they feel physically ill, cannot sleep and shake uncontrollably—if their brain is deprived of a dopamine-stimulating substance for too long. At the same time, neural pathways connecting the reward circuit to the prefrontal cortex weaken. Resting just above and behind the eyes, the prefrontal cortex helps people tame impulses. In other words, the more an addict uses a drug, the harder it becomes to stop.

Research to date shows that pathological gamblers and drug addicts share many of the same genetic predispositions for impulsivity and reward seeking. Just as substance addicts require increasingly strong hits to get high, compulsive gamblers pursue ever riskier ventures. Likewise, both drug addicts and problem gamblers endure symptoms of withdrawal when separated from the chemical or thrill they desire. And a few studies suggest that some people are especially vulnerable to both drug addiction and compulsive gambling because their reward circuitry is inherently underactive—which may partially explain why they seek big thrills in the first place.

Even more compelling, neuroscientists have learned that drugs and gambling alter many of the same brain circuits in similar ways. These insights come from studies of blood flow and electrical activity in people's brains as they complete various tasks on computers that either mimic casino games or test their impulse control. In some experiments, virtual cards selected from different decks earn or lose a player money; other tasks challenge someone to respond quickly to certain images that flash on a screen but not to react to others.

A 2005 German study using such a card game suggests problem gamblers—like drug addicts—have lost sensitivity to their high: when winning, subjects had lower than typical electrical activity in a key region of the brain's reward system. In a 2003 study at Yale University and a 2012 study at the University of Amsterdam, pathological gamblers taking tests that measured their impulsivity had unusually low levels of electrical activity in prefrontal brain regions that help people assess risks and suppress instincts. Drug addicts also often have a listless prefrontal cortex.

Further evidence that gambling and drugs change the brain in similar ways surfaced in an unexpected group of people: those with the neurodegenerative disorder Parkinson's disease. Characterized by muscle stiffness and tremors, Parkinson's is caused by the death of dopamine-producing neurons in a section of the midbrain. Over the decades researchers noticed that a remarkably high number of Parkinson's patients—between 2 and 7 percent—are compulsive gamblers. Treatment for one disorder most likely contributes to another. To ease symptoms of Parkinson's, some patients take levodopa and other drugs that increase dopamine levels. Researchers think that in some cases the resulting chemical influx modifies the brain in a way that makes risks and rewards—say, those in a game of poker—more appealing and rash decisions more difficult to resist.

A new understanding of compulsive gambling has also helped scientists redefine addiction itself. Whereas experts used to think of addiction as dependency on a chemical, they now define it as repeatedly pursuing a rewarding experience despite serious repercussions. That experience could be the high of cocaine or heroin or the thrill of doubling one's money at the casino. “The past idea was that you need to ingest a drug that changes neurochemistry in the brain to get addicted, but we now know that just about anything we do alters the brain,” says Timothy Fong, a psychiatrist and addiction expert at the University of California, Los Angeles. “It makes sense that some highly rewarding behaviors, like gambling, can cause dramatic [physical] changes, too.”

Gaming the System

Redefining compulsive gambling as an addiction is not mere semantics: therapists have already found that pathological gamblers respond much better to medication and therapy typically used for addictions rather than strategies for taming compulsions such as trichotillomania. For reasons that remain unclear, certain antidepressants alleviate the symptoms of some impulse-control disorders; they have never worked as well for pathological gambling, however. Medications used to treat substance addictions have proved much more effective. Opioid antagonists, such as naltrexone, indirectly inhibit brain cells from producing dopamine, thereby reducing cravings.

Dozens of studies confirm that another effective treatment for addiction is cognitive-behavior therapy, which teaches people to resist unwanted thoughts and habits. Gambling addicts may, for example, learn to confront irrational beliefs, namely the notion that a string of losses or a near miss—such as two out of three cherries on a slot machine—signals an imminent win.

Unfortunately, researchers estimate that more than 80 percent of gambling addicts never seek treatment in the first place. And of those who do, up to 75 percent return to the gaming halls, making prevention all the more important. Around the U.S.—particularly in California—casinos are taking gambling addiction seriously. Marc Lefkowitz of the California Council on Problem Gambling regularly trains casino managers and employees to keep an eye out for worrisome trends, such as customers who spend increasing amounts of time and money gambling. He urges casinos to give gamblers the option to voluntarily ban themselves and to prominently display brochures about Gamblers Anonymous and other treatment options near ATM machines and pay phones. A gambling addict may be a huge source of revenue for a casino at first, but many end up owing massive debts they cannot pay.

Shirley, now 60, currently works as a peer counselor in a treatment program for gambling addicts. “I'm not against gambling,” she says. “For most people it's expensive entertainment. But for some people it's a dangerous product. I want people to understand that you really can get addicted. I'd like to see every casino out there take responsibility.”