research papers on gambling addiction

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Risk Factors for Gambling Disorder: A Systematic Review

Diana moreira.

1 Centro de Solidariedade de Braga/Projecto Homem, Braga, Portugal

2 Faculty of Philosophy and Social Sciences, Centre for Philosophical and Humanistic Studies, Universidade Católica Portuguesa, Rua de Camões, 60, 4710-362 Braga, Portugal

3 Faculty of Psychology and Educational Sciences, Laboratory of Neuropsychophysiology, University of Porto, Porto, Portugal

4 Institute of Psychology and Neuropsychology of Porto – IPNP Health, Porto, Portugal

Andreia Azeredo

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.

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 caus*

 OR vulnerabilit*

 OR outcome*

 OR chang*

 OR barrier*

 OR risk

 OR seek*

 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 ​ (Table1 1 ).

Summary of Studies Characteristics

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.

Open access funding provided by FCT|FCCN (b-on).

Declarations

The authors declare that they have no conflict of interest.

Diana Moreira, Centro de Solidariedade de Braga/Projecto Homem, Universidade Católica Portuguesa, Faculty of Philosophy and Social Sciences, Centre for Philosophical and Humanistic Studies, Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto, and Institute of Psychology and Neuropsychology of Porto – IPNP Health (Portugal). Andreia Azeredo, Laboratory of Neuropsychophysiology, Faculty of Psychology and Educational Sciences, University of Porto (Portugal). Paulo Dias, Centro de Solidariedade de Braga/Projecto Homem and Universidade Católica Portuguesa, Faculty of Philosophy and Social Sciences, Centre for Philosophical and Humanistic Studies (Portugal). The authors do not have any interests that might be interpreted as influencing the research. The study was conducted according to APA ethical standards.

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• Open access
• Published: 27 August 2020

Risk factors for gambling and problem gambling: a protocol for a rapid umbrella review of systematic reviews and meta-analyses

• Caryl Beynon 1 ,
• Nicola Pearce-Smith 1 &
• Rachel Clark   ORCID: orcid.org/0000-0003-2800-2713 1  

Systematic Reviews volume  9 , Article number:  198 ( 2020 ) Cite this article

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Gambling and problem gambling are increasingly being viewed as a public health issue. European surveys have reported a high prevalence of gambling, and according to the Gambling Commission, in 2018, almost half of the general population aged 16 and over in England had participated in gambling in the 4 weeks prior to being surveyed. The potential harms associated with gambling and problem are broad, including harms to individuals, their friends and family, and society. There is a need to better understand the nature of this issue, including its risk factors. The purpose of this study is to identify and examine the risk factors associated with gambling and problem gambling.

An umbrella review will be conducted, where systematic approaches will be used to identify, appraise and synthesise systematic reviews and meta-analyses of risk factors for gambling and problem gambling. The review will include systematic reviews and meta-analyses published between 2005 and 2019, in English language, focused on any population and any risk factor, and of quantitative or qualitative studies. Electronic searches will be conducted in Ovid MEDLINE, Ovid Embase, Ovid PsycInfo, NICE Evidence and SocIndex via EBSCO, and a range of websites will be searched for grey literature. Reference lists will be scanned for additional papers and experts will be contacted. Screening, quality assessment and data extraction will be conducted in duplicate, and quality assessment will be conducted using AMSTAR-2. A narrative synthesis will be used to summarise the results.

The results of this review will provide a comprehensive and up-to-date understanding of the risk factors associated with gambling and problem gambling. It will be used by Public Health England as part of a broader evidence review of gambling-related harms.

Systematic review registration

PROSPERO CRD42019151520

Peer Review reports

Gambling is increasingly being identified as a public health problem [ 1 , 2 ]. Harms associated with gambling are wide-ranging and include harms not only to the individual gambler but to their families and close associates as well as wider society [ 3 , 4 ]. The global prevalence of problem gambling has been reported to range from 0.7 to 6.5%, and studies from across Europe have reported a high participation in gambling [ 5 ]. In 2018, a survey conducted in England by the Gambling Commission reported that almost half of the respondents had participated in gambling in the 4 weeks prior to being surveyed [ 6 ]. In addition, 0.7% of respondents were classified as ‘problem gamblers’ and an additional 1.1% of respondents were classified as ‘moderate risk’ gamblers, defined as ‘those who experience a moderate level of problems leading to some negative consequences’ [ 6 ]. The threshold for being considered a ‘problem gambler’ within this particular survey is high—a person has to score 8 or more on the Problem Gambling Severity Index (PGSI) or 3 or more according to the Diagnostic or Statistical Manual-IV [ 7 ]. So the number of people experiencing problem gambling could well be higher.

Risk factors are traits or exposures that increase the possibility that an individual will develop a condition and can be fixed or variable [ 8 ]. The risk factors for gambling and problem gambling are broad and have been reported in numerous systematic reviews and primary studies. At an individual level, risk factors include (but are not limited to) fixed biological factors, such as gender and impulsivity, and behavioural factors such as levels of participation in gambling, excessive use of alcohol and use of illicit drugs and propensity towards violent behaviour [ 9 ]. Broader factors related to the family environment [ 10 ] and gambling availability have also been identified [ 11 ]. A scoping search identified a number of systematic reviews and meta-analyses of risk factors for problem gambling, largely focused on specific risk factors or types of risk [ 9 , 10 , 11 ] although one focused on specific populations [ 12 ]. No systematic reviews, meta-analyses or umbrella reviews were identified examining all risk factors for all populations. In order to understand the breadth of possible risk factors driving gambling and problem gambling behaviours, there is a need to collate this review-level evidence. This work is part of a broader review examining gambling-related harms [ 13 , 14 ].

The overall aim of this umbrella review is to identify the risk factors associated with gambling and problem gambling. The research questions are as follows:

What risk factors are associated with gambling?

What risk factors are associated with different levels of gambling intensity?

This review adopted a rapid review methodology [ 15 ] to identify, appraise and synthesise systematic reviews and meta-analyses, defined here as an ‘umbrella’ review [ 16 ]. The use of existing systematic reviews and meta-analyses enables a broad examination of best available evidence in a timely way and is useful for addressing the high-level questions set out for this review, where multiple risk factors are expected to be identified. This review protocol is being reported in accordance with reporting guidance provided in the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) statement [ 17 ] (see checklist in Additional file 1 ). The protocol is registered on PROSPERO (CRD42019151520). The review will be conducted using EPPI-Reviewer 4.

Definitions of terms

There are multiple definitions of the term ‘gambling’, but for the purpose of this review, gambling is defined (as set out by the Gambling Act 2005) as ‘… any kind of betting, gaming or playing lotteries. Gaming means taking part in games of chance for a prize (where the prize is money or money’s worth), betting involves making a bet on the outcome of sports, races, events or whether or not something is true, whose outcomes may or may not involve elements of skill but whose outcomes are uncertain and lotteries (typically) involve a payment to participate in an event in which prizes are allocated on the basis of chance.’ [ 4 ].

There is no single definition for ‘harmful’ or ‘problem’ gambling, and this can be measured in several ways. For example, reports prepared for the Gambling Commission estimate problem gambling according to scores derived from 2 different instruments: the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) and the Problem Gambling Severity Index (PGSI). The DSM-IV contains 10 diagnostic criteria and possible scores are between 0 and 10; a score of 3 or over indicates problem gambling. The PGSI contains 9 diagnostic criteria and a score of between 0 and 27 is possible; a score of 1–2 is ‘low risk’, 3–7 is ‘moderate risk’ and 8 and over is ‘problem gambling’ [ 7 ]. In the USA, the South Oaks Gambling Screen (SOGS) is commonly used, where positive answers to three out of twenty gambling-related questions are considered indicative of problem gambling [ 18 ]. In order to capture the breadth of literature available, no one definition will be adopted and this review will include papers which define ‘harmful’ or ‘problem’ gambling in different ways.

In the context of this review, a risk factor is defined as any factor investigated as being associated with gambling (including initiation, escalation, urge or intensity), either causally or otherwise. Where the evidence shows the link to be causal (rather than an association), this will be reported.

Inclusion and exclusion criteria

Inclusion and exclusion criteria have been developed using an adapted version of the PICO (population, intervention, comparison, outcome) framework, as set out in Table 1 .

It is expected that two types of study will be identified for inclusion: (i) those that focus on the gambling population and explore all risk factors and (ii) those that focus on a specific risk factor.

Additional inclusion criteria:

Language: English (other languages will not be included, due to the team’s inability to translate)

Publication date: 1 January 2005–4 September 2019. 2005 was selected as a cut-off as in this year the Government issued proposals to reform the law on gambling [i.e. the Gambling Act] and the Economic and Social Research Council/Responsibility in Gambling Trust provided £1 million of funding for research on problem gambling—significantly increasing capacity for research on this topic in England [ 19 ].

Publication type: peer reviewed and grey literature

Setting: reviews of studies which are based within the Organisation for Economic Co-operation and Development (OECD). Where studies set in non-OECD countries are also included, more than half of included studies must be from OECD countries and inclusion/exclusion will be considered on a case-by-case basis.

Search strategy

A comprehensive search will be undertaken using multiple methods to identify both published and grey literature. The search strategy was developed by a Senior Information Scientist in PHE and quality assured by a second Information Scientist.

Electronic searches

The following databases will be searched: Ovid MEDLINE, Ovid Embase, Ovid PsycINFO, Social Policy and Practice, Social Care Online, NICE Evidence and SocIndex via EBSCO. The number of papers retrieved from each database will be recorded. The full MEDLINE search is presented in Additional file 2 ; this will be adjusted for use in other databases. The search will look for terms in the title, abstract, author key words and thesaurus terms (such as MeSH Medical Subject Headings in MEDLINE) where available. The review filter will be used for all databases except for SocIndex (which does not have a validated one). For SocIndex, a set of search terms will be created in order to restrict the search to systematic reviews and meta-analyses.

Grey literature

Reports and other relevant literature that may not be published in databases will be sought by searching Google and websites such as those listed here (years 2005 to 2019). If a website provides a review summary, effort will be made to find the full study report.

Gamble Aware InfoHub

Gambling Commission

GambLib (Gambling Research Library)

National Problem Gambling Clinic

Gordon Moody Association

Gamblers Anonymous

Gambling Information Resource Office Research Library

Advisory Board for Safer Gambling

Gambling Watch UK

Australian Gambling Research Centre

Gambling Research Exchange Ontario

Citizens Advice Bureau

Be Gamble Aware

Problem Gambling, Wigan Council

Gambling Compliance

Child Family Community Australia

International Centre for Youth Gambling Problems and High-Risk Behaviours

Gambling and Addictions Research Centre

Alberta Gambling Research Institute

Responsible Gambling Council

Problem Gambling Foundation of New Zealand

Gambling Commission New Zealand

Victorian Responsible Gambling Foundation

Handsearching

Reference lists of retrieved papers will be searched for additional relevant papers which fulfil the inclusion/exclusion criteria. In addition, if any umbrella reviews are identified, the reference lists will be scanned for inclusion.

Consultation with experts

Once a list of included studies is available, this will be shared with the project Expert Reference Group to check for additional studies. This group includes national and international topic experts.

Screening and selection procedure

A pilot screen will be undertaken whereby each reviewer will independently screen the same 100 randomly selected references/papers and indicate which should be included/excluded. Reviewers will obtain the full paper if this is needed for them to make their assessment. Any discrepancies indicate inconsistencies in understanding of the inclusion/exclusion criteria between reviewers, and this stage will allow these to be identified, discussed and resolved. If necessary, the inclusion/exclusion criteria will be modified, and the changes will be recorded in a decision log.

References will be divided between four reviewers. The title/abstract of every reference will be screened independently by two reviewers (‘review pairs’) according to the inclusion/exclusion criteria, and each reference will be coded as either ‘included’ or ‘excluded’. EPPI-Reviewer will be used to measure inter-rater agreement for all reviewer pairs; agreement of 90% or over will be considered acceptable. If the agreement is less than 90%, the reason will be explored and rectified and screening will be repeated, in line with the guidance from the National Institute for Health and Care Excellence (NICE) on title/abstract screening [ 20 ].

The full articles of the remaining references will be obtained. Full articles will be divided between reviewers and screened using inclusion/exclusion codes set up in advance by the Project Team. Ten percent of the papers screened by each reviewer will be reviewed independently by a second reviewer using the ‘parent’ codes: include and exclude (i.e. rather than specific exclusion codes such as ‘date’, ‘geography’, ‘study type’). A threshold of 80% agreement will be considered acceptable in line with criteria outlined in the AMSTAR 2 (Assessing the Methodological Quality of Systematic Reviews) tool [ 21 ]. A decision on what steps should be taken if the agreement is less than 80% will be made by the Project Team should this situation arise.

Data extraction

Data extraction tables will be used to extract the relevant information from each study. These will include the following information: authors, date, country, the PICO-S elements and the relevant results. Authors will be contacted by the reviewers to ask for missing information or clarification where necessary, and where information is considered essential. Data extraction tables will be pilot tested before being used and signed off by the Expert Reference Group. All reviewers will extract the data from a set of eligible studies; 10% of all papers will be randomly selected and the data from these will be extracted independently by a second reviewer. Agreement between reviewers for data extraction will be checked to ensure this is acceptable (at least 80%). A decision on what steps should be taken if the agreement is less than 80% will be made by the Project Team should this situation arise. The Cochrane PROGRESS-Plus tool [ 22 ] will be used to extract data on the broad dimensions of inequality.

Quality assessment (risk of bias)

The quality of systematic reviews will be assessed using the AMSTAR2 checklist [ 21 ]. Each paper will be independently assessed by two reviewers, and disagreements will be resolved through discussion. If required, a third person will be brought in to resolve ongoing disagreements.

Method of synthesis

Given the broad scope of this review, included studies are likely to be heterogeneous, and therefore, a narrative analysis will be conducted with text used to summarise and explain findings [ 23 ]. Studies will be summarised according to themes. An appraisal of the quality of the literature will be included. Differences by sub-group will be examined where this is reported in the literature to integrate a focus on equity, using the Cochrane PROGRESS-Plus tool [ 22 ]. The body of evidence will be assessed according to the four principles laid out in the CERQual approach which are (1) the methodological limitations of the studies which make up the evidence, (2) the relevance of findings to the review question, (3) the coherence of the findings and (4) the adequacy of data supporting the findings [ 24 ].

This rapid umbrella review will identify and examine the breadth of risk factors associated with gambling and problem gambling. The findings of this review will be utilised as part of a broader review of evidence conducted by Public Health England on gambling-related harms. A full report of this work will be shared and discussed with government departments and published on our government website GOV.UK. The results of this review will also be submitted for publication in a peer review journal.

Any deviations to the protocol considered necessary will be discussed by the Project Team prior to being implemented and documented in a decision log (stored in Excel) for later reporting.

A number of limitations are anticipated. The reliance on existing systematic reviews and meta-analyses is impacted by the quality of their methods and reporting—whilst we are assessing this, if the quality is poor, our ability to fully utilise their results will be limited. In addition, there may be a large number of systematic reviews and meta-analyses, and if they are focused on different risk factors, the results may be difficult to synthesise.

Availability of data and materials

Not applicable.

Abbreviations

Assessing the Methodological Quality of Systematic Reviews

Confidence in the Evidence from Reviews of Qualitative Research

Diagnostic and Statistical Manual

National Institute for Health and Care Excellence

Organisation for Economic Co-operation and Development

Problem Gambling Severity Index

Public Health England

Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols

South Oaks Gambling Screen

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Acknowledgements

The authors would like to thank the following people who either supported the development of the methods or provided feedback on the protocol:

Jenny Mason, Mary Gatineau, Fionnuala O’Toole, Alyson Jones, Dr Robyn Burton, Marguerite Regan, Clive Henn, Dr Felix Greaves, and Professor John Marsden.

This review will be funded by Public Health England.

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Caryl Beynon, Nicola Pearce-Smith & Rachel Clark

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CB and RC developed the methods. NPS developed the search strategy. All participated in drafting the manuscript. RC will be the guarantor of the review. The author(s) read and approved the final manuscript.

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Correspondence to Rachel Clark .

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Supplementary information

Additional file 1..

PRISMA Checklist

Additional file 2.

MEDLINE search. Full search conducted in MEDLINE, enabling replication of review

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Beynon, C., Pearce-Smith, N. & Clark, R. Risk factors for gambling and problem gambling: a protocol for a rapid umbrella review of systematic reviews and meta-analyses. Syst Rev 9 , 198 (2020). https://doi.org/10.1186/s13643-020-01455-x

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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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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.

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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]

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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.”

O.J. Simpson died after his battle with cancer, according to his family.

• LIFE AFTER ACQUITTAL
• MIXED REACTION

New study reveals link between sports betting and increased alcohol consumption

by BARBARA MORSE, NBC 10 NEWS

A new study out of the University of Nevada, Las Vegas, shows a clear link between sports betting and alcohol use.

"It's hidden. Often people go 8 to 10 years before they get help," said Dr. Shane Kraus, a professor of psychology, and the Director of the addictions lab at UNLV.

Gambling, he says, is a $300 billion business, and the odds are not in your favor.

It turns out, gambling isn't the only risky behavior.

"Those who are engaging in sports betting are drinking more than those who don't do sports betting and they're actually doing a lot more binge drinking than those who don't," said Kraus.

He and his research team surveyed more than 4000 people so far. About 1800 of them identified themselves as gamblers who'd bet on sports in the past year.

"It's a clear narrative. We're seeing a lot of people who are doing a lot of sports betting are drinking more frequently," said Kraus.

We're talking about 1.9% more likely to overconsume alcohol, which is defined as five or more drinks for men, four or more drinks for women at a single time.

• HEALTH CHECK: RI ALS organization has new name, same mission

Now their goal is this:

"We want to understand who kind of developed, who started with the problem and kept the problem with gambling, who didn't have any issues with gambling and developed the problems, so we're going to try and understand who's at risk over time with gambling," said Kraus.

Meantime, you know you have a problem with gambling.

"If you can't take it out of your wallet or your paycheck right now, you can't do it and when you find yourself chasing winnings when you're trying to catch up for your losses," said Kraus.

This research is funded by the International Center for responsible gaming and is published in JAMA Network Open.

Kraus said more results from this continuing study will be coming out in coming weeks and months.

Meantime, the experts said gambling and substance use disorder are both addictions. Officials said both are treatable.

Gambling addiction hotlines say volume is up and callers are younger as online sports betting booms

In state after state, centers for problem gambling are noticing an alarming rise in calls to their helplines. 

The circumstances reported are also getting more severe, according to the directors of five problem gambling centers, a gambling researcher and an addiction counselor. People are filing for bankruptcy or losing homes or relationships. At the same time, callers are skewing younger, the experts said — often men in their 20s and 30s.

The directors say the mounting call volume has coincided with the legalization of sports betting and rising popularity of sports betting apps. 

“We believe, nationwide, the rate and severity of gambling problems have increased across the United States since 2018,” said Keith Whyte, executive director of the National Council on Problem Gambling, a nonprofit organization devoted to minimizing the costs and harms of gambling addiction. The group operates the helpline 1-800-GAMBLER. 

“We have every reason to believe the growth of online sports betting is a major contributing factor to the increase in gambling problems,” Whyte said.

In 2018, the Supreme Court struck down a law banning sports betting. To date, 38 states and Washington, D.C., have legalized the practice, according to the American Gaming Association , which advocates for the industry. Thirty of those states allow mobile sports betting.

Florida legalized sports betting in November, and it has since seen calls to its Council on Compulsive Gambling double. Pennsylvania’s Council on Compulsive Gambling saw call volume more than double from 2020 to 2023. 

Calls to Ohio’s Problem Gambling Network, meanwhile, increased 55% in 2023, the first year of legalized sports betting there.

“We are seeing this new trend where it is in fact sports bettors who are making up that big bump in call volume,” said Michael Buzzelli, the organization’s associate director. By February 2023, sports betting was the top form of problematic gambling reported to the helpline, surpassing lottery and casino slot machines, he said.

The issue has gained renewed attention in recent weeks after the Los Angeles Dodgers fired Shohei Ohtani’s interpreter , Ippei Mizuhara, following allegations that he stole money from the star to cover gambling debts. (Mizuhara’s story has shifted, and he has not commented directly about the theft allegations.) Around the same time, Cleveland Cavaliers coach J.B. Bickerstaff said he had received threats from gamblers . 

The companies behind the apps say they’re taking steps to reduce the risk of problem gambling, and they question whether the trend is as big as others say. In their view, mobile betting allows for oversight of practices that used to happen illegally, without safeguards.

FanDuel, which has amassed roughly 2.5 million average monthly players, said it sometimes  suspends or even closes accounts if customers display problematic activity, like wagering higher and higher amounts after a series of losing bets. 

“We’re making sure that the small percentage of people in the United States that should not be using our platform — similar to how they should not be drinking alcohol if they have a problem with alcohol — are not able to get access to it,” said Chris Jones, a spokesperson for the company. He declined to specify how many such instances there have been.

DraftKings — which had 3.5 million average monthly paid users at the end of last year , up from 1.5 million at the end of 2020 — declined to be quoted for this story.

Experts on problem gambling said that despite the companies’ efforts, there’s still a higher risk of addiction now that sports betting is more accessible and highly advertised. 

Several experts pointed out particular aspects of online sports betting that they said can make it more addictive than traditional gambling. For instance, the focus on sports can make bets seem less risky, since people have prior knowledge of a player’s stats or a team’s performance. And unlike a casino, mobile apps allow people to wager money directly from their bank accounts. 

“One can easily, rapidly place many bets that may make it more feasible for vulnerable individuals to experience gambling problems,” said Marc Potenza, director of the Center of Excellence in Gambling Research at Yale University. 

Plus, the possibilities are virtually endless. 

“There’s hundreds, if not thousands, of wagers within each individual game that can be placed now on a smartphone,” said Josh Ercole, executive director of the Council on Compulsive Gambling of Pennsylvania. “You’re not calling your bookie to place ‘the Eagles are going to beat the Giants’ or whatever.”

Whyte said sports betting apps should have easy-to-use, visible tools that allow players to set limits on the time and money they can spend, and they should offer easy access to the national helpline.

DraftKings, FanDuel and other apps do enable users to limit the money they can wager, number of bets, or time spent betting. DraftKings users can find that in their settings, under the title “Responsible Gaming,” while FanDuel users can do so via a small icon labeled “RG” at the top of the homepage. FanDuel also advertises a helpline when users log in, and both apps allow users to voluntarily ban themselves.

Experts also said a rise in advertising for sports betting is helping to fuel the trend: U.S. sports betting operators spent around $282 million on national TV ads from September 2021 to May 2022, according to one report .

“Before 2018, there was no advertising for sports betting during events. Now, you not only have sports betting ads saturating the events, you can even bet on the game that’s right in front of you,” Whyte said. 

Cait DeBaun, a spokesperson for the American Gaming Association, suggested that advertising is “one of the top reasons that customers move to legal, regulated sportsbooks.” Some FanDuel ads highlight the app’s voluntary time and wager limits.

Jake, a 49-year-old member of Gamblers Anonymous, said he’s not opposed to legal sports betting but worries that advertising and targeted promotions make it difficult for some people to quit. He requested that his last name not be published for privacy reasons.

Online sports betting is not yet legal in Georgia, where Jake lives, but he said he sees lots of young people at Gamblers Anonymous meetings, many of whom are addicted to mobile gaming. 

“In meetings, I’m seeing people younger and younger. When I first started meeting, I was the youngest. I was in my early 40s,” he said.

Jake said his addiction took off when he started betting illegally online via bookies and offshore websites.

“I was highly competitive in sports and played a lot of high school sports,” Jake said. “After high school, I didn’t play and I missed the competitive edge. I chased that high, and I got it from gambling.”

In the end, he said, “I lost a marriage. I lost a business. … I would get loans, I would run credit cards up — hundreds of thousands of dollars lost.”

Around 1% of U.S. adults meet the diagnostic criteria for a gambling addiction, according to the National Council on Problem Gambling. But in 2021, a council survey found that a quarter of young adults frequently experienced at least one sign of problem gambling, like hiding bets from other people or feeling restless or irritable when they tried to scale back.

“The legalization of sports betting, the ease of online betting platforms and the normalization of sports wagering contribute to its prevalence among those struggling with addiction,” Lin Sternlicht, co-founder of Family Addiction Specialist in New York City, said in an email. She added that push notifications create a sense of urgency that can foster addictive behavior. 

Both the gaming industry and federal lawmakers have endeavored to address these issues.

Last week, seven gaming companies, including DraftKings and FanDuel, announced a trade group to promote responsible gaming research and education. The group hopes to create a database that enables companies to share information — for instance, if a user voluntarily bans themselves from one app, other companies would know to ban the user as well.

Last year, the American Gaming Association also instituted a requirement that people who appeared in sports betting ads be at least 21 and prohibited college partnerships that promoted sports betting. 

Meanwhile, Democrats in the House introduced a bill in January that would set aside 50% of revenue from a federal tax on sports wagers for gambling addiction treatment and research.

Whyte said the action is much needed.

“The federal government collected, last year, more than $8 billion in gambling tax revenue. Yet they don’t put a single penny of those windfall profits back into the health system,” he said.

Katie Mogg is an intern at NBC News.

Aria Bendix is the breaking health reporter for NBC News Digital.

The Use of Social Media in Research on Gambling: a Systematic Review

• Gambling (L Clark, Section Editor)
• Open access
• Published: 11 May 2021
• Volume 8 , pages 235–245, ( 2021 )

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• Richard J. E. James   ORCID: orcid.org/0000-0002-6644-7011 1 &
• Alex Bradley   ORCID: orcid.org/0000-0003-4304-7653 2  

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Purpose of Review

Social media enables a range of possibilities in the way gamblers and gambling operators interact and content communicate with gambling. The purpose of this systematic review was to synthesise the extant literature to identify the ways in which social media has been investigated in the context of gambling.

Recent Findings

A systematic review of the literature identified 41 papers that collected primary data pertinent to gambling and social media from multiple disciplines. These papers broadly fell into three themes: communication, community and calculation (of sentiment). Papers on communication focused on the content of gambling advertising on social media and the impact on people exposed to it. Studies of gambling communities studied the activity and structures of discussion groups on social media concerning recreational or problematic gambling. Papers on calculation collated social media data to assess sentiment and compared it against betting odds.

There is an emerging multidisciplinary literature that has looked at the use of social media in relation to gambling. There is preliminary evidence that the content and the reach of gambling advertising on social media is a source of concern, particularly for younger people. The themes discussed on gambling support forums appear to be common across communities, focusing on negative emotions, recovery, addictive products and financial support. Using social media to assess sentiment appears to be particularly effective at identifying potential upsets in sporting matches. Future suggestions for research are explored.

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Introduction

The internet has changed the way in which we communicate with each other and entertain ourselves. The gambling sector was an early adopter of online innovation [ 1 ]. One of the major developments has been the proliferation of social media, the communities and networks that enable the sharing of content between groups of people. The aim of this paper is to review the different research questions, methods and findings of studies that have looked at the intersection between social media and gambling research.

Social Media and Gambling

Kaplan and Haenlein [ 2 ] define social media by the use of Web 2.0 functionalities (e.g. Adobe Flash, RSS, AJAX), and the enabling and delivery of user-generated content—multimedia such as text, images, videos and sound. This encompasses a range of communities from blogs to online games and to social networking websites. In addition there are discussion forums that do not use Web 2.0 capabilities but involve the sharing of user content that are part of the same continuity as social media. Social media has itself evolved considerably over the last decade since, as most people now use social media in their daily lives, accessed through a diverse array of devices [ 3 ]. The most prominent accounts on social media now reach millions of people [ 4 ], and it has been adopted by businesses, pressure groups and political figures alike to reach the public [ 5 ].

Gambling companies have embraced social media as a means of communicating with potential customers. In the UK, analysis of the public accounts of gambling companies indicated that in 2017, around 60% of gambling advertising expenditure was spent on online advertising [ 6 ]. Social media represents around 10% of total advertising spend, and spend trebled between 2014 and 2017 [ 6 ]. Building on the existing literature in gambling advertising, there have been a number of studies that have looked at the content of gambling advertisements on social media [ 7 ••, 8 ••, 9 ••].

The proliferation of social media has allowed communities of likeminded people to propagate. In the case of gambling, groups of people discussing topics and issues related to both recreational and problematic gambling activities have developed. This has led to an interest in the nature of the content discussed, and the dynamics of these communities [ 10 ••]. Because many people who experience difficulties relating to gambling disorder do not undergo formal treatment [ 11 ], discussion groups represent an important source of informal care and a source of information for an even larger potential population who lurk and do not participate in the forums [ 12 ].

One area in which the role of social media in gambling has been extensively explored and is not considered in the scope of this review is in the context of social casino games. We recommend readers interested in this topic to reviews by Sirola et al. [ 13 ], and Gainsbury et al. [ 14 , 15 , 16 ]. Social casino games are simulated gambling games, or games with gambling themes, that are free to play on social media platforms, particularly Facebook. There is a developed literature on the role social games have on the transition to real money gambling and potentially addictive behaviour. However, social gaming is becoming less and less integrated with social media: the vast majority of social games are now available as standalone mobile applications as well as via social media. As web browsers increasingly discontinue plugins (e.g. Flash ) that are used by these games, players are increasingly likely to access them outside of social media. Further, although of interest to gambling researchers for many reasons, these are not classified as gambling per se . Gambling regulators have typically not treated them as a gambling activity because they do not involve the wagering of real money. Although many offer microtransactions, there is no way to cash out virtual ‘winnings’ and so do not meet legal definitions of gambling in many, but by no means all, countries.

The purpose of this systematic review was to build a comprehensive overview of the current literature relating to social media and gambling. In doing so, we aim to categorise the types of study in the field and highlight existing knowledge gaps and areas where further investigation is likely to be fruitful.

Search Strategy

Relevant studies were identified through searching electronic databases and citation searches. Four electronic databases were searched: Google Scholar, Web of Science, Scopus, and EBSCO Psychology and Behavioural Collection. A range of search terms were used to capture different social media platforms (Social Media, Social Networking, Twitter, Facebook, Instagram, Forums and Blogging) and gambling (Gambling, Betting and Casino). Each of the search terms representing social media platforms was paired with each of the terms representing gambling to yield a total of 21 searches which were conducted on Google Scholar. Of these 21 search terms, 11 yielded useful articles and were subsequently used as search terms on the other three databases. All searches were conducted from 4 th August to the 3 rd of September 2020. A total of 3265 articles were searched by their titles and abstracts with 85 of those articles being identified as potentially relevant. Backward citation searching identified a further six articles yielding a total number of 91 articles. Grey literature (e.g. reports to gambling regulators, businesses) that were identified in databases and citation searches was included if it reported some form of primary data collection The full text of the remaining 89 articles was inspected, and 41 articles met our inclusion and exclusion criteria (Fig. 1 ).

PRISMA flow chart of the systematic review

Exclusion Criteria

The following exclusion criteria were applied to each of the 89 articles. Studies that were tangentially related to social media [ 17 , 18 , 19 ] or focused on similarity were excluded, as were papers that did not report primary data [ 13 , 16 , 20 , 21 , 22 ]. Papers that were duplicates (e.g. preprints) and not written in English were also excluded. In the case of duplicates, the peer-reviewed version was selected for inclusion. After applying all the inclusion and exclusion criteria, a total of 41 articles were included in this narrative review.

Across the 41 papers included in the systematic review, we synthesised them into three broad areas of study: communication, community and calculation of sentiment. These groupings are ad hoc however, and it is important to be aware that other parcellations exist (e.g. by audience, media content, motivation, perceptions of social media vs. actual social media data). The papers are summarised in Table 1 . The vast majority of the papers included have been published since 2015 (Fig. 2 ).

Graph of the number of papers included in the systematic review published each year

Communication: Using Social Media to Advertise Gambling

The first theme of the review was the use of social media by gambling companies to engage with consumers and promote their products and services. Research in this area addressed questions around the volume and reach of gambling companies via social media, what content gambling operators are posting, the underlying message behind this content, and the extent that vulnerable users of social media are exposed to gambling content.

One consistent finding was the reach that gambling companies can have across social media platforms, often having hundreds of thousands of followers [ 7 ••, 24 , 28 ]. Research also suggests that some betting companies post frequently throughout the day, although there is considerable variability between companies [ 7 ••, 9 ••, 36 , 37 ]. The large reach gambling operators have, and the potential to deliver high volumes of messaging via social media, allows gambling operators to maintain engagement with their products and raise awareness of their services in ways that conventional forms (i.e. newspaper, radio and television) of advertising cannot [ 26 ].

Gambling operators use numerous types of content to engage their audiences [ 7 ••, 30 ], and there is convergent evidence of certain types being common: promotion of gambling products, competition, sports news, customer engagement, betting tips, features, responsible gambling, and the use of humour [ 8 ••, 24 , 34 ]. One common finding across the articles reviewed was the paucity of responsible gambling messages, with some operators never posting responsible gambling messages, whilst others embedded the messages in small text at the foot of pictures [ 7 ••, 9 ••, 24 , 34 , 36 ]. Research has begun to look more closely at the different types of promotional content in social media posts such as in-play odds, cash out, free bet offers, requested odds, enhanced odds, bet builder and cash price competition [ 9 ••]. In the content of the social media activity of British bookmakers, the most popular promotional content is requested odds, where consumers can build their own bets and gambling companies quote individualised odds back to them [ 7 ••, 9 ••]. Despite the higher potential payoff of these bets, they are rarely realised resulting in sizeable profit margins for book makers [ 35 •].

Researchers have also looked at the underlying meaning within gambling content on social media, and found that there is a tendency to portray gambling advertisements in a positive, glamorous light, often minimising the potential losses whilst highlighting the potential for winnings [ 7 ••, 23 , 32 ]. Other occurring themes noted by some authors were adventure, mateship, normalisation of gambling, sexualised imagery and gendered framing of content [ 24 , 28 , 36 ], often segmented towards young men. It has been suggested that content, whether by design or happenstance, is framed in a way that would appeal to children with the use of bright colours, cartoon characters, sounds, animations and celebrities [ 32 , 34 ].

These findings highlight the importance of considering where gambling content is placed within social media, and to what extent vulnerable groups like young people (i.e. too young to gamble) or those with gambling problems are exposed to these messages. The UK Gambling Commission found that 70% of young people had been exposed to gambling adverts through social media [ 39 ]. It also reports that around 10% of young people follow a gambling account on YouTube, Instagram or Facebook [ 39 ]. A subsequent report found that around 66% of young people were exposed to gambling advertisements via social media [ 32 ]. Attempts to profile and classify followers of bookmakers’ accounts suggest that around 6% of their followers were young people, which rises to 17% of followers on esports gambling accounts [ 32 ]. A review of leading Facebook games identified that references to gambling were common (in addition to simulate gambling content), most often for slot machines [ 31 ]. Young people are particularly at risk of exposure on social media if they are male and are sports fans [ 25 ••, 33 •]. The increased opportunity for segmentation and targeting the most receptive audiences that social media offers advertisers, may risk capturing users who for regulatory (e.g. under the age of 18) or corporate responsibility (e.g. at risk of harm) purposes would be of concern. As the evidence evolves it may be that gambling regulators see a need to intervene and set guidelines for the profiling of social media users.

Whilst the impact of young people viewing gambling adverts is still to be determined, we do know that those who report higher risks of problematic gambling are more likely to report increased gambling intentions and behaviours after viewing gambling adverts on social media [ 17 ], as are people who have positive gambling attitudes [ 27 •]. There is some early evidence that adverts on social media might foster positive attitudes toward gambling [ 29 ]. Despite the risk of exposure to young people, and the potential harm to those already vulnerable, the main forms of protection currently being deployed by social media companies and gambling companies appear to be using geo-location, age verification and taking down unsuitable adverts, all of which have their vulnerabilities [ 38 ••]. Additionally there is the complication that social media companies and gambling operators often reside in separate jurisdictions to users, which raises regulatory implications regarding the responsibility for safeguarding and the risk of exposure to unregulated gambling products. One recommendation has been that a different approach is required, based on consumer protection rather than gambling regulation that incorporates social media providers rather than placing the onus solely on gambling providers [ 38 ••].

Community: Gambling Forums as Places of Support or Recreation

The second theme was the use of data from gambling forums, which can be divided into forums where users either seek support and advice from fellow gamblers, or share tips and gambling strategies with each other.

Gambling support forums are popular sources of support that have the advantage of being accessible 24/7 and providing anonymous support to those who experience gambling difficulties. These are aimed at the gambler themselves or those affected by the gambler (e.g. family, friends) [ 10 ••, 28 ]. Several papers using observational, ethnographic and big data techniques have identified that posts can be categorised into: resources to aid recovery (formal and informal), supportive messages, personal stories, requests for help or specific questions, introductions and moderator messages, financial resources and support, impact on family and relationships, and expressing and managing negative emotions of shame and guilt [ 10 ••, 40 , 41 , 42 ].

In addition, analyses of the content of forum posts, and in some cases surveying forum users, have sought to discover and understand the benefits and potential drawbacks support forum usage has on its members. The use of support forums has been reported to reduce loneliness, enhance a sense of identity, increase self-knowledge, manage impulses to gamble, and enhance the range of strategies to manage gambling thoughts and behaviours [ 40 , 41 , 43 ••]. However, gambling forums do pose a number of challenges. For example, users have criticised moderators for being too slow to take down abusive posts or respond to those in crisis [ 28 , 41 ]. Equally, whilst they can offer helpful and useful advice from those who have expertise from experience, the support is not from qualified professionals [ 28 ]. There is tradeoff here between availability and lived experience, and established efficacy. Nonetheless there are encouraging indications that online support forums provide an important source of support to those struggling with gambling experiences and do considerably well despite the lack of resources and funding available.

Research on recreational gambling forums has explored the characteristics of the people who use these forums, and has helped to illuminate the types of posts made to these forums. Users of gambling forums are younger, typically male, heavier users of the internet, and are likely to already be classified as at risk or probable pathological gamblers [ 44 , 45 , 46 ]. There is mixed evidence that they might be more lonely from a subsample analysis [ 45 ]. Users of gambling forums reported greater use of payday loans and using online (real-money) casino sites, but were less likely to visit gambling support forums [ 44 ]. Posts onto these recreational gambling forums were related to gambling experiences, gambling tips/strategies and gambling news [ 44 , 47 ]. In-depth analysis of an online poker forum found two themes consisting of subjective experience of playing poker, and developing their poker skills, in terms of both the mathematical probability of playing, and the psychological control of themselves and other poker players strategies [ 48 ]. Whilst research has detailed the types of people who use pro-gambling forums and the type of content they post about, it is less clear what if anything can be done to help these at risk users from developing or continuing potentially harmful gambling behaviours. Equally, more needs to be done to understand the long-term efficacy of using and participating in an online forum to help manage gambling problems.

Calculation: The Wisdom of Crowds

The third theme that emerged involves the use of social media data to gauge the sentiment of people posting about events that can be bet upon such as sporting matches. Sentiment in these instances refers to the emotional valence social media users display in their postings or engagement towards an outcome. This is then used to predict the outcome of those events. Social media is essentially harnessed as a naïve prediction market, and compared against predictions derived from models such as ELO, or expert judgements such as bookmakers’ odds. A second aim of studies in this theme was to test whether these aggregated data provide additional informational value over bookmakers’ judgements and if they can be used to develop a profitable betting strategy. The majority of the research in this area studies sports (i.e. NFL, NBA, English Premier League) but has also been applied to esports and politics [ 49 , 50 ].

The papers in this field took a few approaches to modelling sentiment. Typically this involved using Twitter posts as their main source of data [ 50 , 51 ••, 52 , 53 ••], but some used specialised sports prediction websites or communities such as Transfermarkt [ 54 •, 55 •]. Others used likes or follows on social media as a measure of popularity to operationalise sentiment bias [ 49 , 56 •, 57 ]. Others have further tried to distinguish between absolute and relative sentiment, finding that changes in relevant sentiment have particular value in successfully predicting outcomes, and also where upsets are likely to occur.

Most papers in this area have compared various ways of operationalising sentiment against odds set by a bookmaker. Even under the most advantageous circumstances, these found sentiment to perform at a similar level of accuracy to bookmakers’ odds, i.e. both predicted the final outcome of a sporting match at the same rate of success. In many cases, sentiment was less predictive of outcomes than odds. However, a common finding that emerged was the ability of sentiment to pick up on longshot outcomes to occur. When setting odds on an outcome, bookmakers have to balance their own calibration of the probabilities of certain outcomes occurring, whilst at the same time creating a set of bets that are attractive to potential consumers. Where sentiment appeared to be particularly effective was in identifying the likelihood of an upset [ 53 ••, 58 ]. The ability of sentiment to predict upsets is important in understanding these might be profitable. Where sentiment-based approaches seem to have particular value is in their ability to identify successful upsets.

The other caveat noted in some of these analyses is that the predictive value of these may vary over time—in particular a couple of analyses [ 52 , 59 ] found that using social media had particular predictive value in the latter half of sports seasons. Further, it appears one of the areas where social media data has particular value is in its immediacy; sentiment on social media moves faster than the changing odds of a bookmaker, and it appears that in certain instances, this immediacy has particular value.

Social media is being used in gambling research to study how gambling companies communicate, how gambling communities develop and how sentiment can be calculated and harnessed to predict outcomes. The majority of these papers have been published in the last 5 years, from a diverse range of areas including gambling studies, economics, data science and sociology. The literature calculating sentiment has clear relevance to gambling but has been exclusively published in outlets unrelated to gambling studies. There are a number of areas that are worth highlighting where social media can be used in future gambling research.

New Platforms and Multimedia

It is important to be aware of the evolving ways in which people communicate on social networking sites and the increasing use of multimedia. At present, the literature has been mostly focused on text posted by gambling companies or users on Facebook and Twitter, which is representative of the way in which companies use social media [ 26 ]. However, social media increasingly involves the use of images, sound and videos, with some popular platforms such as Instagram and TikTok centred around these. Indeed, it has been previously noted that the majority of some gambling companies’ postings on Twitter include images [ 7 ••]. This critical gap in understanding the use of multimedia in gambling advertising warrants examination, and the analysis of image data remains a rich yet untapped source of data for gambling researchers. However, there are methodological challenges in processing these data. It is possible that classification approaches in machine learning can be used to classify images and multimedia at scale. This involves the training of a dataset based on a prior categories or themes, which can be built from rich, nuanced qualitative work, and applying them to a large novel set of data.

The Impact on Vulnerable Groups

One area of concern warranting further examination is the visibility of, and engagement with, gambling adverts on social media by people too young to legally gamble. There is early albeit converging evidence that many children see gambling adverts on social media [ 25 ••, 33 •, 39 ]. Further, a small proportion of followers of gambling companies’ social media profiles appear to be children as well [ 32 ]. It is important to note that gambling operators report wanting, alongside their regulatory obligations, to prevent young people from inadvertently accessing their social media profiles [ 26 ]. Nonetheless, does indicate that there is a need for further study, perhaps by using simulated social media profiles, to examine the ways in which social media advertising reaches younger people. The difficulty with looking at this solely from the perspective of the profiles of gambling companies is that this does not necessarily correspond to what the account’s followers see on their profile. Social media companies often have algorithms to filter the content users receive. Two users who follow the same gambling company on Twitter can be shown different volumes and types of content, based on variables such as the amount of accounts they follow, engagements with content in their network, or previous engagement with gambling content. Therefore there is a need to understand this heterogeneity, and its impact, from the perspective of the user. Studies that have analysed the advertising content on social media have noted the lack of responsible gambling messaging [ 7 ••, 9 ••]. Further research ought to determine the extent that there are differences between advertising on traditional and emerging platforms.

Diversity (of Communities)

Relevant to the use of new platforms and potentially vulnerable groups is the role of intermediaries such as affiliates, tipsters and influencers including celebrities. Whereas gambling companies report more focused social media presences [ 26 ], intermediaries are likelier to use a range of social media platforms, which include ones with greater multimedia usage. They operate in an ambiguous legal area and might be more likely to engage younger audiences [ 8 ••, 38 ••]. There is also a blurred line between content generated by influencers and commercial content that is worth rigorous study.

Further, there are many communities, groups and stakeholders involved in gambling, many of whom are active on social media and hitherto unexamined. Understanding the way social media is used for other gambling related issues is of further interest, especially in the interplay between various gambling stakeholders and policy [ 28 ]. This comes into sharpest focus when looking at communities of recreational gamblers, which has been limited to the online poker community [ 47 , 48 ]. It is worth pointing out that there are a wealth of other gambling communities that have not been explored. Although most of the focus in the communication and calculation of sentiment themes has fallen onto the betting sector, this has not been replicated in understanding the dynamics of the many betting communities that exist.

Longitudinal Research

Across each of the themes, there is potential in further harnessing the longitudinal nature of these data. The content of gambling advertisements for a sporting event will change as the event draws near. The makeup and topics of conversation of discussion groups evolve over months or years, and as information comes in sentiment data might become biased in the same way we see behaviour in stock markets [ 60 ]. Similarly within the theme of community, one of the topics that emerged was the issue of recovery, and particularly recovery as a process. The study of recovery is by its nature a longitudinal process, and exploring how users’ posts on social media change over time might give insight into how gambling support communities facilitate recovery, which has the potential to be scaled up and used for wider benefit. There are also important ethical considerations around the observation and use of data from gambling communities, and approaches that minimise intrusion (e.g. using statistical models to model topics) and/or maximise transparency (e.g. engaging with the group) are desirable.

Replicability

Across all of the themes, there is early encouraging evidence of convergence in studies looking at issues such as advertising content, frequencies and absences of responsible gambling messaging [ 7 ••, 8 ••, 9 ••], in topics on online communities [ 10 ••] and in the efficacy of sentiment to guide betting strategy [ 52 , 58 ]. There is however a need to establish the replicability of these findings, illustrated using the sentiment literature. A range of timeframes, volumes of data and implementations of sentiment have been used that give considerable scope to assess whether there is a most efficacious approach. Some appear to be more effective later in sports seasons, as sentiment and performance become calibrated [ 59 ]. Replications are needed to understand the boundary conditions on the effectiveness of sentiment in predicting the market. There is a risk that the effect size diminishes over time as bookmakers become more aware of these approaches and take sentiment into account, reducing the window for sentiment led betting opportunities. Thus there are multiple threats to the effectiveness of sentiment based approaches that warrant investigation.

Limitations

This review has categorised the studies of social media in gambling into three areas. This should be considered primarily for clarity of presentation. There are many nuances within each theme, such as the group of study (e.g. recreational vs problematic gamblers), type of gambling and research approach (e.g. quantitative vs qualitative, survey vs content analysis). Further, whilst we have identified broad similarities in findings between studies, these should be treated as indicative; the number of papers reviewed is relatively small and so most require further replication.

Conclusions

Research using social media data in gambling research can be categorised into three themes: communication, community and calculation (of sentiment). Social media has been used to study gambling advertising and the groups it reaches and the communities of gamblers that exist online and to aggregate sentiment as a betting tool. In each case, there are promising findings that in time might feed into policy, practice and recreational gambling behaviour (Tables 2 , 3 ).

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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James, R.J.E., Bradley, A. The Use of Social Media in Research on Gambling: a Systematic Review. Curr Addict Rep 8 , 235–245 (2021). https://doi.org/10.1007/s40429-021-00364-w

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Title: realm: reference resolution as language modeling.

Abstract: Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

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Mental health care is hard to find, especially for people with Medicare or Medicaid

Rhitu Chatterjee

With rates of suicide and opioid deaths rising in the past decade and children's mental health declared a national emergency , the United States faces an unprecedented mental health crisis. But access to mental health care for a significant portion of Americans — including some of the most vulnerable populations — is extremely limited, according to a new government report released Wednesday.

The report, from the Department of Health and Human Services' Office of Inspector General, finds that Medicare and Medicaid have a dire shortage of mental health care providers.

The report looked at 20 counties with people on Medicaid, traditional Medicare and Medicare Advantage plans, which together serve more than 130 million enrollees — more than 40% of the U.S. population, says Meridith Seife , the deputy regional inspector general and the lead author of the report.

Medicaid serves people on low incomes, and Medicare is mainly for people 65 years or older and those who are younger with chronic disabilities.

The report found fewer than five active mental health care providers for every 1,000 enrollees. On average, Medicare Advantage has 4.7 providers per 1,000 enrollees, whereas traditional Medicare has 2.9 providers and Medicaid has 3.1 providers for the same number of enrollees. Some counties fare even worse, with not even a single provider for every 1,000 enrollees.

"When you have so few providers available to see this many enrollees, patients start running into significant problems finding care," says Seife.

The findings are especially troubling given the level of need for mental health care in this population, she says.

"On Medicare, you have 1 in 4 Medicare enrollees who are living with a mental illness," she says. "Yet less than half of those people are receiving treatment."

Among people on Medicaid, 1 in 3 have a mental illness, and 1 in 5 have a substance use disorder. "So the need is tremendous."

The results are "scary" but "not very surprising," says Deborah Steinberg , senior health policy attorney at the nonprofit Legal Action Center. "We know that people in Medicare and Medicaid are often underserved populations, and this is especially true for mental health and substance use disorder care."

Among those individuals able to find and connect with a provider, many see their provider several times a year, according to the report. And many have to drive a long way for their appointments.

"We have roughly 1 in 4 patients that had to travel more than an hour to their appointments, and 1 in 10 had to travel more than an hour and a half each way," notes Seife. Some patients traveled two hours each way for mental health care, she says.

Mental illnesses and substance use disorders are chronic conditions that people need ongoing care for, says Steinberg. "And when they have to travel an hour, more than an hour, for an appointment throughout the year, that becomes unreasonable. It becomes untenable."

"We know that behavioral health workforce shortages are widespread," says Heather Saunders , a senior research manager on the Medicaid team at KFF, the health policy research organization. "This is across all payers, all populations, with about half of the U.S. population living in a workforce shortage."

But as the report found, that's not the whole story for Medicare and Medicaid. Only about a third of mental health care providers in the counties studied see Medicare and Medicaid patients. That means a majority of the workforce doesn't participate in these programs.

This has been well documented in Medicaid, notes Saunders. "Only a fraction" of providers in provider directories see Medicaid patients, she says. "And when they do see Medicaid patients, they often only see a few."

Lower reimbursement rates and a high administrative burden prevent more providers from participating in Medicaid and Medicare, the report notes.

"In the Medicare program, they set a physician fee rate," explains Steinberg. "Then for certain providers, which includes clinical social workers, mental health counselors and marriage and family therapists, they get reimbursed at 75% of that rate."

Medicaid reimbursements for psychiatric services are even lower when compared with Medicare , says Ellen Weber , senior vice president for health initiatives at the Legal Action Center.

"They're baking in those discriminatory standards when they are setting those rates," says Steinberg.

The new report recommends that the Centers for Medicare & Medicaid Services (CMS) take steps to increase payments to providers and lower administrative requirements. In a statement, CMS said it has responded to those recommendations within the report.

According to research by Saunders and her colleagues at KFF, many states have already started to take action on these fronts to improve participation in Medicaid.

Several have upped their payments to mental health providers. "But the scale of those increases ranged widely across states," says Saunders, "with some states limiting the increase to one provider type or one type of service, but other states having rate increases that were more across the board."

Some states have also tried to simplify and streamline paperwork, she adds. "Making it less complex, making it easier to understand," says Saunders.

But it's too soon to know whether those efforts have made a significant impact on improving access to providers.

CMS has also taken steps to address provider shortages, says Steinberg.

"CMS has tried to increase some of the reimbursement rates without actually fixing that structural problem," says Steinberg. "Trying to add a little bit here and there, but it's not enough, especially when they're only adding a percent to the total rate. It's a really small increase."

The agency has also started covering treatments and providers it didn't use to cover before.

"In 2020, Medicare started covering opioid treatment programs, which is where a lot of folks can go to get medications for their substance use disorder," says Steinberg.

And starting this year, Medicare also covers "mental health counselors, which includes addiction counselors, as well as marriage and family therapists," she adds.

While noteworthy and important, a lot more needs to be done, says Steinberg. "For example, in the substance use disorder space, a lot of addiction counselors do not have a master's degree. And that's one of their requirements to be a counselor in the Medicare program right now."

Removing those stringent requirements and adding other kinds of providers, like peer support specialists, is key to improving access. And the cost of not accessing care is high, she adds.

"Over the past two decades, [in] the older adult population, the number of overdose deaths has increased fourfold — quadrupled," says Steinberg. "So this is affecting people. It is causing deaths. It is causing people to go to the hospital. It increases [health care] costs."

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6 facts about americans and tiktok.

Increasing shares of U.S. adults are turning to the short-form video sharing platform TikTok in general and for news .

Pew Research Center conducted this analysis to better understand Americans’ use and perceptions of TikTok. The data for this analysis comes from several Center surveys conducted in 2023.

More information about the surveys and their methodologies, including the sample sizes and field dates, can be found at the links in the text.

Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder. This is the latest analysis in Pew Research Center’s ongoing investigation of the state of news, information and journalism in the digital age, a research program funded by The Pew Charitable Trusts, with generous support from the John S. and James L. Knight Foundation.

This analysis draws from several Pew Research Center reports on Americans’ use of and attitudes about social media, based on surveys conducted in 2023. For more information, read:

Americans’ Social Media Use

How u.s. adults use tiktok.

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At the same time, some Americans have concerns about the Chinese-owned platform’s approach to data privacy and its potential impact on national security. Lawmakers in the U.S. House of Representatives recently passed a bill that, if passed in the Senate and signed into law, would restrict TikTok’s ability to operate in the United States.

Here are six key facts about Americans and TikTok, drawn from Pew Research Center surveys.

A third of U.S. adults – including a majority of adults under 30 – use TikTok. Around six-in-ten U.S. adults under 30 (62%) say they use TikTok, compared with 39% of those ages 30 to 49, 24% of those 50 to 64, and 10% of those 65 and older.

In a 2023 Center survey , TikTok stood out from other platforms we asked about for the rapid growth of its user base. Just two years earlier, 21% of U.S. adults used the platform.

A majority of U.S. teens use TikTok. About six-in-ten teens ages 13 to 17 (63%) say they use the platform. More than half of teens (58%) use it daily, including 17% who say they’re on it “almost constantly.”

A higher share of teen girls than teen boys say they use TikTok almost constantly (22% vs. 12%). Hispanic teens also stand out: Around a third (32%) say they’re on TikTok almost constantly, compared with 20% of Black teens and 10% of White teens.

In fall 2023, support for a U.S. TikTok ban had declined. Around four-in-ten Americans (38%) said that they would support the U.S. government banning TikTok, down from 50% in March 2023. A slightly smaller share (27%) said they would oppose a ban, while 35% were not sure. This question was asked before the House of Representatives passed the bill that could ban the app.

Republicans and Republican-leaning independents were far more likely than Democrats and Democratic leaners to support a TikTok ban (50% vs. 29%), but support had declined across both parties since earlier in the year.

Adults under 30 were less likely to support a ban than their older counterparts. About three-in-ten adults under 30 (29%) supported a ban, compared with 36% of those ages 30 to 49, 39% of those ages 50 to 64, and 49% of those ages 65 and older.

In a separate fall 2023 survey, only 18% of U.S. teens said they supported a ban. 

A relatively small share of users produce most of TikTok’s content. About half of U.S. adult TikTok users (52%) have ever posted a video on the platform. In fact, of all the TikTok content posted by American adults, 98% of publicly accessible videos come from the most active 25% of users .

Those who have posted TikTok content are more active on the site overall. These users follow more accounts, have more followers and are more likely to have filled out an account bio.

Although younger U.S. adults are more likely to use TikTok, their posting behaviors don’t look much different from those of older age groups.

About four-in-ten U.S. TikTok users (43%) say they regularly get news there. While news consumption on other social media sites has declined or remained stagnant in recent years, the share of U.S. TikTok users who get news on the site has doubled since 2020, when 22% got news there.

Related: Social Media and News Fact Sheet

TikTok news consumers are especially likely to be:

• Young. The vast majority of U.S. adults who regularly get news on TikTok are under 50: 44% are ages 18 to 29 and 38% are 30 to 49. Just 4% of TikTok news consumers are ages 65 and older.
• Women. A majority of regular TikTok news consumers in the U.S. are women (58%), while 39% are men. These gender differences are similar to those among news consumers on Instagram and Facebook.
• Democrats. Six-in-ten regular news consumers on TikTok are Democrats or Democratic-leaning independents, while a third are Republicans or GOP leaners.
• Hispanic or Black. Three-in-ten regular TikTok news users in the U.S. are Hispanic, while 19% are Black. Both shares are higher than these groups’ share of the adult population. Around four-in-ten (39%) TikTok news consumers are White, although this group makes up 59% of U.S. adults overall .

A majority of Americans (59%) see TikTok as a major or minor threat to U.S. national security, including 29% who see the app as a major threat. Our May 2023 survey also found that opinions vary across several groups:

• About four-in-ten Republicans (41%) see TikTok as a major threat to national security, compared with 19% of Democrats.
• Older adults are more likely to see TikTok as a major threat: 46% of Americans ages 65 and older say this, compared with 13% of those ages 18 to 29.
• U.S. adults who do not use TikTok are far more likely than TikTok users to believe TikTok is a major threat (36% vs. 9%).

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .