gambling addiction research paper

Psychosocial Determinants of Gambling Addiction: A Comprehensive Review

3 Pages Posted: 6 May 2024

Christopher Goodin

Independent

Date Written: May 2, 2024

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

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

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

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

Vol. 54 No. 5 Print version: page 62

• Personality
• Video Games

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

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

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

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

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

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

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

Examining the risks

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

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

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

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

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

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

From gaming to gambling

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

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

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

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

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

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

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

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

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

Early exposure

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

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

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

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

The brain of a problem gambler

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

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

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

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

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

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

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

Betting on the game

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

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

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

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

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

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

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

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

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

Further reading

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

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

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

National Problem Gambling Helpline

Gamblers Anonymous

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• Published: 04 February 2021

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

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

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

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

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Data availability.

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

Code availability

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

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Acknowledgements

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

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Naomi Muggleton

Warwick Business School, University of Warwick, Coventry, UK

Naomi Muggleton, Paula Parpart & Neil Stewart

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Naomi Muggleton, Paula Parpart & David Leake

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Paula Parpart

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

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Correspondence to Naomi Muggleton .

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

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

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• Published: 22 October 2019

Problem gambling in adolescents: what are the psychological, social and financial consequences?

• Goran Livazović   ORCID: orcid.org/0000-0002-0277-5534 1 &
• Karlo Bojčić   ORCID: orcid.org/0000-0001-7901-8833 1  

BMC Psychiatry volume  19 , Article number:  308 ( 2019 ) Cite this article

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The paper examines the roles of sociodemographic traits, family quality and risk behaviour in adolescent problem gambling, with focus on the psychological, social and financial consequences from the socio-ecological model approach. This model emphasizes the most important risk-protective factors in the development and maintenance of problem gambling on an individual level, a relationship level, as well as a community and societal level.

The research was done using the Canadian Adolescent Gambling Inventory with a sample of 366 participants, 239 females (65.3%) using descriptive statistics and t-test, ANOVA, correlation and hierarchical regression analysis.

Males reported significantly higher gambling consequences on all scales ( p  < .001) and significantly more risk behaviour ( p  < .05). Age was significant for psychological consequences ( p  < .01), problem gambling ( p  < .01) and risk behaviour ( p  < .001) with older participants scoring higher. Students with lower school success reported significantly higher psychological consequences of gambling ( p  < .01), higher risk behaviour activity ( p  < .001) and lower family life satisfaction ( p  < .001). The psychological, financial and social consequences were positively correlated with problem gambling ( p  < .001). Age ( p  < .05), gender ( p  < .001), school success ( p  < .01) and the father’s education level ( p . < 05) were significant predictors of problem gambling, with older male adolescents who struggle academically and have lower educated fathers being at greater risk.

Conclusions

Results indicate an important relation between adolescent gambling behaviour and very serious psychological, social and financial consequences. There is a constellation of risk factors that likely place certain individuals at high risk for problem gambling.

Peer Review reports

In this article, we present a socio-ecological analysis of significant sociodemographic, family, school and gambling related factors predicting problem gambling among adolescents, as well as the most important empirical conclusions based on survey results with 366 participants, with special focus on the role of psychological, social and financial consequences, as well as risk-protective factors related to sociodemographic traits, family, school and adolescent risk behaviour. The socio-ecological model includes risk and protective factors on an individual level (health and personal traits), a relationship level (the closest social circle that contributes to the range of experience); the community level (the settings for interaction); and the societal level (social and cultural norms, as well as diverse social policies) [ 1 ]. The gambling panorama has shifted significantly during the past decades, from an initially mild type of entertainment to a hazardous addiction resulting in a number of academic, behavioural, personality, social, interpersonal, financial, criminal or mental health difficulties for children and adolescents experiencing gambling-related problems [ 2 , 3 , 4 ]. Current frameworks conceptualise problem gambling across a risk continuum [ 5 ], as the term describes gambling behaviour that results in adverse consequences for individuals, families and communities [ 6 ]. These consequences can range from impaired mental health, physical health, relationship and family dysfunction, to financial problems, employment difficulties and legal issues [ 7 ].

Research on the characteristics and risk-protective factors in gambling

Despite being illegal for minors, gambling is a common activity among adolescents. On an international level, estimates of past year gambling participation and problem gambling in youth (from 2000 to 2009) were highly variable, with rates of 0.8 to 6.0% [ 8 ], suggesting they exceed those of adults [ 9 , 11 , 12 ]. Researchers have reported evidence that a combination of biological, psychological and social factors contribute to gambling behaviour [ 13 , 14 ]. A recent meta-analyses by Dowling et al. emphasized 13 individual risk factors (alcohol use frequency, antisocial behaviours, depression, male gender, cannabis use, illicit drug use, impulsivity, number of gambling activities, problem gambling severity, sensation seeking, tobacco use, violence, under-controlled temperament), one relationship risk factor (peer antisocial behaviours), one community risk factor (poor academic performance), one individual protective factor (socio-economic status) and two relationship protective factors (parent supervision, social problems) [ 12 ]. A number of problem gambling cross-sectional studies identified female gender, adaptive coping strategies, emotional intelligence, well-being, self-monitoring, personal competence, resilience, interpersonal skills, social competence, social support and bonding, school connectedness, parental monitoring and family cohesion as protective factors [ 3 , 12 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. Integrated models of pathological gambling such as the Pathways model , introduced by Nower and Blaszczynski, suggest that a number of biological, personality, developmental, cognitive and environmental factors can be incorporated into a theoretical framework that helps explain youth gambling behaviour [ 23 ]. Derevensky et al. and Gupta and Derevensky suggest that in addition to helping youth understand the laws of probability, as well as independence of events and erroneous cognitions, attention must be paid to the underlying motivations leading to excessive adolescent pathological gambling (e.g., depressive symptomatology; somatic disorders; anxiety; attention deficits; self-regulation difficulties; academic, personal and familial problems; mood disorders; high risk-taking or poor coping skills) [ 24 , 25 ]. Researchers also emphasize risk factors such as parental/familial gambling and parental/familial approval of gambling, antisocial behaviour, deviant peers, substance abuse and school problems [ 14 , 26 , 27 , 28 , 29 , 30 ]. Problem gambling comorbid with mental health disorders has been associated with increased psychiatric symptoms; substance use problem severity; interpersonal, physical, financial and social difficulties; impulsivity and suicidality [ 31 ]. These factors play a role in the development and/or maintenance of gambling behaviour and problem gambling [ 27 ]. On the other hand, family life satisfaction (cohesion) and the quality of school affiliation, as indicators of social bonding, have been identified as protective factors in relation to youth gambling problems [ 13 , 14 , 30 ].

The present study

This research was aimed on explicating the role of sociodemographic traits (age, gender, residence in urban or rural surroundings) in profiling and predicting the average problem gambler characteristics, as well as studying the impact of traditional protective factors such as family life quality (lower/higher personal satisfaction), family structure (both parents/ other), the parents’ educational (lower/higher education) and professional status (employed/unemployed) and school related factors (school type, academic success). Special emphasis was placed on the relation between problem gambling behaviour and the psychological / social / financial consequences, as well as adolescent risk behaviour (physical violence/ alcohol/ drugs/ smoking/ risky sexual behaviour/school truancy/ deliberate destruction of property) from a predictive perspective. Based on the research aim and problems, the following hypotheses were established: (H1) sociodemographic traits have a significant impact on adolescent gambling; (H2) family life satisfaction, parental traits and school related factors represent protective factors in gambling aetiology; and (H3) problem gambling has significant psychological, social and financial consequences on adolescents.

Study design and research goal

The aim of the study was to examine the role of sociodemographic traits, family relations and parent characteristics, as well as school related factors in adolescent gambling behaviour with special focus on its psychological, social and financial consequences.

Statistical analysis

Quantitative analyses were conducted using descriptive and inferential procedures. The data was first processed for central tendency values on all measured items. The results were obtained using the t-test for independent samples and ANOVA concerning gender, age, place of residence, family structure, school success, school type, parents’ educational level and the parents’ employment status. A correlation analysis was implemented as to investigate the relation between sociodemographic traits, family life quality, risk behaviour, problem gambling and its psychological, social and financial consequences. A hierarchical regression analysis was implemented as to establish the most significant predictors for adolescent problem gambling. Due to a limited sample size and distribution, in order to perform the inferential tests, the “Age” variable was recoded into 2 groups, younger (14, 15 and 16) and older adolescents (ages 17–20). The variable “School success” was recoded into 3 groups, lower achievement (grades 1, 2 and 3 or F, D, C), average achievement (grade 4 or B) and higher achievement (grade 5 or A). The variable “Parent employment” was recoded into 2 groups, the employed and unemployed/other. The variable “Father’s education level” and “Mother’s education level” were both recoded into 2 groups, lower educated and higher educated fathers/mothers. The data were tested using the Shapiro-Wilk normality test for age ( W = .821; p=,000 ) and gender ( W = .602; p=,000 ).

Measures and data collection

A multidimensional questionnaire was constructed for the purpose of this research. The five-degree pen-paper Likert-scale survey consisted of 4 parts in Croatian language.

a) The first part encompassed questions about sociodemographic traits (gender, age, school type, parent education level, family economic well-being, urban or rural residence, academic success, study programme).

b) The second part consisted of a non-standardised 4 item scale with questions on family life quality (i.e. I get along with my parents; I can ask my parents for help; My family agrees on rules mutually ), that was computed and transformed into a new composite variable named “Family life quality”, with consequent reliability analysis showing a high Cronbach’s alpha coefficient (α = .97).

c) The third part consisted of a non-standardised 8 item scale with questions on risk behaviours, which was computed and transformed into a new composite variable named “Risk behaviour” (i.e. I use physical violence to solve problems; I smoke cigarettes; I drink alcohol; I deliberately destroy property…), with consequent reliability analysis showing a satisfactory Cronbach’s alpha coefficient (α = .70).

d) The last, fourth part of the questionnaire was based on the Canadian Adolescent Gambling Inventory [ 32 , 33 ], with 3 standardised subscales on the financial consequences, the social consequences and the psychological consequences scale. The CAGI was developed specifically for adolescents [ 34 , 35 ]. It is a paper-and-pen survey with 44-items, aimed at measuring the range and the complexity of gambling behaviour, rather than a dichotomy of either presence or absence of problem gambling, as is found in most existing adolescent and adult instruments [ 32 , 33 ]. The CAGI has 19 items that measure gambling frequency using six-point response options and time spent gambling in a typical week on 19 forms of gambling and two items to measure money and items of value lost gambling [ 33 ]. It measures four gambling-related domains of loss of control, social, psychological and financial consequences, and a fifth, Gambling Problem Severity Scale (GPSS). In our study, the social consequences scale (i.e. I missed sports practice or other activity because of gambling; I missed a family gathering because of gambling… ) had a satisfactory Cronbach’s alpha coefficient (α = .67), the psychological consequences scale (i.e. I felt guilty for losing money on gambling; I felt sad or depressed because of the amount of money I lost gambling ) had a high Cronbach’s alpha coefficient (α = .93), and the financial consequences scale (i.e. I borrowed money for gambling; I stole to obtain money for gambling or repaying debts… ) had a high Cronbach’s alpha coefficient (α = .85), while the Gambling Problem Severity Scale (GPSS), (i.e. I planned gambling; After gambling, I returned to try win back the lost money… ) also had a high Cronbach’s alpha coefficient (α = .88). In previous research studies, CAGI was found to yield satisfactory estimates of reliability, validity and classification accuracy [ 32 , 33 ]. Studies have shown it is an appropriate instrument for epidemiological studies as well as for clinical and school settings [ 32 , 33 , 34 , 35 ].

Participants

A convenience sample was recruited to reflect the characteristics of the adolescent population. The research was conducted with 366 participants, 239 female and 127 male adolescents aged 14 ( N  = 35; 9.6%), 15 ( N  = 142; 38.8%), 16 ( N  = 20; 5.5%), 17 ( N  = 25; 6.8%), 18 ( N  = 133; 36.3%), 19 ( N  = 9; 2.5%) and 20 ( N  = 2; 0.5%). A total of 98 (37.8%) participants reported living in rural areas, and 161 (62.2%) lived in urban surroundings. 144 (39.3%) participants attended gymnasiums, and 222 (60.7%) vocational schools. 292 (79.8%) reported living with both parents, 13 (3.6%) only with their fathers, and 53 (14.5%) with their mothers, while 8 (2.2%) live with someone else. The 366 participants had a 4.1 GPA with 32.2% ( N  = 118) achieving an A (excellent), 53% ( N  = 194) achieving a B (very good), 12.6% ( N  = 46) achieving a C (good) and 2.2% ( N  = 8) that failed class (F). Participants reported that 6% ( N  = 26) of their fathers completed only elementary school, 229 (62.6%) finished high school, 47 (12.8%) completed college and 63 (17.3%) had university education. Some 6% ( N  = 23) of mothers completed elementary school, 225 (61.5%) finished high school, 42 (11.5%) completed college and 76 (20.8%) had university education.

Ethics and study procedure

A paper-pen survey with high-school participants was conducted in March 2018 via group assessment during class in the Osječko-baranjska and Vukovarsko-srijemska region in Croatia. Participants were introduced to the research goal prior to responding and given instructions on the procedure, as well as basic definitions on gambling behaviour. Individuals were excluded if they were unable to understand and provide informed consent. All participants were informed and guaranteed complete anonymity, in line with the Ethical Code of Research with Children [ 36 ]. Procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. A trained research assistant was responsible for the survey implementation. The research assistant was present at all times during the survey procedure and helped with possible explanations and survey guidelines. After completion, the participants were asked to check the survey answers as to guarantee responding to all the questions. The data was processed using SPSS (v20.0.0) with descriptive and inferential statistical analysis, t-test and one-way ANOVA, correlation analysis and hierarchical regression analysis.

Descriptive and inferential analysis

Sociodemographic characteristics and gambling.

The earliest reported age of gambling initiation was 7 ( N  = 1, 0.3%), while gambling initiation was most prevalent at ages 14 ( N  = 13, 3.6%), 15 ( N  = 10, 2.7%) and 18 ( N  = 11, 3%). Out of 366 participants, 154 (42.1%) participants reported never having gambled. Our results show that playing cards, dares and challenges or skills represent the most prevalent adolescent gambling activities (Table 3 ), while 6.6% ( N  = 24) of them bet regularly in sports betting houses and 5.8% ( N  = 21) do it online, 3% ( N  = 11) regularly bet on casino slot machines and 3.6% ( N  = 13) bet on live casino games, and 6.9% ( N  = 25) bet on casino games online (Table 1 ).

Our t-test for gender differences results show that gender is a significant factor for psychological ( p  < .001), social ( p  < .001) and financial consequences ( p  < .001) of gambling, as well as problem gambling ( p  < .001) and risk behaviour ( p  < .05), with males reporting significantly higher values on all scores (Table 2 ).

Our results were not significant for the social and financial consequences of gambling in relation to age , but showed significant age differences in psychological consequences ( p  < .01), problem gambling behaviour ( p  < .01) and risk behaviour ( p  < .001) with older participants scoring higher (Table 3 ).

Our research has shown (Table 4 ) that lower school achievers report significantly higher psychological consequences of gambling in relation to more successful students ( p  < .01).

Our results show no significant differences in gambling behaviour or its consequences in relation to the school type participants attend (Table 5 ), but indicate significantly higher risk behaviour for vocational school students ( p  < .001).

Results on family and parent characteristics in gambling aetiology

The place of residence (urban/rural) was significant for the quality of family relations t(362) = 10.03; p  < .001, with rural participants reporting significantly higher family life satisfaction ( N  = 98; M = 12.07), but more risk behaviour as well, t(362) = 2.26, p  < .05. Family structure (both parents/other) was significant for family life quality t(362) = − 2.35, p  < .05, with participants from structurally deficient families reporting higher family life satisfaction ( N  = 74, M = 8.74), and for risk behaviour t(362) = − 2.50, p  < .01, as participants from structurally deficient families reported more risky behaviour ( N  = 74, M = 11.90). Our results show the mothers’ employment status was significant only for family life satisfaction, as those who had employed mothers reported more satisfaction t(362) = 3.71, p  < .001 ( N  = 245, M = 8.01). The fathers’ employment status was not significant for any of the examined variables. Both the mothers and fathers’ educational level did not show significance in relation to any of the examined variables.

Correlation and regression analysis results for problem gambling

Our results (Table 6 ) show a moderate to strong positive correlation between the psychological, financial and social consequences with problem gambling ( p  < .001). There was a positive moderate correlation between problem gambling and risk behaviour, as well ( p  < .001). Participants reported a positive weak correlation between school success and the psychological ( p  < .001) and social consequences of problem gambling ( p  < .01).

Our hierarchical regression analysis results show that age ( p  < .05), gender ( p  < .001), school success ( p  < .01) and the father’s education level ( p . < 05) are significant sociodemographic predictors of problem gambling in adolescents, with older male adolescents who struggle academically and have lower educated fathers being at greater risk (Table 7 ). Interestingly, our results show that more psychological, social and financial consequences in gambling positively predict problem gambling ( p  < .001).

This study was focused on explicating the role of sociodemographic traits (age, gender, residence in urban or rural surroundings) in profiling and predicting the average problem gambler characteristics, as well as studying the impact of traditional protective factors such as family life quality, family structure, the parents’ educational and professional status and school related factors. Special emphasis was placed on the relation between problem gambling behaviour and the psychological / social / financial consequences, as well as adolescent risk behaviour.

Adolescent gambling preferences

Derevensky and Gilbeau indicate that typical forms of teen gambling include: card playing for money (poker), sports wagering, dice and board games with family and friends; betting with peers on games of personal skill (e.g., pool, bowling, basketball); arcade or video games for money; purchasing lottery tickets; wagering at horse and dog tracks; gambling in bingo halls and card rooms; playing slot machines and table games in casinos; gambling on video lottery/poker terminals; wagering on the Internet; and placing bets with a bookmaker, recently mostly via the Internet or smartphones [ 14 ]. Our results show that playing cards, dares and challenges or skills represent the most prevalent adolescent gambling activities (Table 3 ), while 6.6% ( N  = 24) of them bet regularly in sports betting houses and 5.8% ( N  = 21) do it online, 3% ( N  = 11) regularly bet on casino slot machines and 3.6% ( N  = 13) bet on live casino games, and 6.9% ( N  = 25) bet on casino games online.

Williams [ 37 ] has reported that gambling tendencies differed from study to study and location to location, as his study has shown that games of skill were the most popular gambling activity (51%), followed by card games (47%), sports betting (27%) and dice games (24%) [ 37 ]. This is not similar to some studies reporting greater rates of lottery participation by adolescents [ 38 , 39 , 40 ]. On the other hand, our present results are consistent with other studies in Alberta which found that adolescents favoured card games, games of skills, and sports betting as compared to other gambling activities [ 37 , 41 , 42 ]. Even though 23% of the Alberta sample reported one or more symptoms of problem gambling, only 2.5% met actual criteria for problem gambling [ 37 ]. This prevalence rate is lower than found in most other studies [ 40 , 43 , 44 , 45 , 46 , 47 , 48 ]. It is also lower than the Shaffer et al. meta-analysis which reported prevalence rates of adolescent problem gambling to be 3.9% [ 49 ].

The role of sociodemographic traits in adolescent problem gambling

Our first hypothesis (H1) assumed that sociodemographic traits have a significant impact on adolescent gambling. Our results show distinct gender differences (Table 2 ), with males reporting significantly higher gambling consequences on all scales ( p  < .001), but risk behaviour, as well ( p  < .05). Previous research established a relationship between male gender and gambling or problem gambling [ 42 , 44 , 46 , 48 , 50 , 51 , 52 , 53 ]. Sheela et al. found gender was significantly associated with adolescents’ gambling behaviour, with males having nearly three times the odds of being gamblers compared to girls [ 54 ]. A study by Di Nicola et al. has shown higher prevalence in gambling frequency among males, and significant gender differences in maladaptive gambling behaviour, with males scoring higher on the SOGS–RA score ( South Oaks Gambling Screen: Revised for adolescents ) [ 55 ]. Similarly, Elton-Marshall, Leatherdale and Turner have shown that online and land-based gambling was significantly more popular among males [ 56 ]. Mutti-Packer et al. indicated that gender was a significant predictor of baseline levels of gambling, as females reported lower initial levels of gambling problems [ 57 ], while studies by Williams [ 37 ] and Gonzalez-Roz et al. [ 58 ] showed no statistically significant differences between gender and gambling behaviour. However, a recent meta-analysis reported male gender to be among the strongest risk factors for problem gambling [ 12 ], which was confirmed in our present study results.

In their investigation on the role of age, Sheela et al. did not find significant associations between age and adolescent gambling behaviour [ 54 ], but an earlier age of first gambling activity was established as a risk factor [ 59 ]. Several research studies found no significant age differences in psychological consequences of gambling, social consequences of gambling, financial consequences of gambling and problem gambling behaviour, as well [ 30 , 55 ], while McBride and Derevensky showed that a greater proportion of non-gamblers were under the age of 18 years, whereas a significantly greater proportion of social gamblers were aged 21–24 [ 60 ]. In a study conducted by Kristiansen and Jensen, the proportions of at-risk gamblers and problem gamblers were significantly higher among older age groups [ 61 ]. Kristiansen and Jensen reported a weak, non-significant relationship between age and the SOGS-RA score, with older respondents reporting more gambling problems [ 61 ]. This is consistent with our findings and could imply that gambling behaviour is related to personal maturation, more agency and social emancipation, but also indicate that preventive actions need to be targeted at specific age groups. As evidence indicates, wagering something of value on an uncertain event often begins as early as grade school [ 44 ], with age 11 being the average age of onset found in a couple of major studies [ 25 , 47 ]. Still, our results show a worrying age decline as the earliest reported age of gambling initiation was 7, while it was most prevalent at ages 14, 15 and 18, implying a need for an earlier onset and optimization of efficient prevention programs in schools and community activities.

Family life, parental traits and school related factors in problem gambling

The second hypothesis (H2) assumed that family life satisfaction, parental traits and school related factors represent protective factors in gambling aetiology. Our research has shown (Table  4 ) that lower school achievers report significantly higher psychological consequences of gambling in relation to better students ( p  < .01). A large amount of international research has found that problem gamblers tend to be concentrated among those lacking college education, and who have dropped out of high school, while several studies have demonstrated correlations between higher spending on gambling and lower levels of education [ 62 ]. Gambling during adolescence has been linked with psychiatric, social, and substance misuse problems in adulthood [ 63 ]. Both recreational and problem gambling have been associated with adverse social functioning and mental health in adolescence including poor school performance and difficulties with aggression and mood [ 53 , 64 , 65 ]. Foster et al. also reported that students who gambled on school grounds had poorer academic performance [ 66 ], while a study by Gonzalez-Roz et al. showed no statistically significant differences between school success and gambling behaviour [ 58 ]. Our results also demonstrate significantly higher risk behaviour activity ( p  < .001) and significantly lower family life satisfaction among participants who report the lowest school achievement ( p  < .001), so the intensity of psychological consequences could also stem and be related to a feeling of rejection due to problems in family, school failure and risk behaviour participation.

Our results showed no significant differences in gambling behaviour or its consequences in relation to the school type participants attended, but indicated significantly higher risk behaviour for vocational school students. These results are in line with similar studies which have shown that vocational school students have significantly higher risk behaviour prevalence than gymnasium students, with no significant school type differences in gambling behaviour [ 53 , 67 ].

The place of residence (urban/rural) was significant for the quality of family relations, with rural participants reporting both significantly higher family life satisfaction, but more risk behaviour as well. Family structure (both parents/other) was significant for both family life quality and risk behaviour, with participants from structurally deficient families reporting higher family life satisfaction and riskier behaviour. Similarly, Foster et al. also did not establish significant gambling differences in relation to family structure [ 66 ], but Canale et al. reported that two family characteristics increased adolescent gambling – living with unrelated others or a single parent, and in poor families [ 68 ]. Our results show the mothers’ employment status was significant only for family life satisfaction, with participants who had employed mothers reporting more satisfaction. The fathers’ employment status was not significant for any of the examined variables. Both the mothers and fathers’ educational level did not show significance in relation to any of the examined variables. Even though our study did not investigate parental gambling behaviours, previous research has positively correlated parental gambling and adolescent gambling, with children of problem gamblers tending to gamble earlier than their peers [ 10 , 44 , 52 , 69 , 70 ], implying the need to educate parents on family risk factors for problem gambling.

The consequences of adolescent problem gambling

The third hypothesis (H3) assumed gambling had significant psychological, social and financial consequences on adolescents. Our results established a positive correlation between the psychological, financial and social consequences with problem gambling (Table 6 ), consistent with previous research on emotional problems and gambling, for example a positive relation between time spent gambling and depression [ 52 , 71 ], also found in a study by Williams [ 37 ] and Rossen et al. [ 72 ], who reported that students with unhealthy gambling practices reported significantly more mental health issues and other addictions/risky behaviours [ 72 ]. Among the high-risk behaviours, adolescents that smoke, consume alcohol and participate in physical fights had significantly higher odds for gambling addiction. In a study by Castrén et al. both smoking and drinking for intoxication were significantly associated with at-risk and problem gambling compared with non-smokers and respondents who had not been drinking for intoxication [ 73 ]. Gambling frequency has been found to be highly associated with other forms of antisocial activity, for example, delinquency [ 74 ]. Additionally, Williams [ 37 ] points to a number of studies that reported a positive relationship between risk-taking and gambling, which was confirmed by a meta-analysis by Dowling et al. [ 12 ].

Predictors of adolescent problem gambling

Our hierarchical regression analysis results show that age, gender, school success and the father’s education level all significantly predicted problem gambling in adolescents, with older male adolescents who struggle academically and have lower educated fathers being at greater risk (Table 7 ). Our results also established that more intense psychological, social and financial consequences in gambling positively predicted problem gambling. For example, Gupta and Derevensky [ 9 ] reported that excessive gambling in boys caused emotion-focused coping strategies, such as anger, frustration or anxiety during negative events. Similarly, Williams found a positive relationship between time spent gambling and depression [ 37 ]. All our regression results are in line with Rossen et al., who found that males are disproportionately at risk of problem gambling [ 72 ]. The same study found that students with unhealthy gambling practices were significantly more likely to report co-existing mental health issues (e.g. depression and suicide attempts) and other addictions/risky behaviours (e.g. use of alcohol and weekly cigarette smoking). For example, Williams found that time spent gambling was a significant predictor for higher levels of impulsivity, possessing more positive attitudes towards gambling, having been in trouble with the police, having suffered from depression, possessing less knowledge about gambling and having greater cognitive errors [ 37 ]. While Ste-Marie, Gupta and Derevensky found that gamblers with the highest scores on state and trait anxiety, as well as for social stress, were likely to meet the criteria for probable pathological gambling [ 28 ], a recent meta-analysis on problem gambling revealed that aggression, anxiety symptoms, attention problems, a big early gambling win, dispositional attention, psychological distress (including internalising symptoms), religious attendance and suicidal ideation were not significantly associated with subsequent problem gambling, so results are inconclusive [ 12 ]. Even though our results did not show a significant role of family structure in gambling, a study conducted by Allami et al. has shown that, at age of 16, parent–child connectedness and higher parental involvement significantly predicted fewer gambling problems, while peer connectedness may have an effect on problem gambling, but that effect likely depends on peer gambling behaviour [ 75 ]. A recent meta-analysis on gambling established parent supervision and socio-economic status as significantly negatively associated with subsequent problem gambling [ 12 ]. Our regression analysis did not establish family life quality or parent employment as significant predictors, while Jackson et al. found that participating in gambling activities was associated with parental employment [ 76 ]. They found no significant associations between gambling and family structure, nor between gambling and parental education. The same study also found a positive association between gambling involvement and depressive symptomology, deliberate self-harm and arguments with others; as it did between gambling participation and engagement in substance use and antisocial behaviours. These behaviours, with the exception of smoking, were significant predictors of greater involvement in gambling [ 76 ]. Interestingly, a study by Williams established a positive attitude toward gambling as the most consistent predictor of gambling behaviour, as well as problem gambling [ 37 ]. Among other established predictors, Williams found that larger amounts of money won while gambling, positive attitudes towards gambling, impulsivity, more gambling-related cognitive errors, greater risk-taking and less knowledge about gambling, were the variables that significantly contributed to the prediction of higher gambling frequency in order of predictability [ 37 ]. Similarly, having won a large sum of money gambling was the best predictor of increased time spent gambling. In addition, spending more time gambling was also associated with higher levels of impulsivity, possessing more positive attitudes towards gambling, having been in trouble with the police, having suffered from depression, possessing less knowledge about gambling and having greater cognitive errors, in descending order of predictability [ 37 ]. Reductions in positive attitudes towards gambling, prior trouble with the police and increased knowledge were the only three variables that significantly predicted decreased gambling frequency [ 37 ]. Surprisingly, even though we did not establish predictive relations between risky behaviour and problem gambling, it has been found to be highly associated with other forms of antisocial activity. For example, delinquency has been found to be positively related to both gambling frequency and problem gambling [ 73 ].

Implications for research and practice

Problem gambling is a complex research area, so findings are sometimes contradictory even though such behaviour is proven to disrupt personal, family, financial, professional and social relations [ 62 ]. Our study was focused on explicating the role of sociodemographic traits, family relations quality and risk behaviour in adolescent problem gambling. Such quantitative studies on sociodemographic factors related to personal characteristics, family life satisfaction and school surroundings are relatively scarce. While many adolescents gamble occasionally and don’t experience significant problems, studies suggest they constitute a vulnerable population for gambling problems, especially with the onset of online gambling. Low numbers of treatment seeking adolescent problem gamblers may be related to the fact that they do not consider disruptive gambling as problematic and underestimate its impact. Treatment-wise, high comorbidity with other disorders may obscure problem gambling, as other problems get more clinical attention, while the heterogeneity within a relatively narrow age range implies the need for divesified approaches [ 77 ]. Our results indicate 3–7% of adolescents who regularly participate in serious gambling activities, in line with a study by Volberg et al., who reported 2–8% of adolescents having serious gambling problems, with another 10–15% being at-risk for the development of a gambling problem, especially young adults ages 18–25 [ 78 ]. Our regression analysis showed that the average at-risk individuals are older male adolescents who struggle academically, have lower educated fathers, attend vocational schools and report low family life satisfaction. It is interesting we established that problem gambling was positively predicted by psychological, social and financial consequences, in accordance with studies that established high comorbidity of problem gambling with psychological conditions [ 79 ], but their relation remains unclear as to the nature or relationship of the three variables in the prevention or inhibition of gambling. Adolescent problem gamblers use less task-focused coping and more avoidance coping strategies than non-gamblers, while male excessive gamblers demonstrate more emotion-focused coping strategies, such as anger, frustration or anxiety during negative events [ 80 ]. Therefore, gambling behaviour is closely related to feelings of serious social, psychological and financial consequences for adolescents. Similar to adults with a gambling disorder, adolescents report having a preoccupation with gambling; repeated attempts at recouping losses; increasing wagers to reach a physiological level of excitement; lying to others about gambling; with anxiety and depression when trying to reduce their gambling. A considerable number of adolescents report gambling as a coping mechanism to psychologically escape daily problems (parental, peer, and school-related) and other mental health issues [ 13 , 81 ]. Surprisingly, family life quality, parent employment and risky behaviour were not established as significant predictors for problem gambling in our study, so the role of parents and family should be further investigated. Lussier et al. examined the concept of resilience for youth gambling problems and other adolescent high-risk behaviours and suggested that family cohesion was an important element in adolescent resilience [ 17 ]. Exposure to an object of addiction at a young age or exposure to a parent’s addiction could both increase the likelihood of developing an addiction. One recent study found that children of pathological gamblers were four times more likely to develop the disorder, so it is important that health professionals, teachers and other experts understand the family background [ 82 ]. Recent findings suggest that some of the early factors associated with the onset of problem gambling in cross-sectional studies have not been identified in subsequent longitudinal studies, which suggests that these factors may be, in fact, consequences of problem gambling or co-exist because they share common causes [ 12 ]. Therefore, longitudinal studies shift the policy focus from elements that co-occur with problem gambling in youth at a certain cross-sectional point in time to predictive factors of gambling at a future time-point, including adulthood. Adolescent gambling behaviour should be viewed on a continuum, from non-gambling to social or occasional and recreational gambling to at-risk gambling, up to problem or pathological, compulsive or disordered gambling [ 14 ]. In conclusion, clinical and research evidence suggests that efficient prevention strategies have an impact through providing facts about gambling which improve knowledge and significantly reduce misconceptions, resulting in more realistic attitudes towards gambling [ 83 , 84 ]. Still, their effectiveness needs further investigation, thus emphasising the need for future efficient preventive social and educational policy.

Limitations

Several limitations of the current study should be noted. The research sample was small; therefore, conclusions of a larger scale and results generalization are out of the scope of this study. In addition, the extent of underreporting or over-reporting of behaviours cannot be determined, although the survey questions demonstrate good intercorrelational reliability. The “Gambling social consequences” scale demonstrated lower reliability, even though standardised survey instruments were implemented. Sociodemographic traits, family relations, risk behaviour and problem gambling experiences were self-reported, but previous studies have shown these measures to be valid [ 85 , 86 ]. Despite these limitations, a strength of the study was the use of instruments with reliable psychometric properties to measure adolescent gambling behaviour and its social, psychological and financial consequences.

The results of our study indicate that there is a distinct role of socio-demographic characteristics in the aetiology of adolescent problem gambling, that are mostly related to gender, age, academic achievement and the father’s educational level. What begins as an exciting benign form of entertainment for most, could result in serious problems for an identifiable group of young people. It should be noticed that our findings clearly indicate an important relation between adolescent gambling behaviour and very serious psychological, social and financial consequences, as well as risk behaviour. There is a constellation of factors that likely place certain individuals at high risk for problem gambling. The increasing awareness that the aetiology underlying gambling problems is not universal, that risk factors may be different for individuals, and that there are a number of distinct pathways which could lead to pathological or disordered gambling, pose new important questions for researchers and practitioners on the structure and focus of preventive activities.

Availability of data and materials

All research data is available upon demand, and was submitted to the Editorial Office in the publication process. Requests for data and materials should be addressed to the corresponding author.

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The first author, GL, envisioned the research framework and was involved in writing the theoretical part of the paper, conducted the empirical data analysis and discussion on the results of the study. The second author, KB, was involved with the practical research data gathering and survey implementation, organised the field research and was involved with the discussion on the results of the study. Both authors have read and approved the final manuscript.

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Correspondence to Goran Livazović .

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The research was conducted according to The Ethical Standards for Research with Children (2003) and the standards of the Ethical Committee for Research of the Faculty of Humanities and Social Sciences in Osijek. Informed written consent was obtained from all individual participants included in the study, as well as their parents and school institutions. All supporting data can be accessed via e-mail, available on demand.

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Livazović, G., Bojčić, K. Problem gambling in adolescents: what are the psychological, social and financial consequences?. BMC Psychiatry 19 , 308 (2019). https://doi.org/10.1186/s12888-019-2293-2

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• Adolescents
• Risk behaviour
• Sociodemographic traits

BMC Psychiatry

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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]

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

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Gambling: Exploring the Role of Gambling Motives, Attachment and Addictive Behaviours Among Adolescents and Young Women

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• Published: 17 May 2022
• Volume 39 , pages 183–201, ( 2023 )

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• L. Macía 1 ,
• A. Estévez   ORCID: orcid.org/0000-0003-0314-7086 1 &
• P. Jáuregui 1  

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There is a growing body of research that seeks to understand the aetiology, consequences and risk factors associated with addictive behaviours in youths. However, research examining the specific profile of adolescent females is very limited. Therefore, the objectives of the present study were, firstly, to explore the differences between attachment, gambling motives (social enhancement and coping), positive and negative affect, and addictive behaviours (gambling, drugs, spending, alcohol and video games) in female adolescents with and without risk of gambling problems. Secondly, the relationships between attachment, gambling motives, positive and negative affect and addictive behaviours were analysed in the subsample of female adolescents with problem gambling Thirdly, we examine the predictive role of positive and negative affect, gambling motives, and attachment in the aforementioned addictive behaviours. The sample was composed of 351 adolescents and young women, of which 312 had no risk of gambling and 39 had gambling problems. The results obtained revealed higher scores in drugs, spending, maternal attachment, and all gambling motives in the group of gambling problems. Likewise, analyses showed that the relevance of the predictor variables (attachment, gambling motives, and affect) varied according to the addiction that was taken as a reference point (i.e., gambling, drugs, spending, alcohol and video games).Consequently, the identification of the possible vulnerability factors for each addiction could be useful in the design of prevention and treatment approaches. In addition, the need for integrated and holistic health- and social- care programmes are suggested in terms of sex and age.

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Introduction

Gambling Disorder (GD) is characterised by persistent and recurrent problematic gambling behaviour that leads to clinically significant damage (e.g. loss of job, deterioration of important personal relationships, indebtedness, anhedonia, emotional disorders, loss of control, sleep disorders, etc.) (American Psychiatric Association [APA], 2013 ). Previous studies in the area have shown that an early onset of gambling is associated with a greater predisposition towards the development of a GD in later life, as well as with a greater severity both of the problem itself and its subsequent negative consequences (Derevensky et al., 2003 ; Jiménez-Murcia et al., 2010 ; Sharman et al., 2019 ). A systematic review conducted by Calado et al., ( 2017a , b ) indicates that problem gambling in the adolescent population ranges from 0.2 to 12.3%. However, research addressing this issue, on one hand, is not based on a homogeneous tool to measure gambling severity and, on the other hand, is based on samples of gamblers, not on the general population (e.g., from non-participation to gambling disorder). In addition, most of the research has focused on exploring the profile of the male gambler (Stark et al., 2012 ). Consequently, studies have yielded contradictory and limited results (Dowling, Merkouris, et al., 2017 ; Dowling, Shandley, et al., 2017 ). In fact, research is increasingly showing that the pattern of gambling may differ according to gender (Abbott et al., 2018 ; Sancho et al., 2019 ).

Gambling motives are a capital aetiological factor of GD, explaining an individual’s vulnerability to the development of addictive behaviour (Huic et al., 2017 ; Jáuregui et al., 2020 ). According to the model proposed by Stewart and Zack ( 2008 ), there are three main reasons for gambling: (a) enhancement motives (ENH), (b) coping motives (COP), and (c) social motives (SOC). Several studies have shown that adult women are more likely to use gambling as a coping mechanism to deal with worries and negative emotions (Lelonek-Kuleta, 2021 ). In the adult population, COP and ENH motives appear to be the main predictors of severe problem-gambling behaviour, whereas social motives seem to be related to non-pathological gambling (Barrault et al., 2019 ; Lambe et al., 2015 ).

In contrast to sex differences in gambling motivation in adults, studies on adolescents seem to show different patterns. In this vein, young people at risk or with problem gambling score higher on all gambling motives, including social motives, than those who do not have a gambling problem (Grande-Gosende et al., 2019 ). In fact, excitement, fun or socialisation are some of the main reasons why adolescents engage in gambling (Jáuregui & Estévez, 2019 ; Jáuregui et al., 2020 ; Neighbors et al., 2002 ). Currently, we have not found enough studies investigating sex differences in gambling motivation in adolescence. Nevertheless, impulsivity, especially affective impulsivity (i.e., the tendency to act rashly under the influence of intense positive and/or negative affective states), seems to be a transdiagnostic factor for addictions in this age group (Del Prete et al., 2017 ; Navas et al., 2017 ). In this line, studies suggest that the younger one is, the higher is one’s level of impulsivity, the greater the tendency towards immediate rewards and higher the difficulty in foreseeing the consequences of risky behaviour; which has been associated with a neurobiological susceptibility that is characteristic of youth, who have certain brain areas that are still developing (Estévez et al., 2015 ; Lockwood et al., 2017 ).

On the other hand, adolescent males and females with severe gambling problems show a remarkably similar prevalence of comorbid mental health problems, highlighting mood and anxiety disorders, substance use disorders, and the frequency and severity of gambling, compared to adolescents without such problems (Ellenbogen et al., 2007 ; Estévez et al., 2020 ; Estévez et al., 2020 ; Estévez et al., 2020 ; Estévez, Jáuregui, et al., 2020 ; Jáuregui et al., 2020 ). Regarding the profile of adolescent females who suffer from gambling addiction, previous studies indicate that, compared to the general female population, they have significantly higher rates of gambling, anxious-depressive symptoms, eating disorders, compulsive buying, and alcohol and drug use (Afifi et al., 2010 ; Boughton & Falenchuk, 2007 ). Moreover, although women tend to initiate addictive behaviours later than do men, the progression towards the development of dependence is quicker for them (Fonseca et al., 2021 ). This phenomenon, known as "telescoping", seems to occur both in substance- and non-substance-related addictions (e.g., alcohol, cannabis, cocaine, opioids, gambling, etc.), and it could explain why women enter treatment with more severe behavioural, family, psychological, and social problems (Slutske et al., 2015 ). However, differences in rates of addictive behaviours have been more closely related to gender/cultural environment than to biological factors (Fonseca et al., 2021 ). Therefore, as addictive behaviours become more feminised and normalised among the female population, we observe an increasing prevalence and an earlier age of onset of those behaviours, highlighting GD, video game addiction, or tobacco and alcohol abuse, among others (Ait-Daoud et al., 2017 ; Erol & Karpyak, 2015 ; Estévez et al., 2020b , 2020c , 2020d ; Estévez, Jáuregui, et al., 2020 ; Lopez-Fernandez et al., 2019 ).

Among the risk and protective factors for addiction in adolescence, the relationship with the family and peers is particularly important (Estévez et al., 2019 ). In this sense, authors such as Estévez et al. ( 2017 ) propose that addictive behaviours in adolescence could be linked to a need for relational satisfaction. In fact, dependency behaviours have been considered by some authors as an attachment disorder, finding negative relationships between secure attachment style and acting-out behaviours in adolescents (Calado et al., 2017a , 2017b ; Schimmenti et al., 2012 ). Teng et al. ( 2020 ) report that attachment to parents and peers is negatively associated with involvement in addictive problem behaviours (Estévez et al., 2020 ; Estévez et al., 2020 ; Estévez et al., 2020 ; Estévez, Jáuregui, et al., 2020 ; Monacis et al., 2017 ). In fact, an insecure attachment style has been found to be a predictor of Internet abuse, Internet gambling disorder, GD, or alcohol and drug abuse disorders in young people (Estévez et al., 2017 ; Schindler, 2019 ; Teng et al., 2020 ). However, the involvement of paternal and maternal attachment according to the addictive behaviour and gender has been unexplored in this age group.

In this sense, attachment constitutes a fundamental factor in determining the abilities, resources and skills an individual will develop in order to cope with everyday life (Mikulincer, & Shaver, 2007 ). However, not all attachment interactions are resolved in a functional way. Sometimes the attachment figure is emotionally unavailable or the infant may judge the attachment figure to be unavailable for her needs. Due to the failure to seek security in the caregiving figure, the infant's regulatory system may develop secondary regulatory strategies. In addictions, it has been suggested the self-medication theory which formulates addictive behaviours as a way to alleviate and regulate mood states, even as a way of emotional avoidance (Khantzian, 1985 ). There is evidence pointing out the expectation among people with GD of alleviating negative mood states and generating positive ones through gambling.

In summary, while there is a growing body of research examining the aetiology, prevalence, and risk factors associated with adolescent gambling disorder, research examining sex differences, that is, the profile of young women and adolescents with problem gambling, is very limited. Therefore, the aim of this study is, firstly, to explore the differences between groups of problem and non-problem gamblers in attachment, gambling motives (social, enhancement and coping), positive and negative affect, and addictive behaviours (gambling, drugs, spending, alcohol and video games). Secondly, we wish to analyse the relationship between these variables in the subsample of at-risk and possible problem gamblers. Thirdly, we intend to examine the predictive role of positive and negative affect, gambling motives, and attachment in the aforementioned addictive behaviours.

Firstly, young women and adolescents with gambling problems are expected to have higher scores on gambling, gambling motives (ENH, COP, and SOC), positive and negative affectivity, attachment difficulties and other addictive behaviours than those without gambling problems. Secondly, gambling behaviour is expected to be positively related to other comorbid addictive behaviours, gambling motives (ENH, COP, and SOC), and positive and negative affect; whereas a negative relationship is expected between addictive behaviours and attachment relationships. Finally, the role of the predictor variables (affectivity, gambling motives and attachment) is also expected to vary according to addiction type (gambling, video games, spending, drugs and alcohol) in the subsample of adolescents and young women with risk and problem gambling.

Participants

A convenience sample was recruited. The study sample included 351 young women and adolescents aged between 12 and 26 years, recruited from educational centers (that is, secondary schools and professional training centers), and treatment centers for pathological gambling associated with FEJAR (Spanish Federation of Rehabilitated Gamblers). All the educational centers from Basque Country (Spain) that comprised participants from the age range of the study were contacted, as well as all the treatment centers for pathological gambling associated with FEJAR (Spain). Finally, 10 educational centers from Basque Country as well as from other autonomous communities from Spain that contacted the research team and showed interest in the study, as well as participants recruited from FEJAR centers, participated in the study.

In this study, participants were divided into two subsamples based on their scores on the South Oaks Gambling Screen Revised for Adolescents (SOGS-RA; Winters et al., 1993 ): (a) the group of problem gamblers included women at risk or presenting possible problem gambling from both the association centres and the general sample, i.e. subclinical sample (scored of 2 or more), and (b) the group of women without gambling problems from the general sample (scored between 0 and 2, including non-gamblers).

Gambling Disorder

South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA; Winters et al., 1993 ). Adapted to Spanish by Secades and Villa ( 1998 ). This instrument is composed of 12 items describing gambling behaviour over the last twelve months. All items have a dichotomous response option (i.e., " yes " or " no "), with the exception of Item 1, which has four response options (e.g. “ Has your betting money ever caused any problems for you such as arguments with family and friends, or problems at school or work? or Have you ever gambled more than you had planned to?” ) . The SOGS-RA is interpreted as follows: 0–1, no problem gambling; 2–3, risk of problem gambling; 4 or more, possible gambling disorder. The criteria used by the SOGS-RA for the detection of gambling problems are similar to the SOGS designed for adults (Lesieur & Blume, 1987) but the risk category combines current symptoms with those indicating the development of a later gambling problem. The original instrument has adequate psychometric properties (Cronbach's alpha = 0.81). In the present study, Cronbach's alpha was 0.91.

Drugs, Alcohol, Video Games, and Spending

MULTICAGE CAD-4 (Pedrero-Pérez et al., 2007 ). This instrument is designed to detect addictive behaviours, both substance and behavioural, as well as the social problems associated with them. The scale is composed of 32 items, which are grouped into eight types of addictive behaviours: (1) alcohol use disorder (2) drug use disorder; (3) gambling disorder; (4) video game addiction; (5) Internet abuse; (6) compulsive spending; (7) sex addiction; (8) eating disorders. In this study, alcohol, drugs, video games and spending were assessed. The items have a dichotomous response option (i.e., "yes" or "no"). Each addictive behaviour is scored on the basis of four questions replicating the CAGE questionnaire (Hayfield et al., 1974 ), including self-perception of the problem, perception of close family and peer relationships, feelings of guilt and withdrawal, and impulse control-symptoms. Interpretation of the scores is as follows: 0–1, no addiction problem; 2, risk of addiction; 3, likely to present addiction; 4, addiction problem. Internal consistency was satisfactory (Cronbach's α = 0.86 for the overall instrument, and above 0.70 for each subscale). The instrument detects between 90 and 100% of already diagnosed cases. In this study, Cronbach's alpha ranged between 0.60 and 0.90.

The Inventory of Parent and Peer Attachment (IPPA; Armsden & Greenberg, 1987 ). Adapted to Spanish by Gallarín and Alonso-Arbiol ( 2013 ). This is a three-part self-report questionnaire that assesses adolescents' attachment to mother, father and peers. Attachment to each specific figure (e.g., mother) is assessed through a 16-item subscale with a 5-point Likert-type response (1 =  never ; 5 =  always ). The aggregate score for each person represents the overall strength of attachment, where high scores indicate high-quality attachment, and low scores indicate insecure attachment bonds. The original instrument has adequate psychometric properties for each specific figure as well as for the final score. The Spanish version shows optimal levels of Cronbach's alpha coefficients for mother (α = 0.87), father (α = 0.88) and peers (α = 0.93). In the present study, Cronbach's alpha coefficients ranged between 0.92 and 0.97.

Gambling Motives

Gambling Motives Questionnaire (GMQ; Stewart & Zack, 2008 ). Spanish adaptation by Jáuregui et al. ( 2018 ). The questionnaire assesses 15 reasons why people gamble, divided into three subscales of five items each: (1) Enhancement motives (ENH): refers to internal positive reinforcement to increase positive emotions (e.g., To get an "intense" feeling ); (2) Coping motives (COP): alludes to internal negative reinforcement, aiming to avoid or ameliorate negative emotions (e.g., " Because it helps you when you feel nervous or depressed "); (3) Social motives (SOC): refers to external positive reinforcement, mainly social affiliation (e.g., " Because it's what most of your friends do when they get together "). Each item is an adaptation of the Drinking Motive Questionnaire (Cooper et al., 1992 ). The GMQ items have a 4-point Likert-type response ranging from 1 ( never/almost never ) to 4 ( almost always ). All subscales showed good internal consistency (α > 0.80). In the present study, Cronbach's alpha ranged from 0.82 to 0.90.

Positive and Negative Affect Schedule (PANAS; Watson et al., 1988 ), adapted to Spanish by Sandín et al. ( 1999 ). The scale is composed of 20 words describing different emotions and feelings, divided into two main subscales with 10 items each: Positive Affect (PA) and Negative Affect (NA). The respondent is asked to indicate whether they experienced any of the described emotions/affections now or in the last two weeks on a five-point Likert scale (1 =  not at all/very slightly to 5 =  very much ). The total score for each subscale is the sum of the 10 items that make up the subscale, so the scores for each subscale range from 10 to 50 points. Higher scores indicate a greater presence of the specific affect. Both subscales, positive and negative affect, have good psychometric indices (α = 0.85 and 0.89, respectively). In this study, internal consistency was high now and at two weeks (α = 0.87).

Both paper-and-pencil and online questionnaires were administered. The vast majority of participants made paper-and-pencil assessments, whereas a minority completed the survey through a dedicated online link under the supervision of their teacher. The questionnaire included general information about the main goals of the study. It was also made clear that there were no right or wrong responses and that participants could email the research team if they wanted further information about the study. To be eligible, participants were requested to give informed consent. Parental consent was sought for those younger than 18 years of age.

Confidentiality, anonymity, and voluntary participation were ensured for all participants. Researchers’ contact information was provided for those who required it. The participants did not receive any compensation for participating. The Institutional Review Board approved the study (ETK-26/17-18). The schools participating in the study have received feedback about the results of the research.

Data Analyses

Firstly, the mean differences between the group of problem gamblers and non-gamblers were analysed using Student's t -test. The effect size of the significant differences was also analysed using Cohen's d (1992), whose parameters establish that an effect size below 0.20 is considered small, around 0.50 medium, and above 0.80 large. Secondly, Pearson's bivariate correlation analyses were carried out between the variables in the study. Thirdly, hierarchical regressions were carried out to analyse the predictive role of positive and negative affect, gambling motives and attachment in gambling, drugs, alcohol, video games and spending. For this purpose, each of the addictive behaviours was analysed using a model in which positive and negative affect was introduced in a first step, positive and negative affect and gambling motives in a second step, and positive and negative affect, gambling motives and attachment in a third step. For the second and third objectives, only the sub-sample of adolescents and young women with risk and problem gambling were considered. Due to the small size of that sample, the results were also replicated with the total sample to check the risk of Type 1 error (rejecting the null hypothesis when it is true).

Firstly, mean differences in attachment, gambling motives, positive and negative affect and the aforementioned addictive behaviours were analysed between possible problem gamblers and non-gamblers using Student’s t- test (Table 1 ). The results showed that female at- risk or with problematic gambling scored higher on gambling, drugs, compulsive spending, maternal attachment, and gambling motives (enhancement, social and coping motives). When analysing the effect sizes for the variables where significant differences were found, they were observed to be large for gambling, spending and enhancement motives, and medium for drugs, mother attachment, and social and coping motives.

Secondly, correlations between the study variables were analysed (Table 2 ). Gambling correlated positively with drugs, spending, negative affect (now), and enhancement and coping motives, and correlated negatively with maternal attachment. Drugs correlated positively with video games, alcohol, negative affect (now and past two weeks), and negatively with maternal and paternal attachment. Spending correlated positively with video games, alcohol, and enhancement and coping motives, and negatively with maternal and paternal attachment. Video games correlated positively with negative affect (last two weeks) and negatively with maternal attachment. Finally, alcohol abuse correlated positively with negative affect (last two weeks) and negatively with paternal attachment. The same results were obtained after replicating the results with the total sample, reducing the risk of Type I error (rejection of null hypothesis when it is true).

Third, the predictive role of positive and negative affect, gambling motives and attachment in gambling, drugs, spending, alcohol, and video games was analysed using hierarchical regressions (Tables 3 , 4 , 5 , 6 ). Hierarchical regression models were conducted, in which the first step included positive and negative affect; the second step included positive and negative affect and gambling motives; and the third step included positive and negative affect, gambling motives, and attachment. In the case of gambling, it was found fo be associated with social and coping motives. The first step explained a 3% of the variance, the second step explained a 34% of the variance, and the third step explained a 37% of the variance. The change in R 2 was significant in the second step. In the case of drug abuse, it was associated with paternal attachment. The first step explained a 5% of the variance, the second step explained a 8% of the variance, and the third step explained a 16% of the variance. The change in R 2 was significant in the third step. In the case of spending, it was associated with enhancement, social and coping motives. The first step explained a 1% of the variance, the second step explained a 22% of the variance, and the third step explained a 24% of the variance. The change in R 2 was significant in the second step. In the case of alcohol, it was associated with enhancement motives, father attachment, and peer attachment. The first step explained a 3% of the variance, the second step explained a 7% of the variance, and the third step explained a 14% of the variance. The change in R 2 was significant in the third step. In the case of video games, none of the models was significant. The same results were obtained after replicating the results with the total sample, reducing the risk of Type I error (rejection of null hypothesis when it is true).

The first aim of this study was to analyse the differences between girls with risk and gambling problems, and the general female population without gambling problems, in addictive behaviours (gambling, video games, spending, alcohol and drugs), gambling motives (ENH, COP and SOC), attachment, and positive and negative affect. Adolescent females with gambling problems were found to score higher on gambling, drug use, compulsive spending, maternal attachment, and all gambling motives. To our knowledge, these data are novel as we found no studies that explore these variables in adolescent females specifically and conjointly. Nonetheless, the results are in line with previous studies conducted with young and adolescent populations, indicating that people with gambling problems obtain higher scores than the general population in gambling severity, gambling motives, attachment, drug abuse, and spending (Estévez et al., 2020a , b , c , d ; Frisone et al., 2020 ; Jáuregui & Estévez, 2019 ).

Furthermore, the analyses in this study show that young women who met the criteria for risk or possible problem gambling presented significantly higher differences in gambling, spending and enhancement gambling motives, and slightly higher in the case of coping motives. Previous studies with samples of adult women have suggested that coping motives, that is, the tendency to gamble to cope with negative emotional states, are the main precursor of addiction in women (Fonseca et al, 2021 ; Lelonek-Kuleta, 2021 ); whereas enhancement motives, that is, the tendency to gamble to increase positive emotions, have been more closely associated with the severity of gambling behaviours in men (Stewart & Zack, 2008 ). However, both coping and mood enhancement motives have been conceptualised as motives for the regulation of affective states, and both of them have been associated with gambling disorder to a greater extent than did social motives (Cooper et al., 1995 ; Grande-Gosende et al., 2019 ). Ellenbogen et al. ( 2007 ), also indicated that the most striking result of their study was that traditional sex differences practically disappeared among adolescents with gambling problems, who showed similar gambling patterns. Although coping motives have been traditionally associated with gambling in women, this study also highlights the role of enhancement motives. There is evidence of an increase in impulsivity during adolescence, including positive urgency and sensation seeking, before stabilising in adulthood (Collado et al., 2014 ; Littlefield et al., 2016 ). However, this is still an under-explored area and requires further research.

Secondly, the relationship between addictive behaviours and the rest of the study variables was analysed in the group of girls with risk and possible gambling problems. We found that drug abuse, compulsive spending, and alcohol abuse correlated negatively with both maternal and paternal attachment, or paternal attachment; whereas gambling disorder and video games correlated negatively with maternal attachment. These results are consistent with previous studies showing that patterns of an insecure attachment style are related to the symptomatic expression of risky addictive behaviours in adolescents, as well as to increased susceptibility for the development of these behaviours in adulthood (Di Trani et al., 2017 ; Strathearn et al., 2019 ; Terrone et al., 2021 ). Similarly, several studies have indicated a close link between adult gamblers and subsequent pathological gambling in their children (Dowling, Merkouris, et al., 2017 ; Dowling, Shandley, et al., 2017 ).

Studies have shown mixed results regarding the influence of the specific attachment figure (i.e., maternal or paternal) in the development of different addictive behaviours (Estevez et al., 2017 ; Forrest & McHale, 2021 ). This study highlights the relationship between maternal attachment and gambling and video game disorders, which are increasingly similar in terms of their structural and addictive characteristics (Dowling et al., 2018 ; Griffiths & Wood, 2000 ). In this regard, a study conducted by Jáuregui and Estévez ( 2019 ) points out that maternal attachment predicts social and enhancement gambling motives, which could explain the positive relationship between gambling and enhancement motives, and the negative relationship between gambling and maternal attachment found in this study. However, the data are still limited and are based on samples composed predominantly of men. Therefore, a more consistent body of research that integrates gender perspective is needed to explore the specific role of attachment figures in addiction types, as well as its relationship with gambling motives.

To conclude, the third aim of the study was to examine the predictive role of positive and negative affect, gambling motives, and attachment in the aforementioned addictive behaviours. The results indicate that the predictive variables differ depending on the addiction type. In the case of substance addictions (i.e., alcohol and drugs), mainly the predictive role of attachment has been highlighted, whereas, in behavioural addictions (i.e., gambling and spending), gambling motives have been identified as the most important predictive variables. Although substance and behavioural addictions overlap in multiple neurobiological and clinical transdiagnostic features, these results reinforce previous studies suggesting that each addictive behaviour also has its own constellation of unique aetiological, personality, or clinical traits (Kim et al., 2020 ; Zilberman et al., 2018 ).

On the other hand, in the case of gambling, coping motives were shown to be the most important predictor, findings that are in line with previous literature on female gamblers (Fonseca et al, 2021 ; Stewart & Zack, 2008 ). By contrast, the analyses in this study do not show affect, neither positive nor negative, as a predictive variable of addictive behaviours in female adolescents. These results are novel and goes against previous literature because, to date, numerous studies have identified the gambling episode as an emotion regulation mechanism to alleviate and/or avoid an intense affect that the person is trying to avoid thinking about (Cicarelli et al., 2017 ; Hing et al., 2016 ). In view of the above, the relationship between affect and gambling is likely to be indirect and, therefore, probably mediated by gambling motives. In this sense, previous research has found a mediating relationship between affect and gambling severity through gambling motives, and have even hypothesised that the relationship between positive affect and gambling may be mediated by enhancement motives, while negative affect may be more closely related to coping motives (Ballabio et al., 2017 ; Kim et al., 2019 ; Takamatsu et al., 2016 ). Nonetheless, these findings may also suggest a differential profile of women suffering from problem gambling based on age (e.g. adults and youths), something that previous studies with other addictions have already demonstrated (Granero et al., 2014 , 2018 ; Nicolai et al., 2012 ). However, more research is required to examine problem gambling from a gender- and developmental stage- perspective, to provide an effective social and healthcare response for this collective.

Limitations

There are limitations that should be considered when interpreting its results. Firstly, the cross-sectional and mainly correlational design of the present study does not allow establishing interpretations regarding causality and the direction of the effects. In the future, longitudinal designs are needed to achieve an in-depth understanding and shed light on the interaction between the variables under scrutiny. Secondly, self-report measures were used, which could have biased the results. The inclusion of clinical and qualitative criteria to detect the presence or absence of addictive disorders could potentially complement the present data. Moreover, the sub-sample of problem gambling size is not large, and it is obtained from association centres and adolescent females from the general population who scored on the SOGS-RA as at-risk or potential problem gamblers. Therefore, this sample may present differential characteristics from other clinical samples (e.g., public hospitals, private therapy centres, people who are not yet in treatment, etc.). Nevertheless, the subclinical sample does allow us to explore risk factors that may precede the development of more severe addictive behaviours in the general population, which is beneficial for preventive purposes. Further research should probably focus on comparing different profiles of female problem gamblers, considering age, as well as socio-demographic and clinical characteristics, as the results of this study cannot be generalised to adult females. However, it should be pointed out that given the lack of research on adolescent females with gambling problems, adult females have been used on several occasions to compare the results of this study. We know that it is developmentally difficult to compare adolescents with adults, but we wanted to consider what we do know about other women with GD despite their age difference. Finally, the findings obtained in this study are based on sex differences, so it would be appropriate to carry out studies based on gender differences.

Despite the limitations of this study, is there is a lack of studies exploring the profile of adolescents and young women with GD, as well as the comorbid presence of GD with substance-related problems and other behavioural addictions in this population. One of the main issues that emerges from these findings is the clear need to consider sex and age when designing treatments for people suffering from GD due to the clinical differences discussed throughout the manuscript. Furthermore, this study showed that the importance of the predictive variables for each addiction could differ from one addiction to another. These results are of great interest for prevention and intervention purposes. Given that higher co-morbidity in early ages is associated with worse prognosis and higher psychopathology in adulthood, the early detection, prevention and treatment of problems related to GD becomes essential.

Recommendation

Many of the therapeutic interventions with adolescents with GD are based on group therapy. Therapy groups are a tool that have demonstrated numerous benefits, highlighting the therapeutic power of the shared experience. There are usually not enough young women to generate a group just with them, however, they do make mixed groups (i.e. with young women and men together) in which age and gambling is a common factor among them. In this sense, the group allow us to observe a micro-representation of how these young people develop their gender roles in society. This allows us to act directly on gender biases, enabling us to seek a balance between autonomy (something culturally associated with the male gender role) and care, interdependence and emotional expressiveness (culturally associated with the female gender role). It should also be noted that interventions with youth are especially preventive for their further development in adulthood. Finally, we make an invitation to think "with gender glasses", that is, to look beyond what is a mere biological sex difference, and to consider the educational and socialisation process that determines gender, as well as its relation to the development of the symptom.

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Macía, L., Estévez, A. & Jáuregui, P. Gambling: Exploring the Role of Gambling Motives, Attachment and Addictive Behaviours Among Adolescents and Young Women. J Gambl Stud 39 , 183–201 (2023). https://doi.org/10.1007/s10899-022-10124-8

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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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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.”

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Online Gambling Addiction: the Relationship Between Internet Gambling and Disordered Gambling

Affiliation.

• 1 Centre for Gambling Education and Research, Southern Cross University, PO Box 157, Lismore, NSW 2480 Australia.
• PMID: 26500834
• PMCID: PMC4610999
• DOI: 10.1007/s40429-015-0057-8

One of the most significant changes to the gambling environment in the past 15 years has been the increased availability of Internet gambling, including mobile; Internet gambling is the fastest growing mode of gambling and is changing the way that gamblers engage with this activity. Due to the high level of accessibility, immersive interface and ease at which money can be spent, concerns have been expressed that Internet gambling may increase rates of disordered gambling. The current paper aimed to provide an overview of the research to date as well as highlight new and interesting findings relevant to Internet gambling addiction. A comprehensive review of the existing literature was conducted to provide an overview of significant trends and developments in research that relates to disordered Internet gambling. This paper presents research to inform a greater understanding of adult participation in Internet gambling, features of this interface that may impact problem severity, the relationship between Internet gambling and related problems, as well as considering the role of the wider spectrum of gambling behaviour and relevant individual factors that moderate this relationship.

Keywords: Addiction; Causation; Determinants; Disordered gambling; Gambling harm; Interactive gambling; Internet gambling; Mental health; Online gambling; Problem gambling; Protective factors; Risk factors.

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Online Gambling Addiction: the Relationship Between Internet Gambling and Disordered Gambling

Sally m. gainsbury.

Centre for Gambling Education and Research, Southern Cross University, PO Box 157, Lismore, NSW 2480 Australia

One of the most significant changes to the gambling environment in the past 15 years has been the increased availability of Internet gambling, including mobile; Internet gambling is the fastest growing mode of gambling and is changing the way that gamblers engage with this activity. Due to the high level of accessibility, immersive interface and ease at which money can be spent, concerns have been expressed that Internet gambling may increase rates of disordered gambling. The current paper aimed to provide an overview of the research to date as well as highlight new and interesting findings relevant to Internet gambling addiction. A comprehensive review of the existing literature was conducted to provide an overview of significant trends and developments in research that relates to disordered Internet gambling. This paper presents research to inform a greater understanding of adult participation in Internet gambling, features of this interface that may impact problem severity, the relationship between Internet gambling and related problems, as well as considering the role of the wider spectrum of gambling behaviour and relevant individual factors that moderate this relationship.

Introduction

Internet gambling (a term largely interchangeable with interactive remote and online gambling) refers to the range of wagering and gaming activities offered through Internet-enabled devices, including computers, mobile and smart phones, tablets and digital television. This mode of gambling, facilitated by technological advances, increased Internet availability and ownership of Internet-enabled devices, is not a separate type of gambling activity. Rather it is a mode of access that is distinct from gambling in person at terrestrial or land-based retail outlets and placing wagers over the telephone. As such, it is a largely automated activity that could be conducted in private, at any time and location, using high-speed Internet connections enabling rapid placement of bets and notification of outcomes. The ability for large wagers, continuous gambling, rapid feedback and instant, easy access to a vast number of betting options has resulted in concerns that Internet gambling could contribute to excessive gambling [ 1 , 2 ].

As a result of the empirical comparisons demonstrating the fundamental parallels between gambling problems and substance use, the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) includes a new category of Non-Substance Behavioural Addiction within the substance addictions category [ 3 ]. Disordered gambling is classified as the first behavioural addiction and will serve as a ‘blueprint’ for research on other syndromes and arguably set a precedent for the compilation of evidence on other similarly excessive behaviours [ 4 ] such as ‘Internet gaming disorder’ (currently in section 3 of the DSM-5). Mounting evidence of distress and dysfunction related to excessive and problematic Internet use and specifically Internet gaming led the DSM-5 Taskforce to officially call for further research on this behaviour [ 5 ]. Given the similarities in the experience and excessive use of Internet gambling and gaming and the potential for harm based on excessive Internet use, pathological use of Internet gambling also warrants specific consideration [ 4 ]. The current paper aimed to provide an overview of the research to date as well as highlight new and interesting findings relevant to adult Internet gambling addiction. A comprehensive review of the existing literature was conducted to provide an overview of significant trends and developments in research that relates to disordered Internet gambling.

Participation

Internet gambling is growing rapidly in terms of popularity, market share and products offered. The online global gambling market was valued at €6.1 billion in 2013, with expected annual growth of 10.1 % in 2018 [ 6 ]. Online gambling accounted for an estimated 8–10 % of the total global gambling market in 2012, and this proportion appears to be increasing [ 7 – 9 ]. Globally, the largest online gambling product is wagering, accounting for 53 % of the online gambling market, followed by casino games (including slot machines/pokies/electronic gaming machines, 25.4 %), poker (14.2 %), and bingo (7.4 %) [ 8 ].

Internationally, an increasing number of jurisdictions are legalizing and regulating Internet gambling [ 10 ]. This follows recognition of the difficulties of enforcing prohibition and the benefits of regulation, including requiring harm minimization measures to enhance consumer protection, and generating taxation revenue [ 1 ]. Although the prevalence of Internet gambling appears to be relatively low, participation is increasing rapidly, particularly in jurisdictions that permit access to regulated sites [ 11 , 12 ••]. For example, in Australia following the legalization of Internet wagering and lottery playing, prevalence rates in Internet gambling rose from less than 1 % in 1999 to 8.1 % in 2011 [ 13 ]. Similarly in the UK, an average of 16 % of respondents had participated in at least one form of online gambling in the previous 4 weeks [ 11 ]. In comparison, only 6 % of the British population used the Internet to gamble in the past year in 2007, although this figure does not include purchasing lottery tickets online, which may have increased the participation rate [ 14 ].

Internet gambling use is likely to continue to grow as online platforms become increasingly used to engage in entertainment and recreational activities, including through phones and other wireless devices. Research suggests that the most commonly reported motivators and advantages of Internet gambling are the convenience and accessibility of this mode [ 15 – 17 ]. Other commonly stated advantages of Internet gambling include greater value for money, including payout rates and bonuses, the speed and ease of online gambling, greater number of betting products and options and the physical comfort of being able to gamble from home.

Internet gambling represents a fundamental shift in how consumers engage in gambling, and concerns have been expressed by various stakeholders about these changes. Disadvantages cited by Internet gamblers include that it is easier to spend money online, it is too convenient and concerns about account safety [ 15 – 18 , 19 •, 20 ]. Other concerns include that the high accessibility to Internet gambling may increase gambling, particularly among technology-savvy youth, and lead to an increase in the incidence and prevalence of disordered gambling [ 1 , 21 ]. These concerns have led to recommendations for Internet gambling to be prohibited, or conversely regulated, in an attempt to institute policies to minimize harms [ 1 , 12 ••, 18 , 22 , 23 •, 24 ].

Internet Gambling and Problem Gambling

Features of internet gambling that may impact problem severity.

Evidence suggests that there is a relationship, albeit complex, between the availability of gambling opportunities and increased levels of related problems [ 25 – 30 ]. Consequently, it has been asserted that the easy access to gambling provided by Internet modes may lead to the development or exacerbation of gambling problems [ 1 , 22 , 24 , 31 ].

Internet gambling also has some unique features that may pose additional risks for harm, particularly for vulnerable populations. Internet gambling differs from land-based gambling primarily in terms of its constant availability, easy access and ability to bet for uninterrupted periods in private, facilitated by the interactive and immersive Internet environment [ 2 , 18 , 32 – 34 , 35 •]. The use of digital forms of money (e.g. credit cards, electronic bank transfers and e-wallets) appears to lead to increased gambling and losses, particularly for problem gamblers, as people feel that they are not spending ‘real’ money [ 16 , 32 , 36 , 38 , 39 ]. Surveys indicate that 19–28 % of online gamblers report it is easier to spend more money online [ 20 , 39 ], while 15 % consider this form to be more addictive than land-based gambling [ 15 ].

The immersive nature of Internet gambling is also clear through reports that online gamblers, particularly those experiencing problems, are more likely to report disruption to their sleep and eating patterns than land-based gamblers [ 18 , 36 , 37 ]. Data collected by gambling treatment services suggest that Internet gambling currently makes a small, but growing, contribution to gambling problems among those seeking formal help [ 37 , 40 , 41 ]. Surveys have found that online problem gamblers are significantly less likely to have sought formal help as compared to land-based problem gamblers [ 20 , 42 , 43 ]. This suggests that problems related to Internet gambling may be underrepresented in treatment-seeking samples and are likely to increase over time as more people participate in this mode and problem severity increases.

The Relationships Between Internet Gambling and Gambling Problems

Initial concerns over the harmful effects of Internet gambling are sensible as numerous studies have found greater levels of problem gambling severity amongst samples of Internet as compared to non-Internet gamblers [ 13 , 31 , 41 , 43 – 46 , 47 •, 48 ]. For example, in an Australian nationally representative prevalence survey, the overall problem gambling rate among Australian non-Internet gamblers was 0.9 %. In comparison, the rate among Internet gamblers was three times higher at 2.7 % [ 13 ]. Fewer than 60 % of Internet gamblers were classified as non-problem gamblers, compared to more than 80 % of non-Internet gamblers, which was a significant difference. Furthermore, the average PGSI score of Internet gamblers was significantly higher than that of non-Internet gamblers. Similarly, a total of 16.4 % of Internet gamblers were classified as either moderate or problem gamblers, compared to a rate of 5.7 % among non-Internet gamblers [ 43 ]. However, there is little evidence available that would enable the causation of Internet-related gambling problems to be determined, and most longitudinal studies contain too few Internet gamblers to provide meaningful analyses.

Despite some indications of a positive correlation, the relationship between Internet gambling participation and problems has not been confirmed. Some studies have found similar rates of gambling problems among Internet and land-based gamblers [ 15 , 41 ]. Research also suggests that very few Internet gamblers gamble exclusively online [ 12 ••, 24 , 48 , 49 ]. Further analyses of prevalence studies that control for factors such as demographic variables and gambling involvement have found that participation in Internet gambling does not independently predict problem gambling severity [ 13 , 20 , 36 , 46 , 50 ••, 51 , 52 ]. For example, even though Internet gamblers were more likely to be classified as being at risk or experiencing gambling problems in a nationally representative survey, when other variables were controlled for, Internet gambling participation was not predictive of problem gambling severity [ 13 ]. Similarly, using data from the 2007 British Gambling Prevalence Study, LaPlante and colleagues [ 50 ••] found that gambling formats (particularly Internet gambling) and problem gambling were not significantly related when gambling involvement was included in the model (based on the number of gambling activities used in the past 12 months). This finding was in contrast to earlier analyses [ 31 ] and demonstrates the importance of controlling for confounding factors.

Further evidence to question the extent to which Internet gambling increases rates of problem gambling can be taken from prevalence studies. Despite rates of Internet gambling increasing in several jurisdictions, little evidence has been found to suggest that the prevalence of problem gambling has increased [ 13 , 53 , 54 ]. An analysis across 30 European jurisdictions failed to identify any association between prohibitions against online gambling, gambling licencing systems, the extent of legal gambling opportunities and the prevalence of gambling disorder [ 55 ••].

The Impact of Internet and Land-Based Gambling on Gambling Problems

Evidence is emerging that Internet gambling is not only predictive of gambling problems but also that when other variables are controlled for, individuals who gamble online may have lower rates of gambling problems. Studies that have isolated Internet-only gamblers have found that these gamblers have lower rates of gambling problems than gamblers who only gamble offline and those who use both online and offline modes [ 48 , 51 , 56 •]. Gamblers who engage in online as well as offline modes appear to have the greatest risks of harm, which is likely related to their greater overall gambling involvement [ 48 , 56 •, 57 ••].

The relationship between Internet and problem gambling is likely mediated by the use of land-based gambling. A study examining actual Internet gambling account activity combined with a self-report measure of gambling problems confirmed that gambling involvement, as indicated by number of games played and days bets placed on in past year, is predictive of gambling problems amongst the sample of Internet gamblers analysed [ 58 ]. These results are consistent with a wide body of research which suggests that gambling disorder is related to high levels of involvement (in terms of expenditure, time, frequency and variety of gambling forms used) [ 13 , 36 , 52 , 59 – 63 ]. Therefore, research suggests that highly involved gamblers are more likely to engage with Internet modes, including those with existing gambling problems, than less involved gamblers. However, a study comparing behavioural data from online gambling sites with self-report of gambling problems found that not all highly involved gamblers were at risk for gambling-related problems, and likewise, not all those with low involvement screened negatively for gambling-related problems [ 64 ]. This is an important finding as it demonstrates (unsurprisingly) that a single gambling index (such as a frequency of gambling, or expenditure) is not adequate to predict gambling problems.

Involvement in Internet gambling appears to be more likely among gamblers with existing problems as compared to non-problem gamblers [ 35 •]. Studies have found that one third to one half of Internet gamblers experiencing gambling problems attribute these to land-based forms of gambling, and over half report that they had existing problems before they ever gambled online [ 13 , 20 ]. This is consistent with one study reporting that problem Internet gamblers prefer land-based over Internet gambling [ 24 ]. Few studies have investigated the types of gambling that are most likely to be associated with problems related to Internet gambling. In an Australian national survey, almost half of all gamblers stated that land-based electronic gaming machines were the primary cause of their problems, including among Internet gamblers [ 13 ]. Internet gamblers are most likely to associate their problems with casino games, sports and race wagering and poker [ 13 , 20 ]. In particular, sports betting appeared to be associated with moderate risk and problem gambling, a finding not replicated among land-based only gamblers [ 13 , 20 ]. However, this finding may be specific to the Australian context as sports wagering is one of the few legal forms of online gambling.

Conversely, for some Internet problem gamblers, this mode of gambling appears to be the proximal cause of problems, with problem gamblers reporting that their problems started after they first gambled online and around half specifically attributing problems to this mode [ 13 , 20 ]. These results are consistent with other research findings [ 57 ••, 48 ], suggesting that for some problem gamblers, Internet gambling played an important causal role, while others had existing problems, which were likely exacerbated by Internet gambling. However, most studies examining the relationship between Internet gambling and problems are cross-sectional, which do not allow for causality to be determined and self-report is subject to bias and reliant on accuracy of reporting. Longitudinal research will be an important addition to this field to address these issues. As Internet gambling increases in popularity and use, it is likely that the next generation of gamblers will use Internet modes earlier in their gambling career, which may increase the proportion of individuals who experience problems that are attributed to this mode. However, there is a growing recognition that Internet gamblers are a heterogeneous group, and research needs to consider how Internet gambling behaviour may be integrated more broadly with offline gambling [ 48 , 65 ].

Risk Factors for Internet Gambling Problems

Personal variables, socio-demographic variables.

Analysis of demographic variables suggests that Internet problem gamblers overall do not represent a distinctly different cohort than gamblers who experience problems related to land-based gambling. Risk factors for Internet problem gambling identified include being male, younger adults, and being from a culturally diverse background [ 13 , 20 , 41 , 66 , 67 ]. The consistent relationship found between problematic Internet gambling and younger age suggests that this population is particularly vulnerable to harms related to this form, and use of Internet gambling amongst young males is an area that warrants further attention in terms of research as well as harm minimisation.

Risk factors identified do not appear to be universal; for example, Gainsbury, Russell, Wood, Hing and Blaszczynski [ 13 ] found problem Internet gamblers more likely to be young, less educated and have greater debts than non-problem Internet gamblers. A subsequent study found only age differed between Internet and non-Internet problem gamblers when controlling for Internet gambling participation, and there were no significant differences based on education or income [ 20 ]. In contrast, Jiménez-Murcia and colleagues [ 68 ] found that online problem gamblers had higher educational levels and higher socio-economic status than non-Internet problem gamblers; however, both groups showed similar psychopathological profiles or personality characteristics. Other studies have also found that Internet gamblers are more likely to have higher educational levels and socio-economic profiles [e.g. 43 , 48 , 65 ], as well as higher levels of problem gambling than non-Internet gamblers. However, these are associations that do not control for the interaction between variables so it is difficult to draw firm conclusions about problem as compared to non-problem Internet gamblers. It is likely that the profile of those at risk for developing Internet gambling problems will change as this mode of gambling becomes more accepted and widely used and further research is conducted.

Physical and Mental Health Comorbidities

Studies have also found higher rates of health and mental health comorbidities, including smoking and alcohol consumption, as well as substance abuse or dependence, and mood disorders among Internet as compared to non-Internet gamblers [ 13 , 15 , 30 , 31 , 43 , 44 , 47 •, 49 , 57 ••, 67 , 69 , 70 ]. \One study found that Internet gambling frequency was significantly associated with poor physical and mental health, after controlling for demographics and pathological gambling, but overall gambling frequency was not [ 71 ]. A study examining irrational and erroneous thinking found that greater levels of erroneous cognitions significantly predicted problem gambling severity when controlling for other variables among Internet gamblers [ 46 ]. As psychological comorbidities and irrational thinking are related to problems amongst land-based gamblers, these results suggest that the clinical characteristics of Internet problem gamblers are similar to offline gamblers.

There is also evidence that Internet problem gamblers have higher rates of drug and alcohol use than non-problem gamblers. Analysis of 1119 surveys completed by online gamblers indicated that compared to non-problem gamblers, problem gamblers were more likely to smoke cigarettes, have a disability and drink alcohol while gambling online [ 67 ]. This is consistent with higher rates of mood and substance use disorders and self-harm among highly involved Internet gamblers [ 70 ]. An Australian telephone survey found that illicit drug use was a significant predictor of having greater levels of gambling problems [ 13 ]. These results may indicate that Internet gamblers who are at risk for gambling problems may engage in a range of risk-taking behaviours, for example, due to high levels of impulsivity [ 72 ].

Nonetheless, the relationships between Internet gambling, gambling problems and other mental health issues are still unclear [ 73 ]. For example, multiple studies in Sweden did not support the assumption that Internet gambling would attract people with low social support, psychological problems, physical problems or health problems such as risky alcohol consumption [ 41 ]. Similarly, offline gamblers were more likely to report health and psychological impacts of problem gambling than Internet gamblers in an Australian study comparing at-risk and problem gamblers [ 20 ]. Furthermore, in a nationally representative Australian telephone survey, Internet gamblers were less likely to drink alcohol and smoke when they were gambling online than when gambling in land-based venues, indicating they were unlikely to be using Internet modes to avoid restrictions on smoking or alcohol [ 13 ].

Overall, existing studies fail to define specific personal or behavioural risk factors to differentiate between Internet and non-Internet problem gamblers. There is some evidence that these do represent at least partially different cohorts; however, the heterogeneity in each group makes specific risk factors difficult to identify. No studies have established the causation between associations found and the direction of any link between problem online gambling. The individual factors related to Internet gambling problems are under-researched and would benefit from longitudinal studies to clarify the mechanism of action of any relationships between variables.

Gambling Behaviours

Intense gambling involvement has been verified as a predictor of gambling problems for online and offline gamblers. Other gambling-related behaviours have also been identified as being potential markers of risky Internet gambling. Gambling online on unregulated sites [ 41 , 74 ] and using multiple different accounts [ 75 ] and different online activities [ 20 , 48 , 57 ••] have been found to be predictive of higher levels of gambling problems. It is possible that unregulated sites attract individuals who are at greater risk for experiencing problems, and use of multiple online accounts and multiple activities is a proxy indicator of gambling involvement, a known predictor of harm.

Analyses of player accounts, including players who exhibit what appears to be risky behaviour, as well as those who have closed accounts due to stated gambling problems, have enabled markers of problem gambling, including early predictors, to be identified. Potential predictors of risky Internet gambling or the emergence of problems include engaging in multiple online gambling activities, high variability in betting, multiple bets per day, many active betting days per month, many bets per betting day, high overall stakes and net loss, increasing bet size and losses, chasing losses and intervals of increasing wagering size, followed by rapid drops [ 58 , 59 , 76 – 80 ]. One notable finding from studies of the bwin.party dataset (which include most of the behavioural analyses that have been conducted) is the consistent finding that participation in live action sports betting (also known as in-play) is an independent predictor of problem gambling severity, when controlling for gambling involvement [ 58 , 59 , 79 ]. This type of betting allows frequent and repeated bets to be placed during a single sporting event, with rapidly determined outcomes, which may be particularly attractive to people who are highly impulsive and at greater risk for disordered gambling [ 81 ]. However, this relationship has not been investigated in independent samples.

In addition to behavioural variables, other information about gamblers’ risk levels can be observed by online operators. Analysis of customer communication with online operators identified risk markers that predicted customers closing their accounts due to stated gambling problems. These included expressed doubts about results of games, requests for account reopening, queries about financial transactions and account administration, the frequency of contacts per month (urgency) and use of a threatening tonality [ 82 ]. These results were based on a relatively small sample with a limited control group. A subsequent study found that automated text analyses of email correspondence aided by human assessment could identify anger (abusive tonality) as well as urgency (time-related words) and a lower use of justification for demands and/or actions, which were found to predict self-exclusion [ 83 ].

Single, unmistakable indicators for problems are uncommon, and therefore detection of risk indicators usually relies on algorithms to detect interaction between these. Further research is still required to untangle whether game-specific characteristics play a causal role in the emergence of gambling problems. Research is also needed on a variety of different player accounts, as the vast majority of research has been done with a single dataset from one European gambling site, which may not be generalizable to other online gamblers. Identifying, detecting and acting on early risk indicators may reduce gambling-related harms sustained by Internet gamblers. However, few online operators have shared their data to be used for research purposes or implemented policies and strategies to detect potentially risky players and implement appropriate resources. Such preventative action is generally not required by Internet gambling regulators, meaning that further action is reliant on operator-initiated action.

Conclusions

Taken together, the evidence reviewed here suggests that Internet gambling does not cause gambling problems in, and of, itself. However, use of Internet gambling is more common among highly involved gamblers, and for some Internet gamblers, this medium appears to significantly contribute to gambling problems. Internet gamblers are a heterogeneous group, and the impact of this mode of access on gambling problems is moderated by a range of individual, social and environmental variables. As Internet gambling continues to evolve and participation increases, particularly among young people who are highly familiar with Internet technology and online commerce, it is likely that related problems will emerge. Research and regulation will have to evolve to further the understanding of the impact of this mode of access on the experience and incidence of gambling disorders.

There appear to be some unique differences between Internet and land-based gamblers who experience problems [ 20 ]. Theoretical models for gambling and problem gambling have been developed based on land-based gambling, largely not considering the recent emergence of Internet modes. It is important to revisit these conceptual models to verify if they account for pathological gambling among Internet gamblers and whether any new variables or interactions should be included to explain the emergence of gambling problems. Research will likely continue to distinguish the characteristics (mediators and moderator) that may be used to identify online gamblers who are at risk for gambling-related problems. This is necessary to develop a more comprehensive understanding of how people develop gambling problems.

Research is needed to understand how to reduce the likelihood of people transitioning to disordered gambling. The Internet offers a potentially strong environment for the provision of responsible gambling, including player-focused tools and resources for moderating play such as expenditure tracking, self-set spend limits, time outs and information [ 19 •, 84 ]. Furthermore, operators can enact strategies to assist customers including targeted notifications (e.g. pop-up messages) based on patterns of play and other tailored contacts derived from analysis of player accounts to identify risky behaviour [ 2 , 85 ]. Enhancing the provision of a responsible gambling environment will require cooperation between independent researchers to design, evaluate and verify strategies, operators to enable access to appropriate data and implement procedures and regulators to require the use of effective responsible gambling policies. Treatment and prevention strategies must be revisited to ensure that these are relevant and effective for Internet gamblers. Brief online interventions as well as in-depth online treatment programmes may be relevant for Internet gamblers [ 85 ]. Online self-exclusion programmes should be developed that would allow individuals to exclude themselves from multiple gambling sites simultaneously.

The findings presented here are important for policy makers due to evidence that Internet gambling in itself is not harmful. The research is also relevant for clinicians, as it suggests that in addition to some gambling forms being more likely to lead to problems, how individuals access these also has an impact on subsequent harms. This highlights the importance of considering the broad spectrum of gambling behaviour and how different patterns of gambling may be associated with the experience of gambling-related harm. Further research is required to identify the protective factors of online gambling environments that may reduce levels of harms among Internet gamblers. These may include the capacity for lower bet sizes than in land-based venues (due to lower costs for operators); the ability to track wins, losses and deposits using an online account, gambling only for short sessions due to other activities concurrently occurring in the home, or outside of a gambling venue; the presence of others when gambling; and access to responsible gambling tools and resources [ 51 ].

Compliance with Ethics Guidelines

Conflict of interest.

Dr. Gainsbury has received grants from Gambling Research Australia; NSW Office of Liquor, Gaming and Racing; Echo Entertainment; Aristocrat Leisure Industries; Manitoba Gambling Research Program; and Sportsbet pertaining to research to understand and enhance the responsible provision of Internet gambling, research to understand optimal treatment approaches for gambling, research to enhance responsible gambling strategies and assessment of problem gambling among casino employees. Dr. Gainsbury has received honoraria from the Department of Broadband Communication and the Digital Economy, Department of Social Services, Gaming Technologies Association, British Columbia Lottery Corporation and Nova Scotia Provincial Lotteries and Casino Corporation for research and expertise to inform responsible gambling messages and responsible gambling strategies for Internet gambling. Dr. Gainsbury has received travel accommodations or expense reimbursement from the British Columbia Lottery Corporation, Clubs ACT, Leagues Clubs Australia, National RSL Clubs, Nova Scotia Gaming Corporation and Casinos Austria to attend and present at conferences on topic of responsible gambling. Dr. Gainsbury was a board member on Techlink Entertainment’s Responsible Gambling Advisor Board from January 2012 through May 2013.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

This article is part of the Topical Collection on Technology and Addiction

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