thesis statement about gambling addiction

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Understanding Gambling Addiction

• Steps to Getting Treatment
• Stopping a Gambling Addiction

Gambling disorder (also called gambling addiction) is characterized by repeated, problem gambling behavior that leads to problems for the individual and their loved ones. Approximately 1% of the population currently has a gambling disorder. Some common symptoms of gambling disorder include not stopping or controlling gambling, lying about gambling, being preoccupied with gambling, and spending excessive amounts of time gambling.

Gambling disorder can cause problems with mental and physical health, relationships, finances, and more. Treatment options for gambling disorder include counseling, medications, and support groups.

This article will discuss what gambling addiction is, symptoms of gambling addiction, causes and risk factors for gambling addiction, effects of gambling addiction, treatment for gambling addiction, and coping through gambling addiction treatment.

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Defining Gambling Addiction

To meet the criteria for a diagnosis of gambling disorder, at least four of the following must have occurred during the past year and caused significant distress:

• Needing to gamble increasingly high monetary amounts to achieve the desired excitement.
• Repeated unsuccessful attempts to cut back on, control, or stop gambling.
• Restlessness or irritability when trying to cut down on or stop gambling.
• Frequently gambling when feeling distressed.
• Frequently thinking about gambling (such as reliving past gambling or planning future gambling).
• Often "chasing one's losses" (i.e., returning to "get even" after losing money gambling).
• Risking or losing a job, school or job opportunity, or close relationship because of gambling.
• Lying to hide gambling activity.
• Relying on others for help with money problems stemming from gambling.

Symptoms of gambling disorder can subside for periods and return.

Gambling problems can develop quickly or over many years. Gambling activities also occur along the following continuum:

• No gambling : People who never gamble
• Casual social gambling : The most common type of gambling. Buying an occasional lottery ticket, occasionally visiting a casino for entertainment, etc.
• Serious social gambling : Regular gambling, and gambling as a primary form of entertainment, but does not harm work or personal relationships.
• Harmful involvement : Gambling that leads to difficulties with personal, work, and social relationships.
• Pathological gambling : Gambling seriously harms all aspects of the person's life, and they are unable to control the urge to gamble despite the harm it is causing.

Typical Symptoms 

Symptoms of gambling disorder can vary, but may include:

• Lying about gambling behavior
• Gambling more than you can afford to lose
• Obsessive preoccupation with gambling (excessively thinking about it even when not in the act of gambling)
• Stopping doing things you previously enjoyed
• Ignoring self-care, school, work, or family tasks
• Withdrawing from friends and family
• Missing family events
• Changes in patterns of eating, sleeping, or sex
• Regular lateness for school or work
• Increased alcohol or drug use
• Decreased willingness to spend money on things other than gambling
• Having conflicts with others over money
• Having legal problems related to gambling
• Neglecting your children's needs and welfare (such as leaving them alone, or neglecting their basic care)
• Frequently borrowing money or asking for salary advances
• Cheating or stealing to obtain money for gambling or paying debts
• Taking a second job, without a change in finances
• Cashing in assets such as savings accounts, RRSPs, or insurance plans
• Alternating between being broke and flashing money
• Organizing staff pools
• Leaving for long, unexplained periods
• Feeling anxious
• Having difficulty paying attention
• Having mood swings and sudden bursts of anger
• Feeling bored or restless
• Feeling depressed or having suicidal ideation

Suicide Prevention Hotline

If you are having suicidal thoughts, contact the  National Suicide Prevention Lifeline  at  988  for support and assistance from a trained counselor. If you or a loved one are in immediate danger, call  911 . For more mental health resources, see our  National Helpline Database .

Traits and Signs In Others 

Gambling addiction can be hard to recognize, especially since signs often remain hidden until they become severe, such as a dire financial situation.

If you notice symptoms of gambling disorder, such as those mentioned above, in a friend or family member and want to talk to them about it, there are ways to approach it:

• Prepare yourself for many possible reactions from them, including anger and denial.
• Manage your expectations (don't expect them to quit right away; it can take time).
• Be honest when sharing your concerns.
• Remember that stopping their gambling behavior is their responsibility, not yours (you are there for support).
• Don't preach or lecture.
• Remain calm and keep control of your anger.
• Recognize their good qualities.
• Seek support from others in similar situations (such as a self-help group for families, like Gam-Anon).
• Let them know how the gambling is affecting you and, if applicable, the children or other family members.
• If you share finances, set boundaries in managing money (review bank and credit card statements, take control of family finances, etc.).

Causes, Triggers, and Risk Factors

Problem gambling stems from a psychological principle called Variable Ratio Reinforcement Schedule (VRRS). With VRRS, mood-stimulating rewards are variable and unpredictable. This can cause someone to gamble compulsively.

Adolescents and young adults are particularly vulnerable to the adverse effects of gambling compared to adults. This may be linked to their stage of brain development, with decision-making and impulse-control skills still developing.

Some factors that may contribute to problem gambling behaviors in adolescents and young adults include:

• Increased availability and access to gambling activities without supervision or physical proximity to a gambling venue (through online gambling)
• Gambling as a coping mechanism for stress and anxiety (including previously experienced trauma, abuse, or neglect, and problems with mental health)
• Family history of gambling or addiction
• Peer pressure
• High number of risk behaviors in other areas (such as alcohol and drug use)
• Problems with decision-making and impulse control
• Exposure to gambling (such as "loot boxes") or simulated gambling (such as slot machines using virtual money or points) through video games
• Seeing parents, siblings, or other family members engage in gambling

Gambling disorder can begin at any age. Males are more likely to start at an earlier age, while females are more likely to start later in life.

Some factors that can contribute to the development of (or difficulty stopping) gambling problems include:

• Having easy access to gambling
• Having an early big win, creating an expectation of future wins
• Holding erroneous beliefs about the odds of winning
• Not taking steps to monitor gambling wins and losses
• Having a history of mental health problems, especially depression and anxiety
• Often feeling bored or lonely
• Having a history of risk-taking or impulsive behavior
• Having self-esteem tied to gambling wins or losses
• Having recently had a loss or change, such as job loss, divorce, retirement, or the death of a loved one

Types of Games Associated With Gambling

Gambling activities can include:

• Casino table games
• Electronic gaming machines (such as slot machines and poker machines)
• Horse racing
• Internet gambling sites
• Charitable raffles

All forms of gambling have the potential to be addictive. But ones that are rapid, have immediate large payouts (such as slot machines), involve betting small amounts to win a huge jackpot, or allow you to place multiple bets at one time tend to be at higher risk for addiction.

Effects of Gambling Addiction

Gambling disorder can have far-reaching effects, and cause problems in a number of areas.

Self-Esteem and Mental Health

Problem gambling has been associated with mental health conditions and considerations, such as:

• Increased negative mood states
• Elevated stress levels
• Feelings of helplessness
• Changes in personality or mood
• Increased drug or alcohol use
• Bipolar disorder
• Obsessive-compulsive disorder (OCD)
• Attention deficit hyperactivity disorder (ADHD)
• Increased risk of suicide

While gambling disorder may not cause these conditions, it can exacerbate the symptoms and effects associated with them.

Relationships

Gambling disorder can cause people to withdraw from friends and family. Behaviors associated with gambling disorder, such as asking to borrow money, lying, stealing, and not fulfilling responsibilities, can lead to conflict with others. These factors and others can strain personal relationships.

Financial losses are not necessary for a person to have gambling disorder, but they often occur.

People who have gambling disorder may experience financial issues such as:

• Credit card debt and other debts
• Lower credit scores
• Denial of mortgages and loans
• Unpaid bills
• Lack of money for food and other essentials
• Regularly borrowing money

Some people with gambling disorder reach a point they begin selling household items or stealing.

Loss of Time and Productivity

When gambling prioritizes a person's time and becomes a mental preoccupation, it can lead to a loss of productivity at work, school, home, or in other areas.

Physical Health

Gambling disorder, and the stress that comes with it, can lead to health problems such as:

• Sleep disturbances and deprivation
• Poor hygiene and self-care
• Stomach or bowel issues
• Overeating or loss of appetite

A Word From Verywell

If your loved one seems increasingly preoccupied by gambling, is withdrawing from other areas of their life, or is noticing negative consequences to their finances, work, or relationships, it could be a sign that this is something to pay closer attention to.

Steps to Get Gambling Addiction Treatment

Treatment for gambling disorder usually involves counseling, and often support groups. In some cases, medication may also be helpful.

Counseling and Psychotherapy

Counseling and forms of psychotherapy (talk therapy) are first-line approaches to gambling disorder treatment. Cognitive-behavioral therapy (CBT) is the most common and frequently studied form of treatment for gambling disorder.

CBT helps people with gambling disorder to identify damaging thought patterns and behavior and modify them into more productive patterns.

CBT for gambling can include components such as:

• Correcting cognitive distortions about gambling
• Developing problem-solving skills
• Learning social skills
• Learning relapse prevention

Other forms of therapy that may be used include:

• Psychodynamic therapy
• Group therapy
• Family therapy

Counseling can help you:

• Deal with gambling urges
• Manage stress and handle other problems
• Gain control over your gambling
• Heal family relationships
• Maintain recovery
• Avoid triggers

Family members affected by a loved one's gambling may also benefit from counseling. Financial counseling can be helpful for those in need of financial recovery and management.

Medications

While there are no U.S. Food and Drug Administration (FDA)-approved medications for treating gambling disorders, medications that treat co-occurring conditions which can make gambling behavior worse, such as depression or anxiety , can be helpful.

Currently, medications are being studied for their potential in treating gambling disorder, particularly in reducing urges and cravings for gambling. Certain opioid antagonists have been found in randomized trials to be more successful than placebo in the treatment of gambling disorder.

More research is needed to determine if medications can be effectively used as a primary treatment for gambling disorder.

Support Groups

Some people with gambling disorder find peer support through groups such as Gamblers Anonymous to be helpful.

Gamblers Anonymous is a 12-step program in which participants attend meetings, share experiences, and offer each other support as they abstain from gambling.

Gambling Therapy is another organization, similar to Gamblers Anonymous, that offers online support groups to people with gambling disorder and their families.

Support groups are not a substitute for professional treatment.

How to Stop a Gambling Addiction

The first step to stopping gambling addiction is recognizing the problem . Once you realize you have a problem with gambling, it's time to reach out for help.

You can start by contacting your healthcare provider, a mental healthcare professional, or support groups and resources for gambling disorder. From there, you can be put in touch with the programs and resources you will need to start your recovery.

Resources and Support 

Places to find resources and support for gambling disorder include:

• Gamblers Anonymous
• National Council on Problem Gambling
• National Problem Gambling Helpline
• Gambling Therapy

Coping Through Gambling Addiction Treatment

Getting professional help for gambling addiction is paramount, but there are strategies you can use at home to help you cope while you go through treatment.

Give yourself actionable, realistic short and long-term goals to help you stay focused.

Distract Yourself

Keep yourself busy with other activities, and look for alternative ways to fill the time you used to spend gambling.

Practice Relaxation

Activities such as yoga , physical activity, meditation, and progressive muscle relaxation, can help foster relaxation.

Avoid High-Risk Situations and Triggers

Stay away from gaming venues, avoid carrying cash and credit cards, or anything else that makes you more tempted to gamble . Some gambling venues and apps have options for you to have yourself voluntarily banned from using their services.

Contact Your Supports

Talk to friends and family, or other people you trust. Or go to a Gamblers Anonymous meeting.

Gambling disorder is associated with a number of symptoms, such as being excessively preoccupied with gambling, intense cravings to gamble, and gambling more than you can afford to lose. Gambling disorder can cause problems with relationships, mental and physical health, finances, productivity, and more. Treatments for gambling disorder include counseling, support groups, and medication.

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By Heather Jones Jones is a freelance writer with a strong focus on health, parenting, disability, and feminism.

Exploring experiences of psychological treatments for gambling addiction

--> Marvin, Joshua (2023) Exploring experiences of psychological treatments for gambling addiction. DClinPsy thesis, University of Sheffield.

Literature Review Gambling addiction is now a growing public health concern. However, our understanding of how individuals experience psychological treatment for gambling addiction is limited. It is important to understand such experiences more deeply, particularly as treatment guidance is under development. This qualitative review explored individual experiences of psychological treatment for gambling and what may be found helpful or challenging. A structured search was performed using three research databases. Eight studies meeting the inclusion criteria were included. These were analysed using a method called thematic synthesis. Four themes about individual’s experience of psychological treatment for gambling addiction were found: getting the treatment you need is difficult, treatment can make a difference, obstacles along the way, and gaining treatment perspectives. Participants experienced challenges when seeking and accessing psychological treatment. However, it was found that psychological treatment can be helpful. These helpful experiences were not without both practical and internal challenges. Through their lived experiences, participants gained treatment perspectives. Such unique perspectives informed their knowledge and understanding of different gambling treatments and ongoing recovery from gambling addiction. These findings hold clinical implications and future recommendations for research. It was recommended to assess treatment accessibility, availability of support, psychological treatment approaches, helpful techniques, and online treatment delivery, including support networks, and recognising the value of lived experience was considered important. Future research should aim to focus on better quality qualitative studies which explore individual experiences of psychological treatment, comparing various gambling treatments, and reasons why individuals may drop out of psychology treatment. Empirical Project The coronavirus disease 2019 pandemic led to significant impacts on individuals’ daily lives. Individuals living with a gambling addiction were particularly vulnerable in the pandemic. Psychological treatment guidance is currently under development, and qualitative research exploring such experiences in the context of the pandemic is limited. This study aimed to make sense of individual experiences of psychological treatment for adults living with a gambling addiction in the United Kingdom in the context of the pandemic. The study analysed data using a method called interpretative phenomenological analysis. Eight participants took part, and semi-structured interviews were used. Participants were recruited from the Northern Gambling Service and had received psychological treatment since the pandemic. Qualitative findings included three themes: out of control, taking back control, and a gambling shadow remains. Most participants experienced significant negative challenges in their relationship with gambling during the pandemic. Participants sought psychological treatment, which helped them limit their gambling harms. Therapeutic relationships and family support further supported this. Participants spoke about ongoing vulnerabilities in their gambling recovery. Further gambling harms were risked by continued exposure to gambling advertising and limited wider gambling support available. The findings have implications for healthcare and policy. It is important to screen to see if individuals experienced difficulties with their gambling during the pandemic. This research supported the delivery of flexible psychological treatment. Wider support and further reviews of limiting gambling exposure and gambling harms are needed. Future research should explore the experiences of harder-to-reach participants and different treatment options.

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Prevalence of Problem Gambling: A Meta-analysis of Recent Empirical Research (2016–2022)

• Original Paper
• Published: 31 December 2022
• Volume 39 , pages 1027–1057, ( 2023 )

Cite this article

• Eliana Gabellini 1 ,
• Fabio Lucchini   ORCID: orcid.org/0000-0002-6092-1907 1 &
• Maria Elena Gattoni 2  

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Gambling is widely considered a socially acceptable form of recreation. However, for a small minority of individuals, it can become both addictive and problematic with severe adverse consequences. The aim of this systematic review and meta-analysis is to provide an overview of prevalence studies published between 2016 and the first quarter of 2022 and an updated estimate of problem gambling in the general adult population. A systematic review and a meta-analysis were carried out using academic databases, Internet, and governmental websites. Following this search and utilizing exclusion criteria, 23 studies on adult gambling prevalence were identified, distinguishing between moderate risk/at risk gambling and problem/pathological gambling. This study found a prevalence of moderate risk/at risk gambling to be 2.43% and of problem/pathological gambling to be 1.29% in the adult population. As difficult as it may be to compare studies due to different methodological procedures, cutoffs, and time frames, the present meta-analysis highlights the variations of prevalence across different countries, giving due consideration to the differences between levels of risk and severity. This work intends to provide a starting point for policymakers and academics to fill the gaps on gambling research—more specifically in some countries where the lack of research in this field is evident—and to study the effectiveness of policies implemented to mitigate gambling harm.

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Specific Prevention Unit, Agency for Health Protection of Milan, Milan, Italy

Eliana Gabellini & Fabio Lucchini

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Maria Elena Gattoni

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Although this article is the result of a common reflection among the authors, Introduction and Discussion are attributable to FL, Methods and Conclusion are attributable to MEG, Results are attributable to EG.

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Correspondence to Fabio Lucchini .

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Funnel Plots

To assess publication bias, funnel plots were created for the prevalence estimates. As Hunter et al. ( 2014 ) recommended for meta-analyses of proportions, sample size (study size) was employed as measure of accuracy on the y-axis.

Funnel plots suggest a certain asymmetry, mainly due to the consistent heterogeneity characterizing the studies, both for cultural, socio-economic issues related to each national/territorial reality, and for reasons related to the different types of sampling, different methods of administration and screening instruments used (Figs. 6 , 7 and 8 ).

Funnel plot—estimates of problem/pathological gambling

Funnel plot—estimates of problem/pathological gambling without the Mori and Goto study (Japan, 2020 )

Funnel plot—estimates of moderate/at risk gambling

Meta-regression and Subgroup Analyses

A series of subgroup analyses using a random effects model were performed to investigate possible factors explaining the variability in meta-analytic estimates. First, a meta-regression was conducted to test the overall effect of the following moderators on the mean effect size:

origin (European vs. non-European)

screening instrument (PGSI/CPGI vs other instruments

interview method (telephone interview—CATI or other types; online survey; face-to-face survey—CAPI or other types).

The results of this technique are reported in the table below.

• Bold parts are the statistically significant data according to the meta-regression

Regarding problem/pathological gambling, the meta-regression did not yield a significant association for the Origin ( p  = 0.5028) and for the Method ( p  = 0.1601; 0.3696). Concerning moderate risk/at-risk gambling, meta-regression showed no significant association for Origin ( p  = 0.3470) and Measure ( p  = 0.2930).

Below are the analyses for the categories that have yielded a significant association according to the meta-regression: the subgroup analysis by screening instrument for problem/pathological gambling (Fig.  9 ) and the subgroup analysis by interview method for moderate risk/at-risk gambling (Fig.  10 ).

Subgroup analysis for screening instrument (problem/pathological gambling)

The subgroup analysis by screening instrument provides strong evidence of variation. The pooled estimate derived from the PGSI (0.92%; 95% CI 0.57; 1.27) turns out to be significantly lower compared to that of studies employing other screening tools (DSM-IV, SOGS, NODS) (3.04%; 95% CI 0.00; 6.32). This difference can be mainly due to the very high estimate obtained in the study conducted in Japan. There were high levels of between-study heterogeneity in each of these subgroups (I 2 Measure = PGSI: 96.1% and I 2 Measure = other instruments: 99.3%).

Subgroup analysis by methods (moderate risk/at risk gambling )

The subgroup analysis by interview method involves three subgroups: telephone interview (CATI or other types), online survey and face-to-face survey (CAPI or other types). Substantial variations are observed depending on the interview method. In particular, the pooled prevalence of the studies involving face-to-face interview is of a considerably lower magnitude (1.53%; 95% CI 0.40–2.66) compared with the other two interview modes. Studies using online surveys have a value almost twice as high (3.20%; 95% CI 1.45–4.95) compared to face-to-face interviews, while studies using telephone interviews have a high estimate in a middle position between the two modes (2.78%, 95% CI 1.90–3.67). High levels of heterogeneity between studies were also found in each of these subgroups (I 2 Method = Telephone interview 96.6%; I 2 Method = Online survey: 97.8%; I 2 Method = Face-to-face survey: 98.5%).

Below are the analyses for subgroups whose effect was not significant according to the meta-regression (Figs.  11 , 12 and 13 ).

Subgroup analysis by origin—problem/pathological gambling

First, when looking at the world region dichotomy (e.g. European/Non-European prevalence study variable), a higher pooled estimate of the non-European countries (1.64%, 95% CI 0.06; 3.23) is noted compared to the lower result of the European studies (1.06%, 95% CI 0.60; 1.52) (Fig.  11 ).

Subgroup analysis by method (problem/pathological gambling)

There is also statistically significant variation on the interview modes. Specifically, the pooled estimate of the studies that used online survey as the method of collection stands out (2.65, 95% CI 0.00; 6.17) (Fig.  12 ).

Subgroup analysis by origin (moderate risk gambling/at risk gambling)

There was no evidence of systematic variation in the prevalence estimates by origin. An analysis for the moderator method was not performed because in the case of moderate risk and at risk gambling estimates, only two studies employing instruments other than PGSI are available.

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Gabellini, E., Lucchini, F. & Gattoni, M.E. Prevalence of Problem Gambling: A Meta-analysis of Recent Empirical Research (2016–2022). J Gambl Stud 39 , 1027–1057 (2023). https://doi.org/10.1007/s10899-022-10180-0

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Accepted : 26 November 2022

Published : 31 December 2022

Issue Date : September 2023

DOI : https://doi.org/10.1007/s10899-022-10180-0

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Gambling essay sample, example.

Addictions have always been a problem to humanity. Many people tend to explain them as weaknesses, sicknesses, or on the contrary, something not worth attention. People tend to think that addictions are mostly connected to substance consumption; everyone is aware of alcohol or drug addiction, for example. Recently, there have also been talks about Internet addiction, video game addiction, sexual addiction, selfie addiction, and so on. Although they pose a serious threat to one’s mental and physical wellbeing as well, rather often they are not taken as seriously as substance abuse. Among them is also gambling addiction, which can ruin lives, and can be difficult to detect and treat.

So, what exactly is gambling addiction, and why is it considered to be so dangerous? Generally speaking, gambling addiction is a compulsive act of gambling. In other words, occasional gambling is not an addiction; systematic, frequent, and harmful gambling is. Compulsive gambling occurs regardless of a person’s financial status, family’s attitude, or work-related problems; a gambling addict will feel the urge to gamble even if he/she is already bankrupt, divorced, and fired—entirely for the thrill of the act of gambling itself. According to the American National Council on Problem Gambling, only in the United States, there are over two million people who meet the criteria of pathological gambling (meaning full-scale addiction), and about five more million whose gambling habits can be described as “problem gambling” ( LiveStrong.com ).

So, there is “healthy” gambling (meaning a gambling person does it for fun, has full control over this activity, and never harms themselves or other people through gambling, usually stopping when a money loss limit is reached, or earlier), and there is compulsive gambling; the latter possesses a number of attributes which allow to diagnose it rather accurately. These attributes are: constantly thinking about gambling, or about where to find more money to gamble (including theft and fraud); asking other people for money to continue gambling; gambling in an attempt to recover lost money; similarly to substance addiction, a pathological gambler needs the increasing amounts of money to feel the same thrill; gambling mostly is done to cope with difficult feelings such as anxiety, guilt, depression, or to get distracted from existing problems (including the gambling problem as well); lying to one’s family members about the scales of one’s gambling, or about the fact of gambling itself; losing precious relationships, jobs, reputations, and so on because of gambling ( MayoClinic ).

As it can be seen, gambling possesses attributes rather typical for any kind of addiction, so the reasons standing behind it may also resemble those causing other forms of addictive behavior. In particular, gambling may help a person escape from feelings of depression and anxiety; a gambler may dream of winning a significant sum of money, thus instantly increasing their own self-esteem, reputation, financial status, and achieving the sensation of accomplishing something important in life. Escaping from mundane reality may also be the subconscious purpose of a gambler; shiny casinos, loud arcades, being surrounded by people who occasionally actually win money—all this can create an illusion of welfare, luxury, and belonging to an elite society. Or, as it is in human nature to look for excitement (meaning thrilling or pleasant emotions and “the taste of life” they cause), gambling is often seen as a source of such emotions. Anticipating a jackpot, a gambler’s body produces large amounts of hormones responsible for pleasure and thrill (dopamine and adrenaline, for instance) causing a natural “high” not too much different from the one caused by substances. Besides, western society tolerates gambling much more than other forms of addiction, such as alcoholism or drug abuse. In fact, gambling is often seen as something thrilling but not dangerous, and mass media and advertising agencies only contribute to this image, producing pictures of a fashionable and stylish life connected to gambling; besides, many young people get introduced to gambling at a rather early age—for example, by playing cards or bingo with their parents; these family activities may look rather innocent, but it is important to remember they may also help a young person develop addiction at some point ( HealthyPlace ). If possible, it is better for parents to spend time with their children in some other ways.

Gambling is a form of addiction no different from substance abuse. It is a huge problem for the western world—just in the United States, there are roughly seven million people with varying degrees of pathological gambling behavior. Possessing a number of symptoms similar to less tolerated forms of addiction such as drug abuse, gambling is still seen as a relatively harmless activity. Mass media portrays gambling as an element of luxury, and many people having personal problems and trying to escape from them visit casinos, attempting to run away from their mundane lives. American society would benefit from gambling being treated as a form of behavior that can cause harm to both gamblers and their family members and friends, as it is already happens with alcoholism or drug addiction.

Works Cited

• Bergeson, Boyd. “What Causes Gambling Addiction?” LIVESTRONG.COM. Leaf Group, 17 Aug. 2015. Web. 24 Apr. 2017.
• “Compulsive Gambling.” Mayo Clinic. Mayo Foundation for Medical Education and Research, 22 Oct. 2016. Web. 24 Apr. 2017.
• Gluck, Samantha. “Psychology of Gambling: Why Do People Gamble?” HealthyPlace. N.p., n.d. Web. 24 Apr. 2017.

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The causes of gambling addiction: an examination of what characteristics and ways of thinking drive gambling issues

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‘Getting addicted to it and losing a lot of money… it’s just like a hole.’ A grounded theory model of how social determinants shape adolescents’ choices to not gamble

• Nerilee Hing 1 ,
• Hannah Thorne 2 ,
• Lisa Lole 1 ,
• Kerry Sproston 3 ,
• Nicole Hodge 3 &
• Matthew Rockloff 1  

BMC Public Health volume  24 , Article number:  1270 ( 2024 ) Cite this article

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Gambling abstinence when underage lowers the risk of harmful gambling in later life. However, little research has examined why many young people refrain from gambling, even though this knowledge can inform protective strategies and lower risk factors to reduce underage gambling and subsequent harm. This study draws on the lived experience of adolescent non-gamblers to explore how social determinants while growing up have shaped their reasons and choices to not gamble.

Fourteen Australian non-gamblers, aged 12–17 years, participated in an in-depth individual interview (4 girls, 3 boys) or online community (4 girls, 3 boys). Questions in each condition differed, but both explored participants’ gambling-related experiences while growing up, including exposure, attitudes and behaviours of parents and peers, advertising, simulated gambling and motivations for not gambling. The analysis used adaptive grounded theory methods.

The grounded theory model identifies several reasons for not gambling, including not being interested, being below the legal gambling age, discouragement from parent and peers, concern about gambling addiction and harm, not wanting to risk money on a low chance of winning, and moral objections. These reasons were underpinned by several social determinants, including individual, parental, peer and environmental factors that can interact to deter young people from underage gambling. Key protective factors were parental role modelling and guidance, friendship groups who avoided gambling, critical thinking, rational gambling beliefs, financial literacy and having other hobbies and interests.

Conclusions

Choices to not gamble emanated from multiple layers of influence, implying that multi-layered interventions, aligned with a public health response, are needed to deter underage gambling. At the environmental level, better age-gating for monetary and simulated gambling, countering cultural pressures, and less exposure to promotional gambling messages, may assist young people to resist these influences. Interventions that support parents to provide appropriate role modelling and guidance for their children are also important. Youth education could include cautionary tales from people with lived experience of gambling harm, and education to increase young people’s financial literacy, ability to recognise marketing tactics, awareness of the risks and harms of gambling, and how to resist peer and other normalising gambling influences.

Peer Review reports

Most research into gambling amongst adolescents has focused on the prevalence and predictors of harmful gambling [ 1 , 2 ]. Since early engagement in gambling is a risk factor for gambling problems in adulthood [ 3 , 4 ], studies have also examined the reasons that adolescents participate in gambling when underage [ 5 , 6 ]. However, little attention has focused on understanding why many young people refrain from gambling. Approximately 50–70% of adolescents report no past-year gambling [ 7 , 8 ], even though underage access to many gambling products is reportedly easy [ 9 ]. Understanding why these adolescents choose to refrain from gambling can inform protective strategies against underage gambling and subsequent gambling harm.

Numerous theoretical models identify the key types of influences on youth developmental outcomes [ 10 , 11 ], health outcomes [ 12 , 13 ], and the development of gambling behaviours and subsequent harms [ 14 , 15 , 16 , 17 ]. These models all recognise that these behaviours and outcomes are influenced by complex interactions between multiple factors (e.g., individual attributes; physical, cultural and social circumstances) and at multiple levels (e.g., individuals, relationships, organisations, society). This recognition that multiple and multi-level factors impact on health behaviours and outcomes can inform an understanding of how various influences interact to shape young people’s decisions to refrain from gambling.

Young people’s self-reported reasons for not gambling

To our knowledge, only two survey studies have examined reasons for not gambling amongst young people. Rash and McGrath [ 18 ] conducted a content analysis of responses to an open-ended survey question asked of 196 Canadian undergraduates (mean age = 21.2 years, SD  = 3.7) who reported no past-year gambling. They were asked to ‘think about what motivates you to NOT gamble and briefly list the top three reasons in rank order.’ The most common motive was financial reasons and risk aversion (33.1%), followed by disinterest/other priorities (21.1%), personal and religious objections (12.2%), addiction concerns (9.6%), influence of others’ values (9.1%), awareness of the odds (8.9%), lack of access, opportunity or skill (2.1%) and emotional distress (1.7%).

Another study focused specifically on young people under the legal gambling age [ 7 ]. It surveyed a weighted sample of 2559 students aged 11–16 years in England, Scotland and Wales. Those who reported no past-year gambling were asked: ‘You said that you have never gambled or never spent your own money on gambling. Why is that?’ and were provided with multiple response options. The most endorsed reasons were lack of interest in gambling (39%), because it is illegal or they thought they were too young (37%), not wanting to play with real money/rather play with free games (25%), not being allowed to gamble by their parents (24%), and because it may lead to future problems (22%). Less common reasons were expecting to lose more than they will win (21%), because they ‘don’t agree with gambling and/or it is not right’ (21%), thinking they were unlikely to win money (19%), not knowing enough about gambling games (11%) and religious objections (10%). Girls tended to report less interest in gambling, while boys were more likely to cite that gambling may lead to future problems. Younger participants were more likely to endorse that they did not agree with gambling and that their parents do not allow them to gamble. These findings align with observations that adolescent non-gamblers tend to be female and younger, compared to adolescent gamblers [ 19 , 20 , 21 ].

Social determinants of adolescent non-gambling

Social determinants of health are the non-medical factors that influence health outcomes [ 22 ]. Several social determinants may directly and indirectly shape the reasons for not gambling that many young people report, although this linkage has not previously been examined. Nonetheless, studies that compare non-gamblers to gamblers amongst adolescents provide some insights into social factors associated with non-gambling.

In a survey of 506 students from six schools in South Australia (mean age = 16.5, SD  = 0.77 years), non-gamblers rated gambling as more unprofitable, compared to gamblers, and were significantly less likely to have family or friends who approved of gambling or who gambled a lot [ 23 ]. In another Australian study of students aged 12–17 years in Queensland and Victoria ( N  = 6377), those who had not gambled in the past month were significantly more likely than past-month gamblers to report having less spending money available, lower alcohol consumption, less exposure to gambling advertisements, and fewer peers or family members who had recently gambled [ 20 ]. Also in Australia, unique predictors of past-year non-gambling identified in two non-probability samples of youth aged 12–17 years ( N  = 826, N  = 843) were parental disapproval of gambling, not gambling with their parents while growing up, not having friends who gambled, and avoidance of simulated gambling [ 8 ].

In New Zealand, Rossen [ 21 ] surveyed students from 12 secondary schools ( N  = 2005; mean age = 15.2 years, SD  = 1.45). Compared to gamblers, non-gamblers tended to have lower rates of internet and computer game usage, alcohol usage, and recall of seeing gambling advertising. They were also less likely to have family members or friends who gambled or had a gambling problem. Further, less liberal attitudes to gambling, lower perceived ease of access to gambling, and lower perceived role of skill in gambling were associated with non-gambling status. Non-gambling was also associated with being required to contribute to household chores, higher importance of spiritual beliefs, higher parental attachment, trust and communication, and lower maternal, paternal and peer alienation.

In the US, a survey of 15,865 eighth-graders in Oregon (mean age = 13.7 years, SD  = 0.50) focused on health behaviours, including gambling during the previous three months [ 24 ]. Good personal safety habits, non-involvement in antisocial behaviour, and strong personal health beliefs predicted non-gambling in both girls and boys. Amongst girls, non-gamblers were also more likely than gamblers to report less screen time on school nights, no tobacco use, and to speak English at home. Amongst boys, living in neighbourhoods with strong social control and non-Hispanic ethnicity also predicted non-gambling. Also in North America, a study of students aged 13–19 years in Canada ( N  = 10,035) found that non-gamblers were less likely to engage in simulated gambling, compared to those who gambled [ 19 ].

In summary, two studies have examined qualitative self-reported reasons given by young people for not gambling, while quantitative research identifies social factors that differ between adolescent non-gamblers and gamblers. However, a detailed exploration linking reasons for not gambling with social factors is lacking. This study therefore aims to draw on the lived experience of adolescent non-gamblers to explore how social determinants can shape their reasons and choices to not gamble as they grow up.

We use a grounded theory methodology in this study, which is appropriate when a research topic lacks a theoretical foundation. This approach allows us to expand upon previous reasons that adolescents report for not gambling to also identify underlying social determinants and processes. The study was approved by our institutional ethics committee (number 23,445).

Recruitment

Participants were adolescents aged 12–17 years who lived in NSW and provided their own and their legal guardian’s informed consent. Due to ethical concerns surrounding anonymity, confidentiality, and minimising legal risk to underage participants, detailed information on participants was not collected. Sampling ensured reasonably even representation from younger (12–14 years) and older (15–17 years) ages, boys and girls, as well as regional and metropolitan locations (Tables  1 and 2 ).

Parents/guardians in the recruitment agency’s database were the initial point of contact to recruit the adolescents to participate in either an interview or online community. The funding agency requested these options be offered, based on the rationale that the strengths and weaknesses of each method would complement each other. The parents were contacted via email with an information sheet and invited to ask their adolescent to complete a brief online recruitment screener, which included questions confirming no past-year adolescent gambling, basic demographics, and confirmation of their and their parent’s consent to participate in the study. Eligible candidates were fully informed of what was expected of them, that their participation was entirely voluntary, and that they were free to withdraw from the study at any time without penalty.

Data collection

Seven participants opted for an interview. The interviews, each lasting about 45 min, explored each participant’s gambling-related experiences during their childhood and adolescence. Participants were asked about their exposure to gambling, attitudes to and participation in gambling while growing up, factors that facilitated or hindered any gambling, motivations for not currently gambling, the impacts of gambling on their lives, their family and social environments, their experiences with simulated gambling, and protective factors. Supplement A contains the full list of questions. Participants were compensated with an AU$60 GiftPay voucher.

Seven additional participants participated in an online community. The online community was convened over seven days, using the Visions Live platform which resembles a social media platform. Participants were asked to participate for about one hour each day in activities and discussions designed to capture their gambling-related experiences while growing up. Nine topics were covered: (1) gambling behaviours and attitudes; (2) parental and family gambling attitudes and behaviours; (3) peer influence; (4) gaming and simulated gambling; (5) their ‘gambling journey’, including key milestones and influences over time; (6) gambling advertising; (7) gambling harms; (8) protective strategies; and (9) future gambling intentions. Supplement B contains the full list of questions. All participants used anonymous avatars. Tiered compensation was based on the number of days they participated, with a maximum of AU$140 in GiftPay vouchers available.

Individual interviews enabled an in-depth oral and narrative account of developmental influences on each participant’s choice to not gamble, while the online communities enabled participants to consider their answers over a more extended time period, to share information on sensitive topics in an anonymous way, and to discuss the topics with the other participants. While the format of questions was adapted to suit the conversational vs. written format of these activities, all were designed to address the same research aims so the two datasets were combined for analysis.

An adaptive grounded theory method was used which combines inductive and deductive analysis [ 25 ]. We used inductive methods to initially openly code and analyse emergent findings from the data, which were also informed by the literature review on sources of influence on young people’s gambling (parents, peers, marketing, etc.). After data familiarisation, we used the constant comparative method to code phrases, sentences and paragraphs in the data to identify relevant features, refine the codes as the analysis progressed, and group and collapse similar codes into broader themes. For example, codes related to ‘parents not gambling,’ ‘parents talking about gambling risks and harm,’ and ‘parental restrictions’ were grouped into a broader theme of ‘parental modelling, rules and guidance shape gambling attitudes and behaviours.’ Deductive consolidation of themes into multiple levels of influence was informed by a public health, socio-ecological systems approach [ 12 , 13 ] to understand the complex multifaceted nature of factors that contribute to adolescent gambling beliefs, behaviours and attitudes. This process allowed us to identify meaningful patterns in the data. While there were some differences in wording and phrasing of codes between the researchers at the preliminary, inductive stages of data analysis, there were no conflicts when consolidating and coding themes in later stages of the analysis.

Trustworthiness of the research was enhanced by collecting data from participants with lived experience, using open-ended questions, and allowing participants to have control over the experiences they shared. Multiple researchers reviewed each analysis draft to ensure confirmability. Participants’ quotes increase authenticity. These are tagged by gender (male, female), age group in years (12–14, 15–17), and data collection method (IDI = interviews, OLC = online community).

Eight themes emerged from the analysis that were grouped into four socio-ecological levels (Fig.  1 ). Environmental influences that shaped reasons for not gambling included age restrictions on gambling. Peer influences comprised having friendship groups with little interest in gambling. Parental influences entailed parental modelling, rules and guidance. Individual factors included having other interests and having little interest in sport, financial literacy and financial priorities, fear of addiction and harmful consequences, reasoned perceptions about gambling and critical evaluations of advertising, and caution about simulated gambling. These influences underpinned several reasons for not gambling articulated by the participants (Fig.  1 ).

Social determinants of reasons for not gambling amongst adolescents

Age restrictions are seen as an unequivocal barrier to gambling

In Australia, it is illegal for people under 18 years to gamble on commercial gambling products. Nearly all participants were quick to note that being under the legal gambling age was the most obvious deterrent to them gambling. They appeared to accept these age restrictions as an unequivocal barrier, based on an implicit trust that the rules exist for a reason: ‘I always… thought that it’s a grown-up thing’ (#1, male, 15–17, IDI). No participants indicated any interest in circumventing age requirements for gambling, even though this was said to be easy:

[Young people] probably could easily get a fake licence or ID, could probably influence an adult or an adult wants to let them into this… [and] some places don’t have the best security in the front entrances, so someone could probably sneak in if they looked a bit older. (#8, male, 12–14, OLC)

Participating only in age-appropriate activities was also an expectation set out by their parents. These young people appeared eager to meet their parents’ expectations and to not break any rules. Accepting that gambling when underage was forbidden was said to lower their interest in gambling.

I don’t gamble because I don’t find it interesting and it is illegal for someone my age, my parents would not want me to gamble. (#13, female, 15–17, OLC) I’ve always been told to not go anywhere near it. I mean I’m also underage so not allowed to, but then it’s also like I’ve always been told that it’s bad and that you could lose a lot of money. (#4, female, 15–17, IDI.)

Parental modelling, rules and guidance shape gambling attitudes and behaviours

In the current study, parental influence was said to be critically important in shaping the participants’ gambling attitudes and behaviours from early childhood onwards. Most participants reported that their parents did not gamble or did so only occasionally. This limited parental gambling was usually associated with having negative opinions of gambling which, in turn, were said to shape the young person’s attitudes and behaviours.

My parents always despised gambling as my uncle wasted all his money on it and went off the rails. So that early instilling of the bad rep of gambling has stuck with me. (#10, male, 15–17, OLC) I think that my parents don’t gamble, and don’t have anything good to say about gambling, has influenced me a lot… Parents think it’s a waste of money as much more likely to lose money than win it… it makes me feel like it’s all fake and everyone who goes there comes back home with empty pockets. (#12, female, 12–14, OLC)

Because the participants tended to recognise how their parents’ opinions, advice and behaviour have influenced their own aversion to gambling, some were highly critical of parents who gambled in front of children.

It sucks that people think it’s ok to do this kind of stuff around kids, who are largely influenced by their parents, as they will view them as heroic figures, and will adopt these bad traits onto themselves. (#8, male, 12–14, OLC)

As well as protecting their child from socialisation into gambling through the family, educating them on the risks and harms of gambling was another protective parental influence that participants recalled. They typically recounted that early childhood messages from their parents focused mostly on conveying a general disapproval of gambling, and then progressed to more detailed conversations about gambling risks and harms as the participants became older. They particularly remembered the cautionary tales that their parents related, usually during the participants’ early adolescence when their exposure to gambling was increasing. These conversations were often reactive, in response to an external cue such as a gambling advertisement. Participants recalled being especially responsive to stories based on real experiences.

My mum is a police officer, so I’ve heard… stories about the dark sides of gambling… and getting addicted to it… [Gambling] hasn’t really interested me that much because I know what can go wrong. (#1, male, 15–17, IDI)

Some participants reported that witnessing harm from gambling made an impression by raising their awareness of the likelihood of gambling losses and the risk of addiction.

I know now that… you’re more likely to lose lots of money than win lots of money… when I saw my Pop losing heaps of money, I’m like, ‘Oh, it’s not all win, win, win.’ (#2, male, 12–14, IDI). Going to Las Vegas, seeing people betting and all the machines… It made me realise how addicted people are. (#12, female, 12–14, OLC) On a school excursion, we had a guest speaker who had experienced gambling… he had taken money out of his workplace… then gambled the money… then he was trying to get it back through gambling… his experience of how that really forced him to experience a lot of hardship with his family and trying to find support with that. So, I’d seen, through those kind of things, the ways that it can negatively impact on people and the way that you can lose control. (#5, female, 15–17, IDI)

Most participants reported parental monitoring and control over their gambling, online gaming and simulated gambling. One participant described how his parents had a ‘no gambling’ rule, and another reported that his mother monitored and limited his spending on in-game items when playing video games. Some parents were also aware of simulated gambling elements in online games and were cautious about their child’s engagement.

It looked like a pokies machine. That’s why my mum was concerned with me playing it because you pulled down the lever and the thing spun, and then if you collected three of those things then you got a reward. (#4, female, 15–17, IDI)

Protective influences from friendship groups with little interest in gambling

As young people enter and advance through their teenage years, peer influences on gambling tend to become more significant. However, while the participants recognised that peer influences could encourage gambling, most reported that their friends did not gamble or that gambling was not part of the interests, activities or conversations in their friendship groups: ‘Me and my friends never really bring up the topic “gambling” and I have never seen them talk about it to anyone else’ (#11, female, 12–14, OLC).

One participant explained that the moral values associated with her cultural background were her main deterrent. Having friends with a similar background also limited her interest in gambling because this friendship group shared other hobbies.

My friends come from backgrounds where gambling is highly discouraged and they have carried that out through our friendship, we don’t talk about gambling often and so I tend not to associate with it, this has also discouraged me from gambling. We have other interests and activities to do that don’t involve gambling. (#13, female, 15–17, OLC) Peers were also said to influence the participants’ attitudes to gambling through vicarious experiences of gambling losses. For example, this participant reported that seeing or hearing about friends losing increased his awareness of the negative consequences that gambling could have: ‘I saw my friends… if they lost then they’d be all like upset… so I started to see like the downsides of it as well’ (#1, male, 15–17, IDI). Some older participants noticed increased peer involvement in gambling in their later teens, alongside more opportunities to gamble. However, the attendant risks appeared to be offset by other environmental, parental and peer protective factors.

Having other interests, and little interest in sport

Many participants discussed how having other hobbies and activities left them with little time or interest in gambling. These activities included dancing, painting, drawing, music and skateboarding, which they might do alone or with friends: ‘My activities outside of school keep me occupied and less likely to take an interest in gambling’ (#12, female, 12–14, OLC). Alternatively, some participants commented that gambling could distract young people from more productive interests and pursuits. Participants recognised that having gambling-related interests might override an adolescent’s interest in other activities, including schoolwork: ‘People start gambling from a young age and set this as their future job [instead] of… focusing on school and their studies and setting a good career’ (#11, female, 12–14, OLC).

Further, an interest in following professional sport was said to expose young people to gambling influences and act as a ‘gateway’ to an interest in gambling. Some participants commented that their own lack of interest in sport helped to protect them from frequent exposure to betting influences and activities. They did not see the point in betting on sporting competitions that they had no interest in. Other participants did report an interest in sport but resisted its gambling influences, possibly due to other protective factors such as parental influences.

Financial literacy and financial priorities

Numerous participants referred to gambling as ‘a waste of money’, a view most said had been conveyed by their parents. These adolescents did not see the point of engaging in chance activities where they risked losing their money: ‘Why waste your money on something that won’t necessarily work?’ (#14, female, 15–17, OLC). Several explained they understood there was a greater chance of losing than winning.

If I were to work hard every day, I would not want to waste it on a low chance of winning more and a high chance of losing most of my money… The closest thing I have done to gambling is just carnival stuff. (#8, male, 12–14, OLC)

These participants typically reported they had better things to spend their money on, both now and in the future. Older participants, in particular, appeared to have a well-developed sense of financial literacy, financial responsibility and future orientation. They believed that their appreciation of the value of money had been instilled by their parents. The following participant’s views on money demonstrate her high level of financial responsibility and her financial priorities that discouraged her from gambling.

I’m very like cautious about where my money goes… I don’t want to lose a lot of money because I like to save all of my money… I very much like to keep my money, because I love to travel and at the end of school, I want to travel around the world a bit. And then I also need to save up for uni and everything, because I don’t want to have a lot of debts… I very much like to know where my money is… Because money is very valuable, especially now when houses cost like tonnes of money, and you need to save up to buy a lot of things, and like inflation is making things more expensive. (#4, female, 15–17, IDI)

Fear of addiction and harmful consequences

Participants reported that fear of addiction and the negative consequences of gambling were powerful deterrents. They recognised a wide range of potential harms, including to finances, relationships, mental health, anti-social behaviour and vocational performance.

Gambling at this age can also lead to higher rates of depression and anxiety, loss of friendship with non-gambling peers, and can also take you away from your family… taking money from your parents, changes in sleep patterns, low energy levels, changes in mood, and can be involved in risk-taking behaviour like fights, vandalism or shoplifting. (#11, female, 12–14, OLC) Getting addicted to it and losing a lot of money… using possessions and stuff even, betting those when you have nothing left even. Like, it’s just like a hole (#1, male, 15–17, IDI).

There was widespread recognition in this cohort that, while gambling harm could be immediate, it could also have long-lasting impacts. Participants tended to view the harm from gambling as extreme and potentially life-changing: ‘it can ruin lives and families, it puts people in debt and ruins whatever they have built their life up to’ (#8, male, 12–14, OLC).

Reasoned perceptions about gambling and critical evaluations of advertising

Amongst the participants, rational beliefs about gambling were evident, particularly in their understanding of the relative chances of winning and losing. Even though some acknowledged the appeal of gambling, they resisted its excitement and financial opportunity because they were aware of the likelihood of losing.

I can see how gambling might be fun due to the adrenaline it can produce or the money which someone could gain, but in my opinion the risk is not worth it. (#9, male, 15–17, OLC)

More commonly, participants said that they were just not interested in gambling, which they often attributed to their rational mindset and ability to think critically, as well as parental advice on how gambling works.

I’m kind of a person who’s very interested in things… ‘So how does it work? What are the odds– how like the statistically point whatever percent of people win something?’ And dad will bring up those things and you go, ‘Why do people even play that? It just seems silly’… When you’re saying there’s an opportunity to get millions of dollars, you’d be like, ‘Of course I want that.’ But… ‘what are the odds of that?’ It’s pretty slim. (#5, female, 15–17, IDI)

Some participants noted an increased awareness of gambling risks and harms as they got older, due to their increased “mental capability” (#5, female, 15–17, IDI). Several participants reported that they applied their critical thinking skills when considering the design of gambling products and their marketing. They felt in control of their choices and able to see through promotional messages about gambling. Two participants mentioned an interest in the psychology of advertising, which they felt helped them resist the appeal of gambling.

As a design student, and looking at the way designers and marketers will try and advertise and appeal to people, I think it’s allowed me to pick up on those things and understand why they’re doing some of the things they’re doing to try and engage an audience in a certain way. (#5, female, 15–17, IDI)

These participants also recognised that the design of gambling environments, including their sounds, lights and colours, is an industry tactic to encourage people to gamble. Some participants recalled being attracted to and intrigued by the design features of gaming rooms they saw as children when they dined at a venue with their family.

I could hear the noises, and I could hear, like, the sounds of the money… then when people opened the doors, I saw the colourful lights and I was, like, ‘Oh, I want to go in there,’ because, you know, I was a kid– it’s colourful. (#4, female, 15–17, IDI)

Caution about simulated gambling

Like most young people, many participants regularly played video games, including games with simulated gambling elements such as loot boxes and wheel spinning. However, they tended to view spending real money in games, including on simulated gambling features, with a great deal of caution and had very low expectations of a worthwhile return. Some also recognised the potential for addiction to gaming and that simulated gambling could encourage young people to engage in monetary gambling.

Spending real money for skins and things is practically gambling… By spending money on skins and things worth no real-life value, the same person might be interested in spending money gambling with the chance to get real life money… A great example is the FIFA video game franchise, whereby you can either purchase ‘packs’ with an in-game currency or real money. Many of my friends decided to use in-game currency until they ran out but by then they were hooked and resorted to using their real money. (#9, male, 15–17, OLC)

The participants reported that their engagement in simulated gambling had not aroused temptations to engage in monetary gambling. However, they believed that other young people might not be so resistant. They saw the potential for simulated gambling to be a ‘gateway’ to real-world gambling, and that its heavy marketing and targeting of young people were harmful. Many were highly critical of the proliferation and extensive advertising of simulated gambling games, including through sponsored online influencers who typically show young people winning on these games in order to encourage real-money expenditure and persistent play.

They show in ads all the time, people just winning constantly but never really show how much money people use and how they get nothing in return and somehow people fall for the trick thinking that they will get loaded with money. I think the game/apps are worse because it shows that they’re winning a lot, which makes people play it more and that’s when the addiction begins. (#12, female, 12–14, OLC)

Grounded theory model

Figure  1 presents the grounded theory model derived from the study’s findings. Key findings are discussed below.

This study has provided insights into the lived experiences of adolescents who refrain from gambling and how numerous social determinants when growing up interact to shape their reasons and choices to not gamble. As Fig.  1 indicates, the participants’ accounts highlight several reasons for not gambling. This study, and previous research, identify not being interested in gambling, being below the legal gambling age, discouragement from parent and peers, concern about gambling addiction and harm, not wanting to risk money on a low chance of winning, and moral objections, as reasons that some young people do not gamble [ 7 , 18 ]. Unlike earlier research, however, no participants cited lack of access or opportunity as a reason for refraining from gambling. This may reflect the widespread availability of gambling in Australia, including through online and mobile devices and thousands of land-based venues, and opportunities to engage in private gambling.

Figure  1 also identifies several social determinants that provide deeper insights into factors that underpin the participants’ reasons for not gambling. In line with a socio-ecological perspective on health behaviour [ 12 , 13 ], these social determinants include multiple layers of influence.

Parental factors appear to be the main formative influence on the participants’ gambling. Research has consistently found that parents play a crucial role in transferring gambling attitudes, knowledge and skills to their children, in educating them on the risks and harms of gambling, and in restricting their gambling and online activities [ 2 , 8 , 26 ]. Qualitative research has drawn on social learning theory to explain how parents can transfer knowledge and skills to their children, so that they learn how to gamble and assign positive meanings to the activity [ 27 – 28 ]. The current study shows how parents can have a converse effect through role modelling and other protective influences that deter their children from gambling. Parents were said to convey negative attitudes towards gambling, discourage gambling by their children, engage in no or limited gambling themselves, and advise their adolescents on the negative consequences of gambling. By limiting their own gambling, these parents helped to protect their children from being exposed to and involved in gambling, and from learning to gamble during childhood [ 27 , 28 ]). Moreover, while harmful parental gambling increases the risk of gambling problems in children [ 8 , 29 ], being exposed to harmful consequences in others, outside the nuclear family, may instead have an educative effect.

Social learning also occurs through peers, particularly in early and later adolescence, when friendship groups can introduce young people to gambling activities, encourage them to gamble, and provide the social rewards of in-group status and peer bonding [ 30 , 31 ]. Peers can influence an adolescent’s gambling behaviour, depending on how normalised, encouraged or discouraged gambling is in their social group [ 30 ]. The current research found that when gambling is not an accepted or shared activity in friendship groups, peers can be a discouraging influence on gambling through their disapproval and avoidance of gambling and by sharing other non-gambling interests.

Environmental factors also shape youth gambling behaviour. Age restrictions on gambling are an important deterrent, as found in this and previous research [ 7 ]. While these age restrictions apply to all underage adolescents, the non-gamblers in this study accepted them as an unequivocal barrier, even though other adolescents might choose to circumvent them. This suggests that it is not just the presence of these restrictions, but instead how young people respond to them, that impacts on their subsequent gambling involvement. These responses may reflect more generalised attitudes to compliance with rules and parental restrictions. Nonetheless, better enforcement of age and identity requirements may further assist in preventing gambling by minors, given that underage access to some commercial gambling products is reportedly easy [ 9 ].

Previous studies have also examined other environmental influences on youth gambling, although mainly in relation to those that encourage gambling. A key focus has been on the role of advertising in fostering youth gambling [ 8 , 32 , 33 , 34 ] and how simulated gambling can normalise and be a training ground for monetary gambling [ 35 , 36 ]. Like most young people in Australia, the adolescent non-gamblers in this study reported widespread exposure to gambling advertising and simulated gambling [ 8 , 20 ]. However, many explained they were sceptical about gambling marketing claims and cautious about simulated gambling, particularly spending real money on this activity. This reasoned and critical thinking about industry tactics and the odds of winning were said to temper their responses to these marketing influences. Other Australian research has found that children are exposed to and can recall the sights and sounds of gambling in venues, even when gambling products are in restricted areas [ 37 , 38 ].

Social connectedness, fostered by extracurricular activities, positive parent-child relationships and pro-social behaviour, is said to lower the likelihood of youth gambling [ 2 ]. Many participants also had little interest in professional sport, so they may be somewhat protected from the associated advertising and other gambling influences that occur when people watch sports broadcasts and share an interest in sport with family and friends [ 39 , 40 ]. Consistent with previous research [ 41 ], having gambling-related interests might override an adolescent’s interest in other activities, including schoolwork. However, it is unclear whether the social connectedness and diversion of having other hobbies and interests is a cause, consequence or co-occurring feature of gambling involvement. Research into adolescents who watch sports but do not gamble is required to better understand factors that help them resist gambling influences in this context.

Several individual factors were implicated in the reasons these young people refrained from gambling which, in turn, may have been shaped by factors such as their personality, parental discipline and friendship groups. Aligned with their tendency for reasoned and critical thinking, the participants saw gambling as a waste of money because of the low chances of winning. Instead, they prioritised spending their money on other interests or tangible goods, and older participants tended to have savings goals for future acquisitions and activities. Research has consistently found a significant relationship between erroneous gambling cognitions and gambling problems in youth and, conversely, the protective influence of rational gambling beliefs [ 42 , 43 , 44 , 45 ]. Financial literacy, that is, being able to make effective decisions about expenditure, saving and budgeting, has an inverse relationship with gambling frequency [ 46 , 47 ], and may therefore deter young people from gambling. The participants’ awareness that people are most likely to lose at gambling was often instilled by parents, who also conveyed cautionary tales and guidance that gambling could lead to addiction and harmful consequences. These young people appeared to take these messages seriously and were fearful that gambling would lead to life-changing harms. Participants recognised a wide range of potential harms, including to finances, relationships, mental health, anti-social behaviour and vocational performance, as also identified in models of gambling harm [ 15 , 48 , 49 ]. Overall, the participants indicated little interest in gambling and instead reported having a wide variety of other interests that they pursued alone or with family and friends. This aligns with previous findings that extra-curricular activities and social connectedness are protective influences for youth gambling [ 2 ].

Several implications arise from the study’s findings. Protective factors implicated in the participants’ reasons and choices to not gamble emanated from multiple layers of influence. This implies that multi-layer interventions, in line with a public health response, are likely to be optimal in deterring underage gambling– including to young people who already gamble and are likely to experience gambling harm. While not all risk and protective factors for gambling and gambling harm are modifiable, those suggested here are practical strategies aimed at preventing and reducing harm amongst young people. At the environmental level, better age-gating for both monetary and simulated gambling, along with less exposure of children to promotional gambling messages, can help protect to young people who might otherwise struggle to resist these influences. Since young people are less able to critically assess gambling marketing, regulation to prevent the advertising of gambling to children and adolescents is a vital strategy [ 50 ]. Interventions that support parental role modelling and guidance for their children can include raising awareness about how parents influence their children’s gambling, and the provision of advice and resources they can use to deter them [ 27 , 51 ].

Youth education is also needed using evidence-based programs. The current study indicates that potentially useful elements include cautionary tales based on the lived experience of people harmed by gambling, as well as education to increase young people’s financial literacy, ability to recognise marketing tactics, awareness of the risks and harms of gambling, and how they might resist peer and other normalising gambling influences. Youth gambling education programs should also be informed by previous research evidence. For example, a systematic review of behavioural change techniques directed at youth gambling indicate that the most successful programs include information of the harm from gambling to relationships, finances, and mental health [ 52 ]. Donati et al. [ 44 ] found that a brief, online, school-based psychoeducational intervention, that comprised a gambling-specific skills training program, increased awareness about gambling, undermined gambling-related cognitive distortions, and reduced gambling frequency and gambling problems.

Naturally, this study has limitations. It focused on gathering in-depth information to provide detailed insights into the lived experiences of a small sample of participants, so the findings may not be generalisable to all adolescent non-gamblers. Data saturation may not have been achieved, and future research could obtain larger samples. The findings may also be influenced by social desirability bias and recall bias, although the memories people have and how they interpret them are likely to influence their subsequent attitudes and behaviours. Given that participants were compensated for their time, the sample may also be skewed towards adolescents who had a greater need for money. A self-selection bias due to the need for parental consent may be present, as consenting parents may have attitudes to gambling and parenting approaches that differ from the broader population. Future research could examine whether this bias exists and how differences in parent-child relationships and attachment styles affect the learning about gambling that occurs in childhood and adolescence. It is also possible that some parents may have monitored their child’s responses to the OLC activities and impacted their responses, or that OLC participants sourced other information to inform their responses.

Future research could collect more detailed demographic data to better understand how adolescents’ decision to not gamble intersect with factors such as socioeconomic status, family circumstances, health, ethnicity, religious beliefs, and school grades, which have been implicated in pathways into gambling and gambling harm [ 29 , 53 , 54 , 55 , 56 , 57 , 58 ].

Grounded theory methodology is necessarily subjective in nature, with the findings shaped by how participants interpret and share their experiences and how the researchers interpret the data. While generalisability is therefore limited, the current study helps to advance understanding beyond simple self-reported reasons for not gambling to identify multi-layered social determinants and processes that can underpin these reasons amongst young people. The resulting grounded theory can inform protective strategies and further research. This study found that the most potent social determinants of non-gambling were from the individual, parental and peer levels. Research that explores how social and commercial determinants at the community, systems, industry and societal levels impact on young people’s gambling choices would also be valuable.

This study has provided a detailed exploration of adolescent non-gamblers and how their reasons and choices to not gamble are shaped by social determinants as they grow up. It concludes that multiple factors and layers of influence interact to deter young people from underage gambling. While the environmental factor of age restrictions on gambling is an important deterrent, parental influences through appropriate role modelling, rules and guidance, as well as peer influences from friendship groups with little interest in gambling, appear to be stronger influences. These influences shape and interact with individual factors to act as deterrents to gambling. Individual factors include having other interests, little interest in sport, financial priorities, fear of addiction and harm from both gambling and simulated gambling, reasoned perceptions about gambling, and the ability to critically evaluate gambling advertising. Research into adolescent gambling lacks a focus on interventions to reduce gambling harm [ 2 ]. The present findings, therefore, contribute knowledge to inform preventive strategies such as youth education programs and parental resources and support, that can help to deter underage gambling through reducing modifiable risk factors and enhancing modifiable protective factors. Importantly however, strategies are also needed to reduce environmental risk factors for gambling harm, such as widespread child exposure to gambling advertising and the normalising influences from simulated gambling. Since early uptake of gambling increases the risk of harmful gambling and subsequent mental disorders in later life [ 4 ], multi-layered public health interventions are important to discourage gambling in adolescents. This exploratory study has provided some preliminary insights into the social determinants that shape some adolescents’ reasons for not gambling, but further research is needed to optimise evidence-based interventions.

Data availability

The data that support the findings of this study are available from the New South Wales Office of Responsible Gambling 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 however available from the authors upon reasonable request and with permission of the New South Wales Office of Responsible Gambling. In the first instance, please contact the corresponding author, Nerilee Hing: [email protected].

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Acknowledgements

We acknowledge the contribution of Florence Le Guyader, Lani Sellers and Lisa Lovell-Davis in assisting with data collection.

Funding for this study was provided by the NSW Government’s Responsible Gambling Fund, with support from the NSW Office of Responsible Gambling RG-7611. The views expressed in this manuscript are those of the authors and not necessarily those of the funding agency. The funding agency had no role in the conceptualisation, design, data collection, analysis, decision to publish, or preparation of the manuscript.

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NHi, LL, HT, KS and NHo designed the study and research materials. KS and NHo conducted and supervised the data collection. NHi, LL, HT, KS and NHo contributed to the analyses and interpretation. NH completed the first draft of the manuscript. All authors read, refined and approved the submitted version of the manuscript.

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Hing, N., Thorne, H., Lole, L. et al. ‘Getting addicted to it and losing a lot of money… it’s just like a hole.’ A grounded theory model of how social determinants shape adolescents’ choices to not gamble. BMC Public Health 24 , 1270 (2024). https://doi.org/10.1186/s12889-024-18286-3

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Online Gambling Addiction Essay

Introduction, causes of gambling.

Bibliography

Gambling can be defined as bets that result to material or monetary gain or loss. Also defined as a game whereby the player takes risk hoping to gain material or monetary value on an uncertain outcome. It entails taking risks on uncertain events outcome to make a gain (Smith, 2001, p. 1). A person is said to be addicted to gambling if he/she is unable to avoid gambling.

Online gambling popularity has grown enormously with development in technology. Computer addiction has lead to an increment to online gambling addiction, due to computer addiction one develop tolerance to computer usage. This leads to greater need of stimulating material in the internet, gambling provide the stimulation that one anticipate to gain.

One develops the urge to gamble, which eventually leads to an addiction. It can also be attributed from genetic disorder, persons born in a family with an online gambling addiction has a tendency of themselves inheriting the addiction. It is based on the assumption that the gambling genes are passed on by parents to their young ones (Ladouceur, 2002, p. 1).

Young age gambling can lead to addiction; as young people do it for the thrill. As they grow old they find it hard to stop, as it become habitual to them. Anxiety or depression shows a strong propensity to gambling addiction. Early win in gambling act as an enticement for one to continue gambling, this give one unrealistic success and always want to repeat the experience again and again.

Nowadays internet access is readily available to the majority of the population, as people search for satisfaction they engage themselves in online activities like gambling, since they are readily available (McCarthy, 2005, p. 1). The readily available internet access means one has complete control of his own online activities. Others are a case of immaturity, being rebellious, in secure and being detached from reality. This leads them to engage in online gambling to prove appoint to their colleagues (Catchall, 2005, p. 1).

Gambling is an addiction as one becomes dependent on the activity; he cannot do without it, it becomes a necessity to him (Woods, 2006, p. 1). Majority of the gamblers have unique psychological characteristics, it provide instant gratification and disassociation to the addicts. It is used as an escape from stressful situations and other unpleasant feeling.

They engage in the activity to drown their stressful life but with time it becomes a way of life to them (Dryden, 1996, p. 1). Due to the fact that people engage into it even when it hurts them and their loved ones, clearly shows it is not a normal undertaking but driven by a stronger uncontrollable urge for satisfaction.

Some take online gambling as a way of relaxing their mind, the thrill from the outcome anticipation distracts their thinking. This cools their mind and relieves it from stressful thoughts and experiences. It can also be a symptom to other disorders, especially mental related disorders. To other it is a game like any other, they play for the fun of it. They believe there are no side effects of being involved in the gambling game. This is just an escape from reality, if it is for relaxation one need not to be dependent on it.

Due to the fact that one is controlled by it, it ceases from being a game or a symptom to a condition (Aiden, 1999, p. 1). Majority of the victim continue to suffer from the addiction condition due to self denial, they hide in pretence it is just a game like any other. As much as it may be perceived as a way to relieve stress, it is a source of stress and depression itself to the participant. Online gambling is more of an addiction than a game to the players (Petry, 2002, p. 1).

As evidenced in the discussion above, most cases of addiction to online gambling start due to other minor problems. However, this develops to become a very serious problem that affects the individual adversely. It is thus of essence that people restrain from behaviours which could make them addicted to online gambling.

Aiden, M. (1999). Casino and Gaming Jobs . Web.

Catchall, S. (2005). Online Gambling Addictions Have Become a Common Problem. Web.

Dryden, R. (1996). Gambling addiction . Web.

Ladouceur, R. (2002). Understanding And Treating The Pathological Gambler. Web.

McCarthy, A. (2005). The Online gambling addiction. Web.

Petry, N. (2002). Internet gambling breeds addiction . Web.

Smith, M. (2001). Gambling Addiction and Problem Gambling. Web.

Woods, H. (2006). Online Gambling: A Growing Addiction . Web.

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Problematic online gambling among adolescents: A systematic review about prevalence and related measurement issues

Irene montiel.

1 Faculty of Education, Universidad Internacional de La Rioja (UNIR), Avenida de la Paz, 137, 26006, Logroño, Spain

Jéssica Ortega-Barón

Arantxa basterra-gonzález, joaquín gonzález-cabrera, juan manuel machimbarrena.

2 Faculty of Psychology, University of the Basque Country (UPV/EHU), Avenida de Tolosa, 70, 20018, Donostia, Spain

Background and aims

Despite its illegality among adolescents, online gambling is a common practice, which puts their mental health and well-being at serious risk. This systematic review summarises international scientific literature from the last 20 years on problematic online gambling among adolescents (11–21 years old) to determine its prevalence and to analyse related measurement issues.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and a protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO, IC: CRD42020162932). Five academic databases were consulted, which resulted in an initial sample of 658 papers.

Sixteen studies met the inclusion criteria for this review. All studies were cross-sectional and targeted students from elementary school, secondary school or university. Most followed a convenience sampling procedure. The primary measurement instruments used were the DSM-IV-MR-J and SOGS-RA. Between 0.77% and 57.5% of adolescents present some degree of problematic online gambling (problem, pathological or disordered) depending on the instruments used, the study samples and the timeframe analysed. Between 0.89% and 1% of adolescents exhibited an online gambling disorder.

Discussion and conclusion

There is a great heterogeneity in the methodology of the reviewed studies (samples, measurement instruments, cut-off points and criteria applied). The limited number of studies and the limited generalizability of their results suggest the need for further research and for development of specific instruments to assess different levels of problematic online gambling in representative samples of adolescents based on clinical ‘gold standard’ criteria and more accurate cut-off points.

Introduction

Gambling is usually defined as the activity or practice of playing a game of chance for money or other stakes and online gambling refers to a range of wagering and gaming activities offered through Internet-enabled devices ( Gainsbury, 2015 ). Many adolescents worldwide are involved in gambling—both online and offline—despite being below the legal gambling age (between 16 and 21 years, depending on the country and type of game) ( Emond & Griffiths, 2020 ). In general, online gambling is less prevalent than offline gambling. However, due to its progressive legalisation and promotion alongside the expansion of technology, online gambling is becoming increasingly popular, especially among young people ( Gómez, Feijóo, Braña, Varela, & Rial, 2019 ; Hollén, Dörner, Griffiths, & Emond, 2020 ; Molinaro et al., 2018 ). According to a recent review of international studies, 5–15% of adolescents gamble online and 40–70% gamble offline, with large differences between countries ( King, Russell, & Hing, 2020 ).

Gambling behaviour can be located at different points on a continuum ranging from occasional, recreational, non-problematic or social gambling to at-risk gambling and then to problem, pathological, compulsive or disordered gambling ( Floros, 2018 ). The terms ‘problem’ and ‘pathological’ gambling are often used interchangeably, but the term ‘problem gambling’ describes an intermediate or subclinical form of the disorder ( Lorains, Cowlishaw, & Thomas, 2011 ). ‘Pathological gambling’ was used in the third and fourth editions of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III and DSM-IV) and in the 10 th edition of the International Classification of Diseases (ICD-10) to designate an impulse control disorder. In the fifth edition, the DSM-5, this disorder is renamed ‘gambling disorder’ and is considered an addictive disorder which can comprise three levels of severity—mild, moderate and severe—based on the number of criteria ( American Psychiatric Association, 2013 ). In essence, gambling disorder constitutes a behavioural addiction characterised by persistent and recurrent problematic gambling behaviour that leads to clinically significant deterioration or distress, including social functioning problems, financial problems or even comorbidity with mental and physical illnesses ( American Psychiatric Association, 2013 ). This relevant change was based on their similarities with addictive disorders, not only in terms of diagnostic, clinical and neurological variables, but also in their treatment and comorbidities ( Petry et al., 2014 ). For its part, in the 11 th revision of the ICD (ICD-11) ( World Health Organization, 2018 ), ‘gambling disorder’ (6C50) also appears and includes the important distinction of the disorder consisting of ‘predominantly online gambling’ (6C50.1). This study employs the term ‘problematic online gambling’ in a broad sense with the aim to comprehend the entire spectrum of problems related to online gambling (problem, pathological and disordered).

Multiple tools have been developed to evaluate and diagnose problematic gambling in adults, such as the South Oaks Gambling Screen (SOGS) ( Lesieur & Blume, 1987 ), the Massachusetts Gambling Screen (MAGS) ( Shaffer, LaBrie, Scanlan, & Cummings, 1994 ), and the Canadian Problem Gambling Index (CPGI) ( Ferris & Wynne, 2001 ). Many of these instruments have been adapted to the adolescent population by modifying items, timeframes or the number of items required to establish a clinical diagnosis. According to a systematic review by King et al. (2020) , the most used instruments for the adolescent age group are the DSM-IV-Multiple Response-Juvenile (DSM-IV-MR-J) ( Fisher, 2000 ), the Problem Gambling Severity Index (PGSI) ( Ferris & Wynne, 2001 ) and the South Oaks Gambling Screen–Revised for Adolescents (SOGS-RA) ( Winters, Stinchfield, & Fulkerson, 1993 ). However, in light of the new types of gambling opportunities (i.e. online) the reliability, validity and suitability of the aforementioned instruments must be re-evaluated ( Potenza et al., 2019 ).

While the available scientific literature on problematic online gambling is still limited ( Lawn et al., 2020 ), it highlights how it can lead to significant consequences. For instance, numerous mental health problems, including depression, stress and anxiety ( González-Cabrera et al., 2020 ), as well as drug use ( Effertz, Bischof, Rumpf, Meyer, & John, 2018 ), problematic Internet use ( Andrie et al., 2019 ; Baggio, Gainsbury, Berchtold, & Iglesias, 2016 ; Gómez et al., 2019 ) and Internet gaming disorder ( Beranuy et al., 2020 ). Mainly, however, online gambling has been associated with problem and pathological gambling ( Lawn et al., 2020 ). Some authors suggest the greater addictive potential of online gambling over traditional gambling, especially for young problem gamblers ( Effertz et al., 2018 ; Yazdi & Katzian, 2017 ). In this regard, various studies note that online gamblers are three to eight times more likely to exhibit problematic gambling than those who do not gamble online ( Chóliz, Marcos, & Lázaro-Mateo, 2019 ; Effertz et al., 2018 ; Griffiths, Wardle, Orford, Sproston, & Erens, 2009 ; Volberg, McNamara, & Carris, 2018 ). However, there are discrepancies in the possible explanation of this phenomenon. Some authors attribute the greater addictive potential of online gambling to situational and structural aspects, such as availability, accessibility, immediacy of reinforcement or speed and frequency of gambling ( Chóliz, 2016 ; Griffiths, 2003 ). In this sense, the Internet offers several “advantages” for the individual compared to offline or land-based gambling such as high accessibility of gambling, even at home or at the workplace, at low costs and with a high level of convenience ( Gainsbury, 2015 ; Griffiths, 2003 ). The Internet also allows anonymity for those who do not want to be recognized as gamblers, and multi-simultaneous gambling experiences ( Effertz et al., 2018 ). Online gambling could be a largely automated activity that could be conducted in private, at any time and location, using highspeed Internet connections enabling rapid placement of bets and notification of outcomes ( Gainsbury, 2015 ). On the other hand, the marketing recruitment and maintenance strategies developed by gambling operators (e.g. promotions in Social Media), seem to be very effective, especially among problem gamblers ( Gainsbury et al., 2016 ).

However, other authors argue that online gambling is not in itself more problematic, but that other variables are involved. For example, evidence indicates that those who are already problem gamblers are more involved in online gambling ( Emond, Griffiths, & Hollén, 2020 ; Wijesingha, Leatherdale, Turner, & Elton-Marshall, 2017 ; Yazdi & Katzian, 2017 ), which may explain the higher prevalence of problem gambling among online gamblers. Also, some studies report lower rates of gambling problems in ‘pure online gamblers’ than in ‘pure offline gamblers’ (e.g. Gainsbury, 2015 ). In many cases, online gamblers are also involved in other traditional gambling activities (mixed gamblers), which can influence the relationship between online gambling and gambling problems ( Baggio et al., 2017 ). In addition, among online gamblers, there are specific gambling activities more associated with disordered gambling (e.g. online and land-based Electronic Gaming Machines) than others ( Gainsbury, Angus, & Blaszczynski, 2019 ), and high overall gambling engagement is an important predictor of gambling-related harms ( Baggio et al., 2017 ; Gainsbury et al., 2019 ).

Other key aspects of problem gambling are sex and age variables as risk factors for developing problematic online gambling. Just as it has been observed that adolescents are particularly vulnerable to developing offline gambling problems ( American Psychiatric Association, 2013 ; Caillon, Grall-Bronnec, Bouju, Lagadec, & Vénisse, 2012 ; Calado, Alexandre, & Griffiths, 2017 ), they are also vulnerable to developing online gambling problems ( Gainsbury, 2015 ; Hubert & Griffiths, 2018 ). This general vulnerability can be explained by the developmental characteristics of adolescence, which is a period of particular vulnerability to engage in multiple forms of risky behaviours ( Jessor, 1991 ) and develop addiction problems due to its immature self-regulation capacity, impulsivity, external locus of control and susceptibility to contextual factors ( Hollén et al., 2020 ). Adolescents' online vulnerability could be due to their overall increased use of the Internet for gambling ( Chóliz et al., 2019 ; King et al., 2020 ) thanks to the fact that they have grown up in a society where gambling is generally accepted, heavily available, and widely promoted through Internet ( Volberg, 2010 ). They may be lured by the pop up gambling advertisements, offers of gifts and free play, tempting easy win messages, thrill of many online games, and visually exciting graphics and photos presented with the games ( Derevensky & Gupta, 2007 ). In addition, adolescents can gamble with a small cost per session, using prepaid debit cards issued more easily and with fewer safeguards than the credit cards, or online intermediaries like PayPal ( Floros, Siomos, Fisoun, & Geroukalis, 2013 ; Wong, 2010 ). In sum, the accessibility, affordability, convenience and anonymity of internet gambling may serve as a good mean for young people to engage in gambling activities without age verification and parental supervision ( Elton-Marshall, Leatherdale, & Turner, 2016 ). This fact is specially worrying since several studies show that the lower the age of online gambling onset, the higher the probability of developing problematic online gambling and the more severe the psychosocial consequences are ( Potenza et al., 2011 ; Wong, 2010 ).

Regarding sex differences, the evidence is not as conclusive. Multiple studies suggest that being male is a robust risk factor, especially during adolescence ( American Psychiatric Association, 2013 ; Dowling et al., 2017 ; Emond et al., 2020 ). However, recent studies have demonstrated a significant increase in online gambling behaviour among women, as well as changing trends in online gambling problem development ( Hollén et al., 2020 ; McCormack, Shorter, & Griffiths, 2014 ; Volberg et al., 2018 ).

The past two decades have seen growing concern and research about online gambling in adolescents ( Calado et al., 2017 ; Griffiths, 2003 ; Griffiths & Parke, 2010 ; King et al., 2020 ), and empirical studies about problematic online gambling have increased substantially. Some reviews have been published that make important contributions to the literature (e.g. Gainsbury, 2015 ) despite not including the most recent studies or discussing the prevalence of problematic online gambling itself. Therefore, this review aims to systematically synthesise research trends in studies about problematic online gambling in adolescents and answer the following research questions, which were derived from the PICO (Population, Intervention, Comparator and Outcomes) format ( Shamseer et al., 2015 ): (a) How is online gambling prevalence being operationalised?; (b) What instruments are being used to evaluate problematic online gambling in adolescents, including its cut-off points, criteria and characteristics such as if they are they diagnostic instruments, and do they evaluate online and/or traditional gambling?; and (c) What is the prevalence of problematic online gambling in adolescents at different levels of severity (problem, pathological and disordered gambling), and are there significant differences according to sex or age?

This systematic review adhered to the systematic search protocol recommended in the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P; Shamseer et al., 2015 ). To ensure quality, a protocol was designed and registered with the International Prospective Register of Systematic Reviews (PROSPERO, ID: CRD42020162932).

Inclusion criteria

The scope of this review included quantitative studies about problematic online gambling in adolescents aged 11–21 years published in peer-reviewed academic journals in the past two decades (2000–2020). This age range was chosen because it accords with Salmela-Aro's (2011) proposal regarding the stages of adolescence, and 21 is the age before which online gambling is illegal in several countries.

Studies had to meet five inclusion criteria: (C1) evaluates some level of problematic online gambling (problem, pathological or disordered) through psychometric instruments that allow to establish different groups of gamblers; (C2) includes a study sample consisting of adolescents between 11 and 21 years; (C3) is published in either English or Spanish; (C4) is a quantitative empirical study with original data; and (C5) provides results on the prevalence of problematic online gambling.

The following exclusion criteria were also applied: (1) studies that analysed exclusively traditional gambling (‘offline’ or ‘land-based’) or forms not legally recognised as online gambling (loot boxes, simulated gambling, etc.); (2) studies whose samples consisted exclusively of persons over the age of 21; and (3) thesis works, qualitative studies, reports, case studies and theoretical reviews.

Identification of studies

An initial systematic and comprehensive search of the following electronic databases was carried out from February to April 2020 (inclusive): SCOPUS, Web of Science, PubMed, PsycINFO and Google Scholar. Searches included studies published between January 1, 2000 and April 30, 2020, which were found using the following Medical Subject Heading (MeSH) terms: ‘online gambling’ OR ‘internet gambling’ OR ‘digital gambling’ OR ‘online bet*’ OR ‘internet bet*’, AND ‘adolesc*’ OR ‘child*’ OR ‘kid’ OR ‘teen*’, AND ‘patholog*’ OR ‘problem*’ OR ‘disorder*’ OR ‘disease’ OR ‘excessive’. Searches were re-run on June 30, 2020 and on October 30, 2020, but no newly published study met the five inclusion criteria. The reference lists of qualitative and review studies were also reviewed manually. Full search results and reference listings for each database consulted are available from any of the authors.

Study selection process

Figure 1 presents a flow diagram of the process of identifying, screening, selecting and including studies in the review. After removing all results other than academic articles, 641 manuscripts were identified in the five databases consulted. Seventeen manually identified articles were added from the list of references of review and qualitative studies. All references ( n = 658) were imported to Zotero. The removal of duplicates produced 401 items for the screening phase. To minimise potential errors and bias in the selection process ( Whiting et al., 2016 ), two independent researchers evaluated the titles, keywords and abstracts of all items to pre-select articles that could meet the eligibility criteria. Consequently, 308 articles were excluded. Kappa's concordance rate among the researchers at this screening phase was good ( κ = 0.795). The first researcher (IM) evaluated the remaining 93 full texts according to the defined eligibility criteria, while the second researcher (JOB) reviewed the application of the criteria to these publications. When there was any discrepancy, the other manuscript authors were consulted until an agreement was reached. Finally, 16 articles were included in the qualitative synthesis. At this stage, Kappa's concordance index among the researchers for eligibility criteria application and final study selection was excellent ( κ = 0.838).

PRISMA Flow diagram of study selection

Data extraction

Table 1 provides a summary of all the information extracted from the sixteen articles selected for review. The data include authors and publication dates; sample countries; final sample size, age and sex of participants (mean, standard deviation and range); terminology used to refer to problematic online gambling (problem, pathological, disordered); measurement instruments and cut-off points used to classify gamblers; reliability data; results on prevalence; and, when analysed in the study, sex and age differences. Following the recommendations of Whiting et al. (2016) for minimising possible errors and bias in data collection, the first researcher (IM.) performed complete data extraction while a second researcher (JOB.) extracted data from 50% of the studies independently to detect and solve any inaccuracies.

Summary of selected international studies ( n = 16) about problematic online gambling in adolescents

Note : y/o = years old; M age = arithmetic mean age; ♂ = boys; ♀ = girls; M = arithmetic mean; SD = standard deviation.

NPG = non-problem gambling; ARG = at-risk gambling; PPG = probable pathological gambling; ARPG = at-risk and/or problem gambling.

Table 2 presents the various assessment instruments used in the 16 studies, their main characteristics (number of items, response format and timeframe), psychometric properties such as reliability and validity and defined cut-off points for classifying participants. They are ordered according to the clinical criteria on which they are based, starting with the most current ones (DSM-5 and ICD-11).

Summary of instruments used for measuring problematic online gambling in adolescents, in selected studies ( n = 16)

Synthesis of results

A synthesis of the results concerning prevalence data was carried out following an ad hoc categorisation based on instrument type (diagnostic, screening or severity scale) and whether online or offline gambling was evaluated. This categorization was used because the instruments employed in the reviewed studies shared certain underlying elements (i.e. family and school problems or loss of control), but their goals, contexts and criteria were different ( Derevensky & Gupta, 2006 ). Even the dimensions assessed by the instruments and their approaches were different and not always clinical. Diagnostic instruments are designed to assess the presence of a clinical disorder following diagnostic criteria from APA (DSM-5) or WHO (ICD-11) and allow us to conclude whether a person meets that diagnostic and therefore presents the disorder or the absence of it (e.g. the Online Gambling Disorder Questionnaire -OGD-Q- or DSM-IV-MR-J). Instead, screening instruments are designed to identify the potential presence of a particular problem and they are typically used as a preliminary step in assessment (e.g. the SOGS-RA), as a way of determining if further, more comprehensive assessment is necessary ( Waldron, 1998 ). On the other hand, some scales have been designed to measure the severity of the problem, not based on clinical criteria but psychological, social and financial consequences of gambling behaviours, that allow a score to be obtained on a continuous scale of severity ranging from low, to medium or high (e.g. the Gambling Problem Severity Subescale -GPSS-). Furthermore, an instrument developed to measure problem gambling in a clinical sample, where the base rate is fairly high, would have weaker classification accuracy when applied to the general population, where the base rate is extremely low ( Stinchfield, 2010 ). Also, in many studies it has been observed that prevalence rates tend to be higher when measured with instruments such as the South Oaks Gambling Screen (SOGS), its adolescent version (SOGS-RA) or the Problem Gambling Severity Index (PGSI; Ferris & Wynne, 2001 ) and lower with clinical DSM criteria (Calado & Griffiths, 2016; Floros, 2018 ). As these aspects can influence prevalence outcomes and make comparison between them inappropriate ( Edgren et al., 2016 ; Floros, 2018 ; Stinchfield, 2010 ), we decided to differentiate them in the narrative synthesis for didactic and presentation purposes.

Thus, this categorisation allowed for distinguishing (a) the prevalence of problem, pathological and disordered online gambling based on diagnostic instruments that address online gambling specifically (e.g. the Online Gambling Disorder Questionnaire [OGD-Q]) or ones that do not (e.g. DSM-IV, MAGS); (b) the prevalence of problem gambling based on the SOGS-RA and SOGS screening instruments, (both of which omit online gambling elements); and (c) the prevalence of high and low to moderate severity of problems using a severity scale such as the Gambling Problem Severity Subscale (GPSS) of the Canadian Adolescent Gambling Inventory (CAGI), which do not have online gambling elements.

Ethics statement

The writing of this manuscript did not involve the use of any procedures on human or animal participants.

Characteristics of the studies

Most of the studies were conducted in Europe ( n = 9), with the United Kingdom and Spain standing out with two studies in each country; followed by North America ( n = 4), with Canada presenting three studies; and Asia ( n = 3). They were all in English and had a cross-sectional design ( n = 16). The sampling procedures were mostly non-probabilistic, convenience or incidental and non-representative of the whole adolescent population ( n = 10), although five studies used international ( Andrie et al., 2019 ) or national probabilistic samples ( Canale, Griffiths, Vieno, Siciliano, & Molinaro, 2016 ; Elton-Marshall et al., 2016 ; Floros et al., 2015 ; Kang, Ok, Kim, & Lee, 2019 ) and two studies included the entire target population in the sample ( Floros et al., 2013 ; Olason et al., 2011 ). Sample sizes were heterogeneous and ranged from 127 ( Matthews, Farnsworth, & Griffiths, 2009 ) to 14,778 ( Canale et al., 2016 ) adolescents between 11 and 21 years old. All samples consisted of students from elementary school, secondary school or university, and two samples were composed only of adolescent gamblers ( Matthews et al., 2009 ; Potenza et al., 2011 ). Of these, only one concerned online gamblers exclusively ( n = 127; Matthews et al., 2009 ). The average time between sample collection and publication was 3.6 years (range 1–7 years).

Measurement issues: online gambling operationalisation

The studies operationalised online gambling and identified online gamblers quite heterogeneously. Only four studies specified the need to have gambled with real money ( Brunelle et al., 2012 ; González-Roz, Fernández-Hermida, Weidberg, Martínez-Loredo, & Secades-Villa, 2017 ; Mcbride & Derevensky, 2012 ; Wong & So, 2014 ), whereas two referred to money or ‘anything else of value’ ( Elton-Marshall et al., 2016 ; Potenza et al., 2011 ). The rest did not specify anything in this regard.

Ten studies asked about any form of gambling or betting on the Internet without specifying any type of gambling ( Andrie et al., 2019 ; Brunelle et al., 2012 ; Canale et al., 2016 ; Floros et al., 2013 , 2015 ; González-Cabrera et al., 2020 ; Matthews et al., 2009 ; Olason et al., 2011 ; Potenza et al., 2011 ; Wong & So, 2014 ). Six studies specified the online games they were referring to ( Aricak, 2019 ; Elton-Marshall et al., 2016 ; González-Roz et al., 2017 ; Griffiths & Wood, 2007 ; Kang et al., 2019 ; Mcbride & Derevensky, 2012 ).

The studies also differed in their time-based criteria for identifying online gamblers. In three studies, participants had to have gambled online in the three months prior to the study ( Elton-Marshall et al., 2016 ; Floros et al., 2015 ; Kang et al., 2019 ), whereas in 9 studies, the period under examination was 12 months prior ( Andrie et al., 2019 ; Brunelle et al., 2012 ; Canale et al., 2016 ; González-Cabrera et al., 2020 ; González-Roz et al., 2017 ; Mcbride & Derevensky, 2012 ; Olason et al., 2011 ; Potenza et al., 2011 ; Wong & So, 2014 ). Four studies included all those who had ever gambled online in their lives ( Aricak, 2019 ; Floros et al., 2013 ; Griffiths & Wood, 2007 ; Matthews et al., 2009 ). In addition, two studies differentiated between gamblers who exclusively gambled online (online-based gamblers), exclusively offline (land-based gamblers) or online-offline (mixed gamblers) ( González-Roz et al., 2017 ; Olason et al., 2011 ).

Measurement issues: problem, pathological and disordered online gambling assessment

There was considerable variability in the measurement instruments and cut-off points used. However, self-administered instruments were used in all cases, and in most cases, the defined timeframe was 12 months (see Table 2 ). Only three studies included specific elements of online gambling in their measuring instruments, while the rest exclusively mentioned items that referred to traditional gambling ( Brunelle et al., 2012 ; González-Cabrera et al., 2020 ; Wong & So, 2014 ).

One study validated a new diagnostic instrument, the OGD-Q for adolescents ( González-Cabrera et al., 2020 ), based on diagnostic criteria from the DSM-5 and the ICD-11 and on recommendations from several experts in the field. Eight studies used diagnostic instruments based on criteria from the DSM-IV or its related text revision, the DSM-IV-TR. Six studies used the Diagnostic and Statistical Manual-IV adapted format for Juveniles DSM-IV-MR-J ( Fisher, 2000 ), one used the list of symptoms from the DSM-IV ( Stinchfield, 2003 ) and one used the DSM-IV MAGS subscale ( Shaffer et al., 1994 ).

Three studies used the SOGS-RA screening instrument ( Winters et al., 1993 ) while one used its adult version, the SOGS ( Lesieur & Blume, 1987 ). Both are based on clinical criteria from the DSM-III or its revised version, the DSM-III-R (American Psychiatric Association, 1980, 1987).

Two studies assessed the severity of problem gambling through the continuous GPSS scale, which is part of the CAGI ( Tremblay, Stinchfield, Wiebe, & Wynne, 2010 ).

Conversely, one study combined a scale designed ad hoc to evaluate online betting (Survey of Online Betting), which was developed by a team of experts, with the Internet Addiction Scale ( Gunuc & Kayri, 2010 ), based on the premise that problematic online betting is an example of Specific Problematic Internet Use (SPIU).

In general, the cut-off points in diagnostic and screening instruments were equal to 4 or more satisfied criteria to classify adolescents as probable pathological gamblers and three criteria generally classified them as exhibiting problem or at-risk gambling. However, it is worth noting that not all the instruments used include the same number of items. For example, while the OGD-Q includes 11 items, the DSM-IV-MR-J includes 9 and the SOGS-RA 12. Also, there were more particularities in each study (see Table 2 ), such as in one study, in addition to defining the cut-off point of 4 criteria, accounted for the duration of symptoms to determine the diagnosis of an online gambling disorder, differentiating it from problem gambling ( González-Cabrera et al., 2020 ), while another study designates a participant as at-risk/problem gambling by fulfilling only one criterion of the DSM-IV ( Potenza et al., 2011 ).

Only six studies analysed the internal consistency of the scales used by means of Cronbach's alpha ( Brunelle et al., 2012 ; Canale et al., 2016 ; Floros et al., 2013 , 2015 ; González-Cabrera et al., 2020 ; Kang et al., 2019 ). In addition, one study performed an in-depth psychometric analysis of the instrument used on a sample of adolescent online gamblers ( González-Cabrera et al., 2020 ) (see Tables 1 and ​ and2 2 ).

Prevalence of online gambling and problematic online gambling

There was great inconsistency in the number of adolescents that were found to gamble online, ranging between 0.6% in Spain ( González-Roz et al., 2017 ) and 37.2% on the island of Kos ( Floros et al., 2013 ). Based on the past year's data, the percentage varies from 3.5% of adolescents in Canada ( Brunelle et al., 2012 ) and China ( Wong & So, 2014 ) to 32.8% in Spain ( González-Cabrera et al., 2020 ). If we look only at those studies that used representative samples of adolescents, online gambling past-year prevalence varies between 6% in European adolescents ( Andrie et al., 2019 ) and 15.6% in Italian adolescents ( Canale et al., 2016 ).

In addition, high variability in problematic online gambling prevalence was found in the reviewed studies. The prevalence of different problematic online gambling levels (disordered or pathological and problem gambling) is described below, taking into account samples, the defined timeframe, the type of assessment instrument used—diagnostic, screening or severity scale—and whether the instrument in question addressed online gambling specifically.

Pathological or disordered online gambling in adolescents and pathological gambling among those who gamble online

Based on diagnostic instruments with online gambling elements, the prevalence of pathological online gambling or online gambling disorder during the last year in adolescents ranged from 0.89% in Spain ( González-Cabrera et al., 2020 ) to 1% in China ( Wong & So, 2014 ), while the prevalence among those adolescent who have gambled online ranged from 2.7% in Spain ( González-Cabrera et al., 2020 ) to 15.4% in Canada ( Brunelle et al., 2012 ). However, based on diagnostic instruments used for offline gambling, the past-year prevalence of pathological gambling in adolescent online gamblers varied from 7.5% in Iceland ( Olason et al., 2011 ) to 18.1% in Cyprus ( Floros et al., 2015 ) and the lifetime prevalence of pathological gambling in adolescent online gamblers ranged from 11.1% in Greece ( Floros et al., 2013 ) to 33% in the UK ( Griffiths & Wood, 2007 ).

It should be noted that only three of the seven studies mentioned used a representative sample of the adolescent population. Specifically, Floros et al. (2013) , used a sample consisting of all students on the Island of Kos, Floros et al. (2015) used a representative sample of 2,684 Cypriot students aged 12–16 years and Olason et al. (2011) used a sample consisting of almost all adolescents from Hafnarfjörður (Iceland). In all cases, the presence of pathological gambling among adolescents in general (4.1%, 2.5% and 2.2%, respectively) and among online gamblers (11.1%, 18.1% and 7.5%, respectively) was assessed.

Problem online gambling in adolescents and problem gambling among those who gamble online

When a diagnostic instrument including online gambling elements, such as the OGD-Q, was used to evaluate online gambling problems, the past-year prevalence among adolescents in general was reported at 0.77%, and 2.4% among online gamblers ( González-Cabrera et al., 2020 ). However, the latter figure amounted to 16.2% when DSM-IV criteria to diagnose offline problem gambling were used ( Mcbride & Derevensky, 2012 ). Furthermore, when screening instruments designed to evaluate offline problem gambling (such as the SOGS-RA) were used among online gamblers, the prevalence of problem gambling varied between 10.2% in Spain ( González-Roz et al., 2017 ) and 21.9% in Italy ( Canale et al., 2016 ). It should be noted that only the last of the four studies mentioned used a representative sample of the Italian adolescent population of 15–19 years of age to assess problem gambling among adolescents in general (4%) and among online gamblers (21.9%).

At-risk/problem online gambling in adolescents and at-risk/problem gambling among those who gamble online

Using diagnostic instruments that refer to online gambling, the past-year prevalence of at-risk online gambling among adolescents in general was 0.56% in Spain ( González-Cabrera et al., 2020 ). However, among adolescent online gamblers, prevalence varied between 1.7% in Spain ( González-Cabrera et al., 2020 ), 22.9% in China ( Wong & So, 2014 ) and 26.1% in Canada ( Brunelle et al., 2012 ).

In studies where At-Risk and Problem Gambling (ARPG) were measured together prevalence among adolescent online gamblers varied from 48.4% in a cross-national European study using the SOGS-RA ( Andrie et al., 2019 ) to 57.5% in the U.S. state of Connecticut using the MAGS-DSM-IV in a self-selected sample of past-year gamblers ( Potenza et al., 2011 ). It should be noted that only one of the five studies mentioned used a representative sample of the European adolescent population between 14 and 18 years of age to assess at-risk or problem gambling (ARPG) among adolescents in general (3.6%), among adolescent gamblers (28%), online gamblers (48.4%) and only-offline gamblers (26.5%).

Severity of gambling behaviour among adolescents who gamble online

In terms of the severity of gambling behaviour evaluated with the GPSS/CAGI, the prevalence of highly severe problems in the last 3 months among adolescent online gamblers was between 17.4% in Canada ( Elton-Marshall et al., 2016 ) and 17.8% in South Korea ( Kang et al., 2019 ). The prevalence of low to moderate problems was between 18.2% in Canada ( Elton-Marshall et al., 2016 ) and 25.5% in South Korea ( Kang et al., 2019 ). It should be noted that the two studies mentioned used a representative sample of adolescent population. Specifically, Elton-Marshall et al. (2016) used a representative sample of Canadian adolescents and Kang et al. (2019) of Korean students aged 13–17 years.

Sex and age differences in problem, pathological and disordered online gambling

Six studies provided data on sex based differences in prevalence. They all agreed that boys have more online gambling related problems than girls do ( Andrie et al., 2019 ; Aricak, 2019 ; Floros et al., 2013 ; González-Cabrera et al., 2020 ; Potenza et al., 2011 ; Wong & So, 2014 ). Three studies provided data on age differences. One found that among all adolescents who participated in the study, the proportion of ARPG was highest in the older age group (4.5% at 16–17.9 years in comparison to 3% at 14–15.9 years, P < 0.001), in which there were a higher proportion of online gamblers (7.6% in comparison to 4.9%, P < 0.001) ( Andrie et al., 2019 ). The other two studies did not find significant differences between the different age groups between 12 and 19 years of age ( Floros et al., 2013 ; Potenza et al., 2011 ).

The main objective of this systematic review was to synthesize the research related to problematic online gambling, including problem, pathological and disordered online gambling in adolescents and among those who gamble online. In particular, it focused on answering several research questions relating to: (a) the operationalisation of online gambling prevalence; (b) the diverse instruments for assessing problematic online gambling in adolescents, their cut-off points/criteria and characteristics; and (c) the international prevalence data of different grades of problematic online gambling. Initially, 658 peer-reviewed papers were identified, of which 16 ultimately met the five eligibility criteria for inclusion in this systematic review.

Concerning the first research question about the operationalisation of online gambling prevalence, there was, as expected, high heterogeneity. This could explain, at least partially, that the proportion of adolescents that gamble online varied between 0.6% ( González-Roz et al., 2017 ) and 37.2% ( Floros et al., 2013 ). However, when representative samples of the adolescent population were used, the prevalence range was between 6% ( Andrie et al., 2019 ) and 15.6% ( Canale et al., 2016 ), in line with the range found in the King et al.'s (2020) review of online gambling in adolescents (5%–15%). The breadth of the range could be explained by at least five reasons. First, the cultural and legal context of the studies may influence gambling behaviours and, consequently, prevalence rates ( Volberg et al., 2018 ). Second, since not all studies specified the need to gamble for real money, some adolescents might have responded with simulated forms of gambling in mind, as these are common at these ages ( Elton-Marshall et al., 2016 ). Third, the specific forms of online gambling referred to were different in each study, some of which were much more frequent than others, such as online betting ( Hollén et al., 2020 ), that has been related with higher risk of problems ( McCormack, Shorter, & Griffiths, 2013 ). Fourth, the timeframe evaluated varied from three months ( Elton-Marshall et al., 2016 ; Floros et al., 2015 ; Kang et al., 2019 ) to a lifetime ( Aricak, 2019 ; Floros et al., 2013 ; Griffiths & Wood, 2007 ; Matthews et al., 2009 ). Finally, in all but one of the reviewed studies ( González-Roz et al., 2017 ), online gamblers were considered a homogeneous group, disregarding that a large percentage of them also gamble offline ( Elton-Marshall et al., 2016 ; Olason et al., 2011 ).

It is important to note that the group made up of online and offline gamblers is known as mixed gamblers and, according to different studies, mixed gambling predicts both at-risk gambling and problematic gambling better than pure online gambling does. This could be because, beyond the characteristics of the internet, mixed gambling implies greater engagement in the behaviour due to the diversity of games, access modes and time spent on them. González-Roz et al. (2017) and Olason et al. (2011) suggested that this could explain the higher prevalence of at-risk and problem gambling found among adolescents who gamble online (and are mixed gamblers) compared to those who gamble offline in numerous studies, including eleven studies of the present review (e.g. Brunelle et al., 2012 ; Griffiths & Parke, 2010 ; Griffiths & Wood, 2007 ; Mcbride & Derevensky, 2012 ; Olason et al., 2011 ). In line with this issue, Blaszczynski, Russell, Gainsbury, and Hing (2016) conclude that exclusive online gamblers represent a different subpopulation at lower risk of harm (problem gambling, gambling involvement and consumption of alcohol) compared to gamblers engaging in multiple forms of gambling. This discussion could be compared to that described in the scientific literature on substance abuse about "polydrugs users", that is those who use multiple substances at the same time, who have more negative and severe social and health consequences compared to monosubstance users ( Steele & Peralta, 2020 ).

Concerning the second research question of this review about the measurement instruments and cut-off points/criteria used, there was noteworthy variability as well as limited psychometric analysis. It is also significant that virtually no instrument has been validated for adolescents who gamble online or has been based on updated diagnostic criteria (DSM-5 and ICD-11). In this review, only one study used a specific instrument to assess online gambling disorder based on the most current diagnostic criteria ( González-Cabrera et al., 2020 ) while two others only included some elements of online gambling in the items of the diagnostic instrument used DSM-IV-MR-J ( Brunelle et al., 2012 ; Wong & So, 2014 ).

The most commonly used instruments in the reviewed studies were the DSM-IV-MR-J and SOGS-RA, that were designed to assess offline gambling problems. The SOGS-RA is a screening instrument designed for the clinical context and is adapted from its adult version (SOGS), which evaluates symptoms of ‘problem gambling’ and negative consequences of gambling, such as the amount of money gambled or the feeling of guilt ( Derevensky & Gupta, 2006 ), but it does not confirm the existence of a mental disorder ( Edgren et al., 2016 ). Meanwhile, the DSM-IV-MR-J is a diagnostic instrument for evaluating ‘pathological gambling’ in adolescents using the diagnostic criteria of the DSM-IV (preoccupation, tolerance, escape, etc.). Although common, this term is outdated because it draws on the old consideration of the problem as an impulse control disorder, and some authors consider it pejorative and inappropriate for referring to adolescents ( Petry et al., 2014 ; Volberg, Gupta, Griffiths, Ólason, & Delfabbro, 2010 ). This could explain why ‘problem gambling’ is the preferred term when studying adolescent gambling behaviour and why it has been used as an equivalent to pathological gambling in several studies. In spite of its wide use, problem gambling was discarded from the DSM-5 for being too generic, as it incorporates subclinical problems and conditions ( Petry et al., 2014 ).

Furthermore, as noted, the diagnostic criteria on which these tools were based have not been clinically tested on the adolescent population, which calls into question their validity for this population ( Edgren et al., 2016 ; King et al., 2020 ; Stinchfield, 2010 ; Volberg et al., 2010 ). For some instruments, such as the MAGS or SOGS-RA, it has even been suggested that the criterion validity is not equivalent for boys and girls ( Edgren et al., 2016 ). Moreover, none but one of the reviewed instruments has been validated in adolescent online gamblers ( González-Cabrera et al., 2020 ), which could present a validity issue when assessing problematic online gambling in adolescents and in the highest-risk population: online gamblers. In general, there is limited assessment of the psychometric properties of the measurement instruments used in the studies and several authors recommended further research in this regard ( Derevensky & Gupta, 2006 ; Edgren et al., 2016 ; King et al., 2020 ; Potenza et al., 2019 ; Stinchfield, 2010 ). In this review, only seven studies provided information on reliability using Cronbach's alpha, but it was insufficient ( Edgren et al., 2016 ).

Regarding the third research question on the international prevalence of different levels of problematic online gambling, and sex and age based differences, the results are greatly affected by the type of assessment tool employed. Between 0.89% and 1% of adolescents exhibited an online gambling disorder based on diagnostic assessment which included online elements. However, between 0.77% and 57.5% of adolescents meet some criteria for problematic online gambling, a range much higher than that found by Calado et al. (2017) on problem gambling in adolescents (0.2%–12.3%). Even when we look only at the studies that used representative samples of the adolescent population, we observe that between 1.1% and 48.4% of adolescents presented some degree of problematic online gambling. The breadth of these ranges could be due at least partially to the differences between samples, assessment instruments, cut-offs, and timeframes. For example, using offline gambling based diagnostic instruments, a higher prevalence is obtained than when using specific online gambling diagnostic instruments. Furthermore, when problem gambling screening instruments are used, the prevalence of problem gambling in online gamblers (10.2%–21.9%) is higher to that found in the review by Delfabbro, King, and Derevensky (2016) , according to which between 4% and 8% of adolescent gamblers are experiencing significant gambling-related problems. Among the studies that used representative samples of the adolescent population, we observe that the range of prevalence rates of pathological gambling among online gamblers is from 7.5% to 18.1%, while the prevalence of problem gambling is 21.9% and the percentage of adolescents online gamblers with a high severity of problem gambling is between 17.4% and 17.8%.

Regarding the differences found in prevalence according to sex, although only six studies analysed this issue, all of them indicated that boys have more problems related to online gambling than girls do, in line with findings on offline gambling ( Calado et al., 2017 ). These results may have been skewed by the type of gambling included in each study and, therefore, cannot be considered conclusive. For example, boys place more online sports bets than girls do ( Hollén et al., 2020 ; McCormack et al., 2014 ), which has been significantly associated with problematic online gambling ( Olason et al., 2011 ; Potenza et al., 2011 ). However, recent studies have seen significant growth in online gambling behaviour in women and some trend changes in the development of online gambling problems ( Hollén et al., 2020 ; McCormack et al., 2014 ; Volberg et al., 2018 ), which points to the need for further analysis on gender related differences in online gambling.

Regarding age, only three studies analysed its relationship with problem, pathological or disordered online gambling. As the results were contradictory, a firm conclusion cannot be established. For example, Potenza et al. (2019) and Floros et al. (2013) found no significant differences in age, whereas Andrie et al. (2019) observed a higher proportion of ARPG among older adolescents. In line with this, studies such as that of Hubert and Griffiths (2018) , which included adolescents and adults, found that almost half of the pathological online gamblers were between the ages of 16 and 20.

It should be pointed out that most of the studies included in this review utilized non-representative convenience samples, which do not allow generalize their results to the entire adolescent population. Moreover, the methodological differences between studies and the cultural or legal context, also compromise comparability across studies and countries, making the establishment of a general prevalence very difficult, even when this is crucial to advance in the scientific knowledge of this problem.

Recommendations and future directions

These results yield several recommendations for researchers. First, there is a pressing need to clearly operationalise online gambling and the types of gambling included therein in a unified way, as authors such as King et al. (2020) have suggested. In this sense, it would be worth considering the inclusion of some gambling behaviours characteristic of adolescents, such as the use of loot boxes in online video games, simulated gambling (where it is not necessary to bet real money) or skin gambling (the use of virtual goods as virtual currency to bet), as they seem to be associated with problematic online gambling in adolescents ( Floros et al., 2013 ; King, Delfabbro, Kaptsis, Zwaans, 2014 ; Kristiansen & Severin, 2020 ; Wardle, 2019 ; Zendle, Meyer, & Over, 2019 ). In addition, although loot boxes are not legally regarded as gambling in all countries ( Griffiths, 2018 ), Belgium has already declared them a form of gambling and the Netherlands and Denmark are moving in the same direction ( Kristiansen & Severin, 2020 ).

Second, the findings of this review highlight the need to design new assessment tools specifically for adolescents and online gambling. Researchers should analyse the psychometric properties of these instruments and incorporate timeframes and cut-off points which are based on a ‘gold standard’ diagnostic criterion to differentiate subclinical online gambling problems from the clinical disorder ( Stinchfield, 2010 ). It would also be advisable to test the instruments in population-based and clinical samples and use each one in the context for which it was created ( Derevensky & Gupta, 2006 ; Edgren et al., 2016 ; Tremblay et al., 2010 ). Further, given the changing nature of the phenomenon of online gambling and rapid technological evolution, it would be a good practice to ensure that the time between sample collection and publication of the results is as short as possible.

Third, and in line with the above, there is an urgent need to unify the terminology used by the scientific community to refer to different levels of problematic online gambling including online gambling disorder. Accordingly, it would be necessary to discuss in the next revision of the DSM the inclusion of the online version of gambling disorder. The term ‘gambling disorder’ best suits the current classification systems and available scientific evidence ( Petry et al., 2014 ), but the new technological context deserves a place in this classification, as it already has in the ICD-11. This might prompt further study and generate more solid evidence concerning this issue, such as its prevalence and age and sex differences.

Limitations

This review faced some limitations that should be discussed. First, the number of studies included in the review was limited likely due to the relative novelty of the subject and the very strict protocol requiring compliance with the five inclusion criteria. It is, therefore, possible that other studies of potential relevance did not appear in this review. Second, as some of the reviewed studies predate the DSM-5, some inconsistency in the results was expected. Third, the methodology of this review did not provide meta-analytical results owing to the high heterogeneity of information collected. Fourth, the limited representativeness of most of the study samples does not allow generalization of their results to the entire adolescent population. Finally, an ad hoc categorisation of the results was carried out based on the type of instrument used, which may not match the original intention of the authors. This was considered appropriate from an analytical and pedagogical approach to facilitate the organisation and understanding of the narrative synthesis.

Conclusions

Although problematic online gambling in adolescents is an area of increasing research interest, this systematic review highlights several aspects. First, there is great heterogeneity in the operationalisation of online gambling. Second, there is a lack of consensus about the terminology, instruments and cut-off points used to assess problematic online gambling in a broad sense and online gambling disorder in a clinical sense. Third, the need to develop and psychometrically improve measurement instruments, especially for adolescents and online gambling, is noteworthy, to promote early detection and intervention. This leads to a significant disparity in the prevalence outcomes of different levels of problematic online gambling in adolescents and among those who gamble online, as difficulties in establishing firm conclusions about the extent and severity of the problem.

Funding sources

This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness, RTI2018-094212-B-I00: (CIBER-AACC), and by the International University of La Rioja, Project “Cyberpsychology (Triennium 2017–2020)”.

Authors' contributions

All the authors designed the review. IM wrote the protocol, conducted literature searches, analyzed of the records and wrote the first draft of the manuscript. JOB and ABG contributed to the selection of studies and data extraction. JGC and JMM supervised the project, provided feedback and reviewed the manuscript. JGC acquired the financial support for the project leading to this publication. All authors contributed to and approved the final manuscript. All authors had full access to all data in the review and take responsibility for the integrity of the data.

Conflict of interests

The authors declare that are are no financial, industrial or other relationships that may constitute a conflict of interest concerning this work.

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