Table 1.

Summary of Studies Characteristics

Study Identification	Objectives	Sample			Conclusions
		N	Age	% male
Bellringer & Garrett (2021)	Aimed to extend current knowledge of gambling behaviors during COVID-19 restrictions by examining New Zealand data	N = 301	> 18	44	Behavioral risk factors included being a current low risk/moderate risk/problem gambler, a previously hazardous alcohol drinker or past participation in free-to-play gambling-type games
Bergamini et al. (2018)	To investigate the prevalence of at-risk gambling in a large, unselected sample of outpatients attending two community mental health centers, to estimate rates according to the main diagnosis, and to evaluate risk factors for gambling	N = 56	18–70 M = 48.7 SD = 13.7	48	Comorbidity with schizophrenia, bipolar disorder, unipolar depression and group B personality disorder
Bibby and Ross (2017)	To investigate the relationship between alexithymia and loss- chasing behavior in people at risk and not at risk for problem gambling	N = 58	M = 48.1 SD = 13.5	86	Alexithymia and problem gambling risk were significantly positively correlated
Biegun et al. (2019)	Validates one proposed measure of problem video gaming, in a Canadian undergraduate university student sample	N = 651	> 18	47	The video game problem is positively associated with average time spent playing the game, social alienation and online gaming motives such as competition, escape, coping, recreation and socialization In contrast, there is no correlation between problem gambling and several of its mental health correlates – depression, anxiety and stress
Black and Allen (2021)	Examined the association of baseline social, demographic, and clinical predictor variables with course in 48 older (≥ 60 years) and 57 younger (< 40 years) subjects with PPG in a prospective follow-up study	N = 105 (n = 57 younger; n = 48 older)	Younger (< 40 years) M = 27.5 SD = 8.0 Older (≥ 60 years) M = 66.6 SD = 6.9	Younger 81 Older 38	Predictors of disorderly gambling during follow-up included more severe PPG symptoms, more severe depressive symptoms, self-reported childhood neglect, gambling-related cognitive biases, and more role limitations due to physical health
Browne et al. (2019)	The present study attempted comprehensive measurement and evaluation of 25 known risk factors for gambling-related harm in order to determine which factors provided large and unique explanatory power	N = 1174	> 18 M = 43.4 SD = 15.3	40	Trait impulsivity is by far the most important risk factor. Overspending, less use of safe gambling practices, and more mistakes are the most significant proximal injury risks
Buth et al. (2017)	The aim of our study is to identify potential risk factors for disordered, problem, and at-risk gambling and to assess their respective relevance	N = 4,082 (n = 81 disordered; n = 72 problem; n = 121 at-risk; n = 2,808 social)	14 –65 Disordered M = 33.5 SD = 13.2 Problem M = 33.4 SD = 12.5 At-risk M = 40.4 SD = 12.5 Social M = 41.9 SD = 13.3	Disordered 79 Problem 68 At-risk 66 Social 54	Significant risk factors for gaming disorder include risk for alcohol use, poor mental health, young age (≤ 26 years), low formal education, growing up with a single parent, parents with addiction problems, and being working class Risk factors for problem gambling include parents with substance use problems, poor mental health and young age
Butler et al. (2019)	Examined the association between gambling problem severity and health risk behaviors, health, and wellbeing	N = 2,303	> 18	38	Low-severity players are approximately twice as likely to have poor mental health, and medium/high-severity players are three times more likely to have poor mental health than players with no issues
Çakici et al. (2021)	To investigate the prevalence and risk factors of PPG	N = 799	18 to 65	Lifetime non-PPG 86 Lifetime non-PPG 14	Being male, age range of 18–29, single, living alone and marry less than 5 years are the risk factors for PPG
Çakici et al. (2015)	To investigate the characteristics of adults’ participation in gambling, and to determine the prevalence of ‘problem and pathological gambling’ in North Cyprus	N = 966	18 to 65	–	Risk factors for becoming a problem and pathological gambler include being male, between the ages of 19–28, having a higher education, having a job, and being born in Cyprus
Cavalera et al. (2018)	Examined adult gambling behaviors from a local perspective in order to assess the adult at risk and problem gambler’s profile stratified by genre and by different forms of game	N = 4,773	18–94 M = 40.3 SD = 17.6	50	Both disadvantaged and problem gamblers are associated with men, and gamblers play more than one game and play strategy-based games
Cowlishaw et al. (2016)	Evaluated the prevalence and correlates of gambling problems in a U. S. representative sample reporting treatment for mood problems or anxiety	N = 3007	18–24	27	Lifelong gambling problems predicted relationship and financial difficulties, as well as marijuana use, but not alcohol use, mental or physical health, and use of health services
Cunha et al. (2017)	To identify characteristics with higher odds of distinguishing a group of PPG from (1) a group of gamblers without PG and 2) a SP	N = 331 (n = 162 NPG; n = 117 SP; n = 52 PPG)	> 18 NPG M = 33.6 SD = 10.9 SP M = 29.3 SD = 8.4 PPG M = 36.7 SD = 12.7	NPG 27 SP 44 PPG 83	The odds of being a PPG were higher for men with less education and less adaptive psychological relationship skills. Conversely, women with higher levels of education and more adapted psycho-relational functioning had higher odds of becoming NPs
De Pasquale et al. (2018)	To investigate the prevalence of Internet gaming disorder among Italian university students and to explore the associations between the former and dissociative phenomena	N = 221	18 to 25 M = 21.56 SD = 1.42	42	Data showed a positive correlation between Internet gaming disorder risk and some dissociative experiences: depersonalization and derealization, absorption and imaginative involvement, and passive influence
Delfabbro et al. (2012)	To conduct comparisons of the extent to which male and female problem gamblers report a range of potentially visible behavioral indicators of problematic gambling	N = 1,185 fortnightly gamblers (n = 338 problem gamblers as classified by the Problem Gambling Severity Index)	18–98 –		Men differed the most between problem gamblers and non-problem gamblers, either through signs of emotional distress or through trying to hide their presence on the gaming floor from others. Among women, signs of anger, declining caregiving and attempts to get credit were the most prominent indicators
Dennis et al. (2017)	To investigate whether unmet basic psychological needs evolve toward a level of psychological vulnerability that puts older adults who gamble at risk for becoming problem gamblers	N = 379	60–93 M = 68.0 SD = 6.9	42	Satisfaction of basic psychological needs also moderated the negative effects of socioeconomic status on risky gambling behavior
Dufour et al. (2019)	To examine factors influencing trajectories of poker players	N = 304	> 18 M = 32.5 SD = 11.5	88	Symptoms of depression were significant predictors of the third trajectory, while impulsivity predicted the second
Fluharty et al. (2022)	To examine a range of predictors of (i) gambling during the first strict lockdown, (ii) gambling more frequently during this strict lockdown compared to before lockdown, and (iii) continued increased frequency of gambling during the relaxation of restrictions	N = 556	> 18	51	As lockdown restrictions eased, individuals of ethnic minority backgrounds, who were current smokers, and with lower educational attainment were more likely to continue gambling more than usual
Flórez et al. (2016)	To examine the relation among these four factors in pathological gamblers	N = 144 (n = 44 PPG; n = 100 HC)	> 18 PPG M = 43.2 SD = 11.8 HC M = 50.3 SD = 8.4	PPG 98 HC 95	Pathological gamblers showed higher levels of impulsivity and more implicit attitudes towards gambling than the control group Active pathological gamblers showed more impulsivity, more explicit gambling cognitions and alcohol dependence than inactive gamblers
Gori et al. (2021)	To apply a Comprehensive Model of Addiction and to delve deeper the dimensions involved in the vulnerability and maintenance of the disease	N = 253	> 18 M = 47.8 SD = 12.4	83	Alexithymia may increase the risk of developing a gambling disorder, mediating the association between insecure attachment and dissociation
Hing and Russell (2020)	This study used an EGM-specific measure Problem Gambling Severity Index to achieve its aim of identifying risk factors specifically associated with problematic EGM play	N = 1,932	> 18 M = 41.84 SD = 16.46	53.1	High-risk EGM players tended to be younger, male, more educated, never married, had higher (although still modest) incomes, and were more likely to have problems with alcohol
Hing et al. (2016a)	To develop separate risk factor models for gambling problems for males and for females and identify gender-based similarities and differences	N = 8,917	18–24	–-	Important predictors of risk status among female gamblers included: age 18–24, not speaking English at home, living in a group household, unemployed or unemployed, private betting, EGM, scratch or bingo, and taking money from others Gambling is there for social reasons, to win money or for general entertainment Risk factors for men include: age 18–24, not speaking English at home, low education, living in a group household, unemployed or inactive, gambling on EGM, table games, racing, sports or lotteries, and winning at non-social gambling Reasons for money or general entertainment
Hing et al. (2016b)	To identify demographic, behavioral, and normative risk factors for gambling problems amongst sports bettors	N = 639	18–24	64	High-risk sports bettors were young, male, single, educated, full-time employed or full-time students
Hing et al. (2017)	Determine demographic, behavioral, and psychological risk factors for gambling problems on online EGMs, online sports betting and online race betting, and compare the characteristics of problematic online gamblers on each of these online forms	N = 162 (n = 64 non-problematic online EGM gamblers; n = 98 problematic online EGM gamblers)	> 18 NPG M = 39.6 SD = 15.3 PPG M = 36.8 SD = 12.7	NPG 69 PPG 71	Risk factors for online sports betting were being male, younger, lower income, born outside Australia, speaking a language other than English, more frequent sports betting, higher levels of psychological distress and negative attitudes towards gambling Risk factors for online match betting are being male, younger, speaking a language other than English, more frequent match betting, more forms of gambling, self-reported semi-professional/professional gamblers, illegal drug use while gambling, and negative attitudes towards gambling
Jiménez-Murcia et al. (2020)	To identify empirical clusters of GD based on several measures of the severity of gambling behavior and considering the potential role of patient sex as a moderator	N = 512	> 18 M = 43.0 SD = 13.5	92	The most severe GD traits were associated with single and multiple gambling preferences for non-strategic and strategic games, early gambling activity, higher impulsivity, higher dysfunctional scores for the harm avoidance and self-regulation personality traits, and more lifetime stressful life events
Kim et al. (2016)	Research had three aims; (a) explore gender differences (e.g., demographics, co-morbidities, gambling variables) among helpline callers using psychometrically robust measures, (b) assess whether gender predicts treatment utilization following contact and (c) assess whether systematic gender differences exist on gambling and psychosocial outcomes at 3-, 6- and 12-month follow-ups	N = 147	> 18 M = 39.3 SD = 13.4	43	Women compared to men described greater problem severity and shorter problem duration and were more likely to report video game machines as their most problematic form of gambling, greater distress, and lower quality of life Men, despite the lower severity and distress of the problem, were more likely to access treatment after contacting the helpline
Landreat et al. (2020)	To identify a typology of gamblers based on clinical and gambling characteristics, and to investigate factors associated with these different profiles in a large representative sample of gamblers	N = 628 (n = 256 NPG; n = 169 PGWT; n = 203 PGST)	> 18 M = 43.4 SD = 12.9	67	Anxiety or depressive symptoms may be the result of problem gambling problems
Medeiros et al. (2016)	To evaluate the association between anxiety symptoms, gambling activity, and neurocognition across the spectrum of gambling behavior	N = 143	18–29 M = 24.8 SD = 2.9	52	Anxiety may be associated with relevant clinical variables in the broad spectrum of gambling activity
Price et al. (2022)	Examined online gambling behavior during COVID-19 land-based gambling restrictions and associations with changes in mental health, impacts on household income due to the pandemic, financially focused motivations, and symptoms of gambling problems	N = 940	> 18 M = 33.9 SD = 16.4	53	An association between altered online gambling participation during COVID-19 and increased mental health problems, increased gambling problem severity, negative impact on household income, and a more financially oriented self-concept
Rodriguez-Monguio et al. (2017)	To assess the prevalence of co-occurring addictive behaviors and mental health disorders in pathological gamblers seeking treatment across all levels of care during a comprehensive period of time before casinos become operative in Massachusetts, in 2018	N = 869	> 18	71	The most common comorbidities in patients with a primary diagnosis of gambling addiction were anxiety disorders, mood disorders, and substance use disorders
Swanton et al. (2021)	To explore the relationship between financial well-being and changes in gambling behavior during the coronavirus 2019 (COVID-19) shutdown	N = 764	18–82 M = 43.8 SD = 14.8	85	Self-reported financial well-being has a strong negative association with gambling problems but is not related to gambling participation
Volberg et al. (2017)	To identify demographic characteristics, health-related behaviors, and gambling participation variables that statistically predicted the odds of being a problem or pathological gambler	N = 7,121	> 18 M = 27.8 SD = 9.1	42	Male participants, who smoked daily, suffered from depression, had increased effects of home gambling, had low levels of education, started gambling before age 21, or played in casinos, game rooms, or jogging tracks in the U.S. state. Problem or pathological gamblers were 1.5 to 2.3 times more likely last year than participants who did not fall into any of these categories
Wong et al. (2017)	To examine the main and interaction effects of gambling-related cognitions and psychological states on the gambling severity among a group of PPGs in Hong Kong	N = 177	18–65	100	Participants who reported a higher level of stress had more stable and serious gambling problems than those who reported a lower level of stress, regardless of their level of gambling-related cognitions