Problem and non-problem gamblers: a cross-sectional clustering study by gambling characteristics

Risk Factors of At-Risk/Problem Gambling Among Young Adult Spanish Students

The gambling industry in Spain has experienced sustained growth in recent years. This has raised concerns about its potential impact, particularly during emerging adulthood, which is a high-risk period for the development of problematic gambling behaviors. The aim of this study was to estimate the prevalence of problem gambling in young Spanish adults and to examine individual factors associated with at-risk or problem gambling. The participants were 2762 young adults studying at university and vocational schools (64.16% women, Mage = 19.47; SD = 1.64) recruited in three Spanish autonomous communities. Individuals completed an e-assessment including sociodemographic variables, gambling-related characteristics, and the Problematic Gambling Severity Index. A total of 3.51%, 2.14%, and 0.51% were low risk, moderate risk, and problem gamblers, respectively. Predictors of at-risk/problem gambling were early gambling onset (OR 0.790; 95% CI 0.672, .929), mixed versus offline modality gambling access (OR 2.216; 95% CI 1.396, 3.516), and having gambled in the previous year in a casino (OR 1.910; 95% CI 1.194, 3.056), sports betting (OR 1.597; 95% CI 1.003, 2.542), or electronic gaming machines (OR 2.111; 95% CI 1.064, 4.189). Gambling is ubiquitous in the general population and a high proportion of young adults present low and moderate risk of problem gambling. Addressing gambling is a public health challenge and policies to regulate it should be targeted at delaying the age of onset and restricting access to online gambling.

Cluster analysis based on gambling variables and mental health in a clinical population of gamblers

BACKGROUND

Interest in characterizing individuals involved in addictive behaviors has been growing, which allows tailoring prevention and intervention strategies to the gambler's needs. The study aimed to 1) identify clusters of gamblers according to gambling-related characteristics and mental health; and 2) analyze differences in psychological variables between the clusters.

METHODS

A total of 83 participants undergoing treatment for gambling disorder (Mage = 45.52, 51.8 % female) completed a set of questionnaires. Hierarchical cluster analysis was performed to classify gambling based on gambling variables (i.e., gambling severity and gambling motives) and mental health (i.e., depression, anxiety, and hostility). Several ANOVAs were conducted to illustrate the distinguishing features of each cluster, encompassing both the variables included in the cluster analysis and other relevant psychological variables.

RESULTS

Findings suggest that gamblers can be classified into three clusters based on these variables: 1) "high gambling severity and good mental health," 2) "high gambling severity and poor mental health," and 3) "low gambling severity and good mental health." These clusters were differentiated as a function of psychological variables, such as emotional dependence, alexithymia, and stressful life events.

CONCLUSIONS

Classifying gamblers according to their profile provides a better understanding of their needs and problems, allowing for a more tailored approach in terms of prevention and intervention strategies.

Impact of Gambling on the Internet on Middle-Term and Long-Term Recovery from Gambling Disorder: A 2-Year Longitudinal Study

Online gamblers are more likely to experience gambling problems. The main objective was to compare the frequency of recovery one (middle-term) and two (long-term) years after treatment initiation, according to the gambling medium (Internet vs. land-based gambling). The secondary objectives were (i) to compare online and offline gamblers at inclusion and (ii) to investigate whether the gambling medium was a predictive factor of recovery. Outpatients beginning treatment for a GD (n = 237) were assessed at inclusion (treatment initiation) and after 1 and 2 years. Bivariate analyses were performed to compare online and offline gamblers at inclusion and on the frequency of recovery at one and two years. Two multivariate logistic regressions were then performed to identify factors associated with middle- and long-term recovery. The majority of patients achieved middle (74.2%) and long-term (78.9%) recovery, with no difference between online and offline gamblers. The gambling medium was not a predictive factor of recovery. Patients with a higher perceived self-efficacy (OR = 1.04 [1.01-1.07], p = .046) and having no history of mood disorders (OR = 11.18 [2.53-49.50], p < .001) at inclusion were more likely to achieve middle-term recovery, while long-term recovery was associated with a lower level of sensation seeking (OR = 0.67 [0.48-0.92], p = .015) at treatment initiation. Online gambling did not seem to influence middle- and long-term recovery compared to offline gambling. Enhancement of perceived self-efficacy and treatment of mood disorders, and treatment strategies focused on sensation-seeking may represent helpful care strategies for favouring achievement of middle-term recovery and maintenance of long-term recovery, respectively. ClinicalTrials.gov NCT01248767, date of first registration: November 25, 2010.

A longitudinal study of gambling motives, problem gambling and need frustration

Gambling motives are an important element in understanding the development of problem gambling, yet most of the recent studies investigating their role in problem gambling have been cross-sectional. This study analyzed the links between gambling motives and problem gambling using a longitudinal study design. The moderating effect of the frustration of basic psychological needs was also assessed. The study sample with 1,022 participants (48.43% female, M_age = 49.50 years) was surveyed at three timepoints (T1-T3) in 6-month intervals. The Problem Gambling Severity Index (PGSI) was used to measure problem gambling and need frustration was assessed with The Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS). The data were analyzed using a multilevel mixed-effects regression model where PGSI was the outcome variable. Gambling motives and need frustration were the predictors while psychological distress (measured with the 5-Item Mental Health Inventory, MHI-5), offshore/onshore online gambling, and socio-demographic factors were used as control variables. All the motives predicted problem gambling individually over time. In contrast, motives to escape, to win money, and to compete along with need frustration predicted problem gambling over time in the full model. In addition, money motive and need frustration had an interaction effect so that higher need frustration combined with money motive predicted more severe gambling problems. The results of this study provide a valuable longitudinal perspective on gambling motives, frustration of basic psychological needs, and gambling problems which can be used to develop and improve treatment efforts and programs of problem gambling.

Risk Factors for Gambling Disorder: A Systematic Review

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

The relationship of gambling with sensation-seeking behavior and psychological resilience in university students

PURPOSE

We aimed to analyze the relationship of gambling with university students' sensation-seeking behavior and psychological resilience.

DESIGN AND METHODS

This cross-sectional and correlational research was performed with 650 students. Data collection tools were Personal Information Form, South Oaks Gambling Screen (SOGS), Zuckerman Sensation-Seeking Scale (ZSSS), and Brief Resilience Scale (BRS).

FINDINGS

Of the students, 1.2% were probably pathological gamblers. There was a positive relationship between students' SOGS and ZSSS scores (p < 0.05). However, there was no relationship between students' SOGS and BRS scores (p > 0.05).

PRACTICE IMPLICATIONS

Psychological support units and instructors take an active role in determining the gambling behaviors of students and related factors in advance, raising awareness of gambling, and preventing the negative effects of these behaviors in faculty campuses.

Psychopathogical status and personality correlates of problem gambling severity in sports bettors undergoing treatment for gambling disorder

BACKGROUND AND AIMS

Sports betting has been barely explored independently from other gambling behaviors. Little evidence is available regarding the factors affecting its severity in a clinical sample. The current study explores new determinants for sports betting severity in Spain by the inclusion of psychopathological distress and personality factors.

METHODS

A sample of 352 Spanish sports bettors undergoing treatment for gambling disorder was recruited. Multiple regression models were used to evaluate the effects of sociodemographic variables, the age of onset of gambling behavior, the global psychopathological distress (SCL-90R GSI) and the personality profile (TCI-R) on sports betting severity and their influence over frequency (bets per episode) and debts due to gambling.

RESULTS

We found that older age, higher psychopathological distress, lower self-directedness level, and higher novelty seeking level were predictors of gambling severity in Spanish sports bettors. The highest betting frequency was found in men, with the lowest education levels but the highest social status, the highest psychopathological distress, reward dependence score, and self-transcendence trait and the lowest persistence score. Debts were also associated to higher score in cooperativeness as well as older age.

DISCUSSION AND CONCLUSIONS

Our findings call for further exploration of factors affecting sports betting severity regarded as a separate gambling entity subtype, as some of the traditional factors typically found in gamblers do not apply to sports bettors or apply inversely in our country. Consequently, sports bettors might deserve specific clinical approaches to tackle the singularities of their gambling behavior.

Gambling Marketing Strategies and the Internet: What Do We Know? A Systematic Review

Background: The gambling industry has developed many types of gambling on Internet in recent years. Gambling is a social activity for a majority of the world population, but problem gambling (PG) can emerge. The trajectories of gamblers from initiation to PG development are influenced by many variables, including individual and environmental variables and also variables linked to the gambling characteristics. Marketing has been reported to influence gamblers' perceptions and behaviors, but this is not as clear for digital marketing. Digital gambling marketing is broad, ranging from the marketing of gambling websites to communication and advertising on the social media and networks. The objective of this article was to fill this gap by conducting a systematic literature review in order to answer the following questions: (1) What are the strategies of digital gambling marketing? (2) What is the effect of this exposure on gambling representations, intentions and practices? Method: A systematic review was conducted following the PRISMA guidelines on Pubmed database (Medline) from February 2020 to March 2020 and Scopus. Existing papers published between January 2000 and February 2020 were identified by searching with this algorithm: ((("internet"[MeSH Major Topic] OR (communications[All Fields] AND media[All Fields])) OR ("social media"[MeSH Terms] OR ("social"[All Fields] AND "media"[All Fields]) OR "social media"[All Fields])) AND "gambling"[MeSH Major Topic]) AND ("marketing"[MeSH Terms] OR "marketing"[All Fields]), in title, keywords or abstract. Results: Ninety-one candidate studies were selected, 21 studies were selected for the systematic review. Sport appeared as a specific target of online gambling marketing. A growing range of platforms for online sport betting and the development of strategies on the social media were identified. Regarding content, a systematic association between sport and gambling was highlighted. Vulnerable populations, such as young people, appeared to be at high risk of exposure to gambling marketing. Conclusion: Little data is available on the strategies of digital gambling marketing or on exposure to it. Sport could be the first target for future research to understand how the industry is targeting specific populations, and what influence these strategies could have on PG development.