Objective: Online gambling has increased the accessibility and range of gambling products available to people all over the world. This trend has been particularly noticeable in the United Kingdom. ...Cryptocurrency-based gambling is a new, largely unregulated, way to gamble online, which uses mostly anonymous blockchain-based technologies, such as Bitcoin. The present research investigated consumer protection features of 40 frequently visited and U.K.-accessible cryptocurrency-based online gambling operators. Method: A content analysis was performed by visiting all 40 cryptocurrency-based online operators and recording their safer gambling and consumer protection practices. Coded features included aspects of the sign-up process, features of any safer gambling pages, customer support practices, and Identity verification. Results: Results revealed significant failings in the account registration process; none of the operators verified the identity of new users, and 35% required only an email or no personal information for sign-up. Overall, 37.5% of operators offered no safer gambling tools and a further 20% offered only one. Additionally, 64.7% of operators continued to email promotional material after being informed of a user's impaired control when gambling. Less than half of the analyzed operators held a valid license (47.5%), and none of the operators with an available deposit page required identity verification before enabling deposits. Conclusions: These results highlight the potential risks for young and vulnerable individuals, especially when a lack of identity verification is paired with the inherent anonymity of cryptocurrencies. Furthermore, it emphasizes the need for greater policy and research attention toward cryptocurrency-based online gambling.
Public Health Significance Statement
Cryptocurrency-based gambling is a fast-growing gambling format, and top operators are heavily investing in consumer reach through sports advertisements. However, information on the safer gambling and consumer protection practices of frequently visited operators is nonexistent. The findings in this study showed significant failings in consumer protection and safer gambling practices, which suggest an increased risk of gambling-related harm in cryptocurrency-based gambling.
Since the 1990s, gambling has been considered a public health concern. The characteristics of games and the environments in which gambling is carried out are major causes of gambling disorder. ...Information and communication technologies (e.g., Internet, mobile phones) have been adapted for gambling, and new forms of online gambling have appeared.
Online gambling is currently legal in many countries worldwide, and it is continuing to expand globally. In Spain, online gambling has been legal since 2012, when the government authorized companies to operate in this space. Many other countries have been through a similar process of legalization and the promotion of online gambling.
In this study, we analyzed the prevalence of gambling disorder in Spain, as well as differences between online and traditional gambling, according to sex and age group. Prevalence indicators of gambling disorder were higher than expected, and this result was especially evident with regard to online gambling.
Gambling problems have consistently been linked to suicidality, including suicidal ideation, attempts, and suicide. However, the magnitude of the relationship has varied significantly across studies ...and the potential causal link between gambling problems and suicidality is currently unclear. A meta-analytic literature review was conducted to (a) synthesize pooled prevalence rates of suicidality among individuals with gambling problems; (b) determine if individuals with gambling problems had an increased likelihood of reporting suicidality compared to individuals without gambling problems; and (c) review evidence on causality and directionality. A search in Web of Science, APA PsycInfo, APA PsycNet, Medline, CINAHL, ProQuest, Embase, and Google Scholar electronic databases identified 107 unique studies (N = 4,691,899) that were included for review. Studies were included if they were available in any European language and provided sufficient data for the calculation of prevalence rates or effect sizes. Two researchers extracted the data independently using a predefined coding schema that included the Newcastle-Ottawa Quality Assessment Scale. Random-effects meta-analyses yielded pooled prevalence rates of 31.6% (95% CI 29.1%, 34.3%) for lifetime suicidal ideation and 13.2% (95% CI 11.3%, 15.5%) for lifetime suicide attempts. Individuals with gambling problems had significantly increased odds of reporting lifetime suicidal ideation (OR = 2.17, 95% CI 1.90, 2.48) and lifetime suicide attempts (OR = 2.81, 95% CI 2.23, 3.54) compared to individuals without gambling problems. Two studies reported that individuals with pathological gambling had an increased risk of dying by suicide. Metaregression analyses suggested that the risk of study bias was positively related to the prevalence rates of suicidal ideation. Sex proportions were found to moderate the odds of suicidal ideation, but the direction of the effect was inconsistent. For suicide attempts, psychiatric comorbidity and sample size were positively and inversely, respectively, associated with prevalence rates. The synthesis indicates that suicidality is common among individuals with gambling problems and hence should be addressed by help agencies. Inferences on causality and directionality are hampered by a lack of longitudinal studies.
Public Significance Statement
This meta-analytic literature review of 107 studies shows that lifetime suicidal ideation and suicide attempt(s) are commonly reported among individuals with gambling problems and that individuals with gambling problems have an increased likelihood of reporting lifetime suicidal ideation, suicide attempt(s), and dying by suicide compared to the general population. The observed increased likelihood of suicidality should be noted and addressed by help agencies and policymakers. Little is known about the directionality and mechanisms underlying this relationship, which needs to be investigated in future high-quality longitudinal research.
Background and Aims
Participating in online gambling is associated with an increased risk for experiencing gambling‐related harms, driving calls for more effective, personalized harm prevention ...initiatives. Such initiatives depend on the development of models capable of detecting at‐risk online gamblers. We aimed to determine whether machine learning algorithms can use site data to detect retrospectively at‐risk online gamblers indicated by the Problem Gambling Severity Index (PGSI).
Design
Exploratory comparison of six prominent supervised machine learning methods (decision trees, random forests, K‐nearest neighbours, logistic regressions, artificial neural networks and support vector machines) to predict problem gambling risk levels reported on the PGSI.
Setting
Lotoquebec.com (formerly espacejeux.com), an online gambling platform operated by Loto‐Québec (a provincial Crown Corporation) in Quebec, Canada.
Participants
N = 9145 adults (18+) who completed the survey measure and placed at least one bet using real money on the site.
Measurements
Participants completed the PGSI, a self‐report questionnaire with validated cut‐offs denoting a moderate‐to‐high‐risk (PGSI 5+) or high‐risk (PGSI 8+) for experiencing past‐year gambling‐related problems. Participants agreed to release additional data about the preceding 12 months from their user accounts. Predictor variables (144) were derived from users’ transactions, apparent betting behaviours, listed demographics and use of responsible gambling tools on the platform.
Findings
Our best classification models (random forests) for the PGSI 5+ and 8+ outcome variables accounted for 84.33% (95% CI = 82.24–86.41) and 82.52% (95% CI = 79.96–85.08) of the total area under their receiver operating characteristic curves, respectively. The most important factors in these models included the frequency and variability of participants’ betting behaviour and repeat engagement on the site.
Conclusions
Machine learning algorithms appear to be able to classify at‐risk online gamblers using data generated from their use of online gambling platforms. They may enable personalized harm prevention initiatives, but are constrained by trade‐offs between their sensitivity and precision.
In recent years researchers have emphasized the importance of artificial intelligence (AI) algorithms as a tool to detect problem gambling online. AI algorithms require a training dataset to learn ...the patterns of a prespecified group. Problem gambling screens are one method for the collection of the necessary input data to train AI algorithms. The present study’s main aim was to identify the most significant behavioral patterns which predict self-reported problem gambling. In order to fulfil the aim, the study analyzed data from a sample of real-world online casino players and matched their self-report (subjective) responses concerning problem gambling with the participants’ actual (objective) gambling behavior. More specifically, the authors were given access to the raw data of 1,287 players from a European online gambling casino who answered questions on the Problem Gambling Severity Index (PGSI) between September 2021 and February 2022. Random forest and gradient boost machine algorithms were trained to predict self-reported problem gambling based on the independent variables (e.g., wagering, depositing, gambling frequency). The random forest model predicted self-reported problem gambling better than gradient boost. Moreover, problem gamblers showed a distinct pattern with respect to their gambling based on the player tracking data. More specifically, problem gamblers lost more money per gambling day, lost more money per gambling session, and deposited money more frequently per gambling session. Problem gamblers also tended to deplete their gambling accounts more frequently compared to non-problem gamblers. A subgroup of problem gamblers identified as being at greater harm (based on their response to PGSI items) showed even higher values with respect to the aforementioned gambling behaviors. The study showed that self-reported problem gambling can be predicted by AI algorithms with high accuracy based on player tracking data.
Youth problem gambling has become an emergent public health issue, and adolescents constitute a vulnerable age group for the development of gambling-related problems. Although there is research ...concerning the risk factors of youth problem gambling, rigorous evaluations of the effectiveness of preventive initiatives is still rare. The present study evaluated the efficacy of an integrative intervention to prevent youth problem gambling based on a multidimensional set of factors including gambling-related knowledge, misconceptions, attitudes, gambling frequency, amount of money spent, total hours spent gambling per week, and sensation seeking. A pre- and post-test design was performed with 111 Portuguese high-school students randomly assigned to two groups (experimental and control). The findings demonstrated that the intervention was effective in improving correct knowledge about gambling, reducing misconceptions and attitudes, and in decreasing the total hours spent gambling per week. The intervention was also effective in reducing the number of at-risk/problem gamblers during the study period. Furthermore, these findings were stable after a 6-week follow-up. Overall, the intervention program appeared to be effective in correcting some gambling-related behaviours, and provides suggestions for future interventions.
Both the Problem Gambling Severity Index (PGSI) and the Short Gambling Harms Screen (SGHS) purport to identify individuals harmed by gambling. However, there is dispute as to how much individuals are ...harmed, conditional on their scores from these instruments. We used an experienced utility framework to estimate the magnitude of implied impacts on health and wellbeing.
We measured health utility using the Short Form Six-Dimension (SF-6D), and used this as a benchmark. All 2603 cases were propensity score weighted, to balance the affected group (i.e., SGHS 1+ or PGSI 1+ vs 0) with a reference group of gamblers with respect to risk factors for gambling harm. Weighted regression models estimated decrements to health utility scores attributable to gambling, whilst controlling for key comorbidities.
We found significant attributable decrements to health utility for all non-zero SGHS scores, as well as moderate-risk and problem gamblers, but not for PGSI low-risk gamblers. Applying these coefficients to population data, we find a similar total burden for both instruments, although the SGHS more specifically identified the subpopulation of harmed individuals. For both screens, outcomes on the SF-6D implies that about two-thirds of the 'burden of harm' is attributable to gamblers outside of the most severe categories.
Gambling screens have hitherto provided nominal category membership, it has been unclear whether moderate or 'at-risk' scores imply meaningful impact, and accordingly, population surveys have typically focused on problem gambling prevalence. These results quantify the health utility decrement for each category, allowing for tracking of the aggregate population impact based on all affected gamblers.
A large contemporary UK cohort study, the Avon Longitudinal Study of Parents and Children, was used to investigate gambling behavior and to explore the antecedents of regular gambling in the ...17–24-year age group. Participants completed computer-administered gambling surveys in research clinics, on paper, and online. The sample sizes were 3566 at age 17 years, 3940 at 20 years, and 3841 at 24 years; only 1672 completed all three surveys. Participation in gambling in the last year was reported by 54% of 17-year-olds, rising to 68% at 20 years, and 66% at 24 years, with little overall variance. Regular (weekly) gambling showed a strong gender effect, increasing among young men from 13% at 17 years to 18% at 20 years, and 17% at 24 years. Although gambling frequency increased between the ages of 17 and 20 years, gambling behaviors showed little variance between 20 and 24 years, except online gambling and betting on horseraces. The commonest forms of gambling were playing scratchcards, playing the lottery, and private betting with friends. Gambling on activities via the internet increased markedly between 17 and 24 years, especially among males. In the fully adjusted model, individual antecedents of regular gambling were being male, and having a low IQ, an external locus of control, and high sensation seeking scores. Parental gambling behavior and maternal educational background were associated with regular gambling in both sexes. Regular gambling was associated with smoking cigarettes and frequent and harmful use of alcohol, but no associations with depression were found.
Accurate and rational gambling beliefs have been found to play a protective role against gambling disorder (GD) and add unique insights into the prevention and intervention of gambling-related harms. ...Adopting the Protective Gambling Beliefs Scale (PGBS) as a measurement tool of these gambling beliefs, this study tested its psychometric properties and whether these gambling beliefs were associated with responsible gambling (RG) behaviors with a probability community sample of adult gamblers (N = 464) in Macao, China. Consistent with the past studies, PGBS was found unidimensional with high reliability. The construct validity of PGBS was verified by its negative associations with gambling-related interpretive biases and GD symptoms. Moreover, we found a significant and positive association between protective gambling beliefs and RG behaviors after controlling for the effects of gambling-related interpretive biases and demographic variables. Conventional approaches to gambling harm reduction focus more on maladaptive cognition. Our findings may offer empirical evidence that protective gambling beliefs also help reduce gambling-related harms and enable gamblers to keep their gambling at a relatively safe level. PGBS is not only a valid and reliable instrument to measure gamblers' protective gambling beliefs but also a potential means to promote RG practices.
Voluntary self-exclusion from gambling is a common harm reduction tool in individuals with a gambling disorder. Previous data have demonstrated that many gamblers breach their own self-exclusion, ...typically through other online services outside the jurisdiction in which they are self-excluded. The present study aimed to carry out a new follow-up measure-similar to previous studies in the same setting-of self-exclusion and its breaching in Sweden, in order to allow for the follow-up assessment of a nationwide, multi-operator self-exclusion system introduced in Sweden in 2019.
A web survey to the web panel of a market survey company addressed 1505 past-year gamblers, who responded to a number of questions about gambling habits, including screening for gambling problems using the Problem Gambling Severity Index and self-exclusion-related items corresponding to previous studies.
Nine percent of past-year gamblers had self-excluded using the Spelpaus service. In logistic regression, self-exclusion was significantly associated with gambling problems, past-year online casino gambling, and absence of online poker gambling. Among self-excluders, 49 percent had ever gambled despite being self-excluded. Among those breaching their self-exclusion, the most common gambling types during self-exclusion were online casino (82 percent), sports betting (47 percent) and lotteries (43 percent).
Self-exclusion remains a popular harm reduction tool against problem gambling, more common than in previous studies, mostly in individuals with recent gambling problems and in online casino gamblers. However, breaching self-exclusion is somewhat more common than in previous research. Online casino represents the most common means of self-exclusion breaching. Policy-making in the area needs to further address the risk of breaching one's self-exclusion and may further address the risk of overseas gambling.
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