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Growth of Internet gambling has fuelled concerns about its contribution to gambling problems. However, most online gamblers also gamble on land-based forms, which may be the source of problems for some. Studies therefore need to identify the problematic mode of gambling (online or offline) to identify those with an online gambling problem. Identifying most problematic form of online gambling (e.g., EGMs, race betting, sports betting) would also enable a more accurate examination of gambling problems attributable to a specific online gambling form. This study pursued this approach, aiming to: (1) determine demographic, behavioral and psychological risk factors for gambling problems on online EGMs, online sports betting and online race betting; (2) compare the characteristics of problematic online gamblers on each of these online forms. An online survey of 4,594 Australian gamblers measured gambling behavior, most problematic mode and form of gambling, gambling attitudes, psychological distress, substance use, help-seeking, demographics and problem gambling status. Problem/moderate risk gamblers nominating an online mode of gambling as their most problematic, and identifying EGMs (
Participation in online gambling continues to increase in tandem with its deregulation, prolific advertising, the widespread uptake of computer and mobile technologies, and increased availability of high speed internet access (
A second issue potentially confounding an accurate understanding of problem online gambling is that measures of problem gambling, such as the PGSI (
Studies need to identify the problematic mode of gambling (online or offline) and problematic form of gambling (e.g., EGMs, race betting, sports betting) to be able to more accurately characterize those with a gambling problem attributable to a specific online gambling form. This study pursues this approach and aims to: (1) determine demographic, behavioral and psychological risk factors for gambling problems on online EGMs, online sports betting and online race betting; and (2) compare the characteristics of problematic online gamblers on each of these online forms. Understanding risk factors is important to inform improved targeting of harm minimisation and other public health measures for Internet gambling. Further, identifying risk factors for each online gambling form will enable additional tailoring of these measures to high-risk consumers who engage in each of these activities. Literature on the characteristics of online gamblers and online problem gamblers, along with associated risk factors, is now briefly reviewed to contextualize this study.
Several studies have compared online gamblers to offline gamblers (
Research has also compared problem online gamblers to non-problem online gamblers to determine risk factors for gambling problems amongst Internet gamblers. Compared to non-problem online gamblers, problem online gamblers tend to be male, younger, to gamble on a wider range of activities, to have higher gambling expenditure, to hold more erroneous gambling beliefs, and to hold more negative attitudes toward gambling (
While the above two types of comparisons are legitimate and informative, confusion arises when results are interpreted as meaning that online gambling is necessarily the source of gambling problems amongst those categorized as problem online gamblers. Indeed, the relatively high problem gambling rates found amongst online gamblers (
To our knowledge, only one study has analyzed the characteristics of problem gamblers whose gambling problems relate specifically to online gambling (
In summary, most studies of online problem gamblers have not determined whether their gambling problem is specifically related to an Internet mode of gambling. These analyses therefore include online gamblers with offline gambling problems. This lack of distinction of most problematic gambling mode amongst dual-mode gamblers means that risk factors for online gambling remain uncertain. Further, these studies have not distinguished which form of online gambling is most problematic. They have therefore been unable to identify risk factors for specific forms of online gambling. This study seeks to overcome these issues by conducting analyses comparing those who attribute their problems to each of three forms of online gambling (EGMs, sports betting, race betting) to those who also gamble online on those forms and have not experienced problems, and then comparing the groups who attribute their problems to these three online forms to each other. These analyses are the first to specifically study the characteristics and risk factors of gamblers whose problems develop in each of these online gambling forms. Because of the early stage of this avenue of research, the current research is considered exploratory and no specific hypotheses are presented Similar to previous analyses of risk factors for different types and modes of gambling (e.g.,
A total of 4,594 eligible respondents completed an online survey which targeted Australian adults who had gambled in the past 12 months. Participants were recruited via advertisements on Internet gambling sites (
Inclusion criteria were: (a) non-problem or low-risk gamblers based on the Problem Gambling Severity Index (PGSI;
All respondents completed the 9-item PGSI (
Respondents completed questions related to their: gender (male/female), age (in years), education (recoded into those with or without a tertiary degree), income, work status (recoded into those working and not working, such as those on a pension, retired, unemployed), country of birth (Australia or other), and main language spoken at home (English or other).
Respondents completed questions related to: frequency of engagement on each of ten forms of gambling over the last 12 months (which was also used to calculate a variable that determines how many of the different forms they engage in), the percentage of engagement in each form that was done online, self-reported gambler status (professional, semi-professional, amateur), alcohol use when gambling (no vs. at least sometimes), and illicit drug use when gambling (no vs. at least sometimes). Those who stated that they had gambling-related problems were also asked whether they thought they needed help in relation to their gambling (no or yes) and whether they had ever sought any of 10 types of help in relation to their gambling (recoded into no help-seeking vs. yes to any combination of the 10 types of help).
Respondents completed the Kessler 6 (K6;
We conducted two major sets of analyses. First we compared the moderate-risk/problem gamblers whose problems reportedly stemmed from each online form (EGMs, sports betting, race betting) to non-problem/low-risk gamblers who engaged in that form online. Hereafter, we refer to the former group as problematic online gamblers and the latter group as non-problematic online gamblers.
The second set of analyses compared three groups of respondents: those whose problems reportedly stemmed from online EGMs, online race betting and online sports betting. All gamblers in these groups were moderate risk or problem gamblers and had nominated that online gambling on that form was responsible for their gambling-related problems.
Both sets of analyses followed the same structure: the relevant groups were compared on demographic, gambling behavior and psychological variables using bivariate, pairwise analyses. These were conducted using chi-square tests of independence (with pairwise tests of proportions where required) for categorical variables, or with one-way ANOVA (with Tukey pairwise comparisons where required) for continuous variables. For gambling frequency and percentage of gambling done online, Mann–Whitney
Given that discriminatory power can be shared between two or more independent variables, binary logistic regressions predicting problem gambling status were also conducted, to determine which of the significant variables from the bivariate analyses remained significant when controlling for the other variables. An alpha of 0.05 was used throughout.
In terms of missing data, the income question included an option to not disclose this information. This variable was considered in the bivariate analyses, but not the logistic regressions. We did also perform regressions including income, noting that it made very little difference to the results. Therefore, we have opted to report the results with income excluded. The K6 was also excluded from the regression results, as it was highly correlated (>0.6) with the PGSI (which was included as a predictor in the first comparisons, and was the factor that differentiated the groups in the latter comparisons). Tolerance checks were also conducted on the regression models and tolerance was >0.4 for all variables in all models, once K6 was excluded.
We also explored other possible analyses, including multinomial logistic regression models, where all predictors included in the binomial logistic regression models reported in
This study was carried out in accordance with the recommendations of the National Statement on the Ethical Conduct of Research Involving Humans, and was reviewed and approved by Southern Cross University Human Research Ethics Committee. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Because of the sensitive nature of the survey and the vulnerability of the sample, an informed consent preamble warned that some questions may have been confronting and challenging for some respondents, assured respondents of confidentiality and anonymity, and advised that respondents could withdraw their participation at any time. The survey contained contact details for telephone and online gambling help services.
Compared to non-problematic online EGM gamblers, problematic online EGM gamblers had significantly lower incomes. They gambled on EGMs more frequently, and were significantly more likely to use alcohol or illicit drugs at least some of the time when gambling. They were significantly more likely be experiencing psychological distress, and to have significantly more negative attitudes toward gambling (
Bivariate analyses comparing non-problematic and problematic online EGM gamblers.
Variable | Non-problematic online EGM gamblers ( |
Problematic online EGM gamblers ( |
Inferential statistics |
---|---|---|---|
Gender (% male) | 68.8 | 71.4 | χ2(1, |
Age (Mean/SD) | 39.6 (15.3) | 36.8 (12.7) | |
Education (% with degree) | 34.4∗ | 15.3 | χ2(1, |
Work status (% working) | 68.8 | 76.5 | χ2(1, |
Income ($000’s, Mean, |
86.1∗ (42.7) | 65.8 (42.7) | |
Country of birth (% Australia) | 75.0 | 83.7 | χ2(1, |
Main language spoken at home (% English) | 85.9 | 88.8 | χ2(1, |
Frequency of gambling on EGMs in last 12 months (median) | 2.0 | 4.0∗ | |
Percentage of EGM gambling online in last 12 months (median) | 50 | 60 | |
Number of forms in last 12 months (mean, |
5.2 (2.0) | 5.7 (1.8) | |
Gambler status | χ2(2, |
||
Professional | 0.0 | 1.0 | |
Semi-professional | 9.4 | 12.2 | |
Amateur (%) | 90.6 | 86.7 | |
Alcohol use when gambling (% at least sometimes) | 57.8 | 77.6∗ | χ2(1, |
Drug use when gambling (% at least sometimes) | 6.3 | 23.5∗ | χ2(1, |
Kessler 6 (grouped, % high psychological distress) | 0.0 | 21.4∗ | χ2(1, |
Kessler 6 score (mean, |
1.8 (3.1) | 7.4∗ (6.3) | |
Attitudes toward gambling (mean, |
1.2∗ (1.3) | 0.7 (1.0) |
All of the significant variables from the bivariate analyses were included in a multivariate logistic regression, with the exception of income and Kessler 6. Lowest tolerance between the variables was 0.85, indicating no multicollinearity problems. The overall model was significant χ2(5,
The results were similar to the bivariate analyses, with the exception that the gambling attitudes variable was no longer significant when controlling for the other variables in the model (
Logistic regression predicting non-problematic online EGM gamblers compared to problematic online EGM gamblers.
Variable | Wald | OR | 95% C.I. for OR |
||||
---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
Gambling Attitudes | -0.25 | 0.16 | 2.41 | 0.120 | 0.78 | 0.57 | 1.07 |
Compared to non-problematic online sports bettors, problematic online sports bettors were significantly more likely to be male, younger, have a lower income, be born outside of Australia, and speak a language other than English as their main language at home. They gambled on sports more frequently, but did less of their sports betting online, and were significantly more likely to consider themselves to be semi-professional gamblers. They were significantly more likely to use illicit drugs at least some of the time when gambling, to be experiencing psychological distress, and to have more negative attitudes toward gambling (
Bivariate analyses comparing non-problematic and problematic online sports bettors.
Variable | Non-problematic online sports gamblers ( |
Problematic online sports gamblers ( |
Inferential statistics |
---|---|---|---|
Gender (% male) | 90.4 | 98.3∗ | χ2(1, |
Age (Mean/ |
41.3∗ (14.0) | 31.1 (9.8) | |
Education (% with degree) | 42.9 | 44.8 | χ2(1, |
Work status (% working) | 79.2 | 77.3 | χ2(1, |
Income ($000’s, Mean, |
91.9∗ (44.6) | 82.3 (49.4) | |
Country of birth (% Australia) | 84.4∗ | 76.2 | χ2(1, |
Main language spoken at home (% English) | 93.1∗ | 77.9 | χ2(1, |
Frequency of gambling on sports in last 12 months (median) | 4.0 | 6.0∗ | |
Percentage of sports gambling online in last 12 months (median) | 100∗ | 98.0 | |
Number of forms in last 12 months (mean, |
4.5 (1.7) | 4.6 (2.2) | |
Gambler status | χ2(2, |
||
Professional | 2.3 | 3.3 | |
Semi-professional | 8.0 | 16.0∗ | |
Amateur (%) | 89.7∗ | 80.7 | |
Alcohol use when gambling (% at least sometimes) | 67.7 | 64.1 | χ2(1, |
Drug use when gambling (% at least sometimes) | 3.5 | 8.8∗ | χ2(1, |
Kessler 6 (grouped, % high psychological distress) | 0.7 | 12.7∗ | χ2(1, |
Kessler 6 score (mean, |
1.7 (2.6) | 6.4∗ (5.3) | |
Attitudes toward gambling (mean, |
1.4∗ (1.2) | 1.1 (1.0) |
All of the significant variables from the bivariate analyses were included in a multivariate logistic regression. Lowest tolerance between the variables was 0.89, indicating no multicollinearity problems. The overall model was significant χ2(10,
The results were relatively similar to the bivariate analyses, although percentage of sports betting conducted online, self-reported professional gambling status and drug use when gambling were no longer significant (
Logistic regression predicting non-problematic online sports bettors compared to problematic online sports bettors.
Variable | Wald | OR | 95% C.I. for OR |
||||
---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
% sports betting online | -0.00 | 0.01 | 0.04 | 0.851 | 1.00 | 0.99 | 1.01 |
Professional status (ref = amateur) | 1.58 | 0.453 | |||||
Semi-professional | 0.24 | 0.58 | 0.17 | 0.682 | 1.27 | 0.41 | 3.99 |
Professional | 0.50 | 0.55 | 0.84 | 0.358 | 1.65 | 0.57 | 4.84 |
Drug use while gambling (ref = no) | 0.15 | 0.38 | 0.16 | 0.689 | 1.17 | 0.55 | 2.47 |
Compared to non-problematic online race bettors, problematic online race bettors were significantly more likely to be male, younger, less likely to have a degree, and more likely to have a lower income, be born in Australia, and to speak a language other than English at home. They gambled on races more frequently, did less of their race betting online, and were significantly more likely to gamble on more forms of gambling. They were significantly more likely to rate themselves as a semi-professional gambler, and to use drugs at least sometimes when gambling. They were significantly more likely be experiencing psychological distress, and to have more negative attitudes toward gambling (
Bivariate analyses comparing non-problematic and problematic online race bettors.
Variable | Non-problematic online race bettors ( |
Problematic online race bettors ( |
Inferential statistics |
---|---|---|---|
Gender (% male) | 88.8 | 96.2∗ | χ2(1, |
Age (Mean/SD) | 43.5∗ (14.4) | 39.0 (12.8) | |
Education (% with degree) | 41.4∗ | 35.4 | χ2(1, |
Work status (% working) | 77.6 | 81.1 | χ2(1, |
Income ($000’s, Mean, |
91.4∗ (44.8) | 84.4 (43.7) | |
Country of birth (% Australia) | 85.0 | 89.7∗ | χ2(1, |
Main language spoken at home (% English) | 94.5∗ | 89.7 | χ2(1, |
Frequency of gambling on races in last 12 months (median) | 4.0 | 6.0∗ | |
Percentage of race betting online in last 12 months (median) | 95.0∗ | 90.0 | |
Number of forms in last 12 months (mean, |
4.5 (1.7) | 4.9∗ (1.8) | |
Gambler status | χ2(2, |
||
Professional | 2.4 | 0.7 | |
Semi-professional | 8.4 | 12.4∗ | |
Amateur (%) | 89.2 | 86.9 | |
Alcohol use when gambling (% at least sometimes) | 67.6 | 73.2 | χ2(1, |
Drug use when gambling (% at least sometimes) | 3.0 | 9.3∗ | χ2(1, |
Kessler 6 (grouped, % high psychological distress) | 0.5 | 11.7∗ | χ2(1, |
Kessler 6 score (mean, |
1.6 (2.5) | 5.1∗ (5.0) | |
Attitudes toward gambling (mean, |
1.4∗ (1.2) | 1.0 (1.0) |
All of the significant variables from the bivariate analyses were included in a multivariate logistic regression. Lowest tolerance between the variables was 0.83, indicating no multicollinearity problems. The overall model was significant χ2(12,
The results were relatively similar to the bivariate analyses, although education, country of birth and percentage of race betting conducted online were no longer significant (
Logistic regression predicting non-problematic online race bettors compared to problematic online race bettors.
Wald | Sig. | OR | 95% C.I. for OR |
||||
---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
Education (ref = tertiary) | 0.15 | 0.16 | 0.86 | 0.355 | 1.16 | 0.85 | 1.57 |
Country of birth (ref = not Australia) | 0.35 | 0.25 | 2.01 | 0.156 | 1.42 | 0.87 | 2.31 |
% of race betting online | 0.02 | 0.00 | 3.66 | 0.056 | 1.01 | 1.00 | 1.01 |
The following analyses identify distinguishing characteristics between problematic online EGM gamblers, problematic online race bettors and problematic online sports bettors.
Problematic online sports bettors and race bettors were significantly more likely to be male, have a tertiary degree and have higher incomes compared to problematic online EGM gamblers. Problematic online sports bettors were significantly younger compared to both problematic online EGM gamblers and problematic online race bettors. Problematic online sports bettors were significantly less likely to be born in Australia, and significantly more likely to speak a language other than English at home compared to problematic online race bettors, with problematic online EGM gamblers not significantly different to either of the other groups on these variables (
Descriptive statistics and inferential tests for demographic variables by most problematic online gambling form.
Variable | Problematic online EGM gamblers ( |
Problematic online sports bettors ( |
Problematic online race bettors ( |
Inferential statistics |
---|---|---|---|---|
Gender (% male) | 71.4a | 98.3b | 96.2b | χ2(2, |
Age (Mean/SD) | 36.8a (12.7) | 31.1b (9.8) | 39.0a (12.8) | |
Education (% with degree) | 15.3a | 44.8b | 35.4b | χ2(2, |
Work status (% working) | 76.5 | 77.3 | 81.1 | χ2(2, |
Income ($000’s, Mean, |
65.7a (42.4) | 81.7b (49.6) | 83.8b (44.3) | |
Country of birth (% Australia) | 83.7a,b | 76.2b | 89.7a | χ2(2, |
Main language spoken at home (% English) | 88.8a,b | 77.9b | 89.7a | χ2(2, |
Number of forms in last 12 months (mean, |
5.7a (1.8) | 4.6b (2.2) | 4.9b (1.8) | |
Gambler status | χ2(2, |
|||
Professional | 1.0a | 3.3a | 0.7a | |
Semi-professional | 12.2a | 16.0a | 12.4a | |
Amateur (%) | 86.7a | 80.7a | 86.9a | |
PGSI (mean, |
9.5a (6.0) | 8.3ab (4.6) | 7.4b (4.6) | |
Alcohol use when gambling (% at least sometimes) | 77.6a | 64.1b | 73.2a,b | χ2(2, |
Drug use when gambling (% at least sometimes) | 23.5a | 8.8b | 9.3b | χ2(2, |
Kessler 6 (grouped, % high psychological distress) | 21.4a | 12.7ab | 11.7b | χ2(2, |
Kessler 6 score (mean, |
7.4a (6.3) | 6.4ab (5.3) | 5.1b (5.0) | |
Attitudes toward gambling (mean, |
-1.27a (0.96) | -0.90b (0.99) | -1.00a,b (1.04) | |
Problems emerged after you first gambled online (% after) | 41.8a | 64.4b | 51.6a | χ2(2, |
Thought you needed help in relation to your gambling (% yes) | 54.1a | 35.9b | 44.0a,b | χ2(2, |
Ever sought help (% yes) | 46.9a | 31.5b | 27.5b | χ2(2, |
Problematic online EGM gamblers were significantly more likely to participate in more forms of gambling compared to both problematic online sports bettors and race bettors, and were significantly more likely to use illicit drugs when gambling compared to both of these groups. Problematic online EGM gamblers were also significantly more likely to drink alcohol at least sometimes when gambling compared to problematic online sports bettors. No significant differences were observed in terms of self-rated professional gambling status. Problematic online EGM gamblers had significantly higher PGSI scores compared to problematic online race bettors, with problematic online sports bettors not significantly different to either group.
Problematic online EGM gamblers were significantly more likely to be experiencing high psychological distress compared to problematic online race bettors, and also to have higher K6 scores, with problematic online sports bettors being not significantly different to either group. Problematic online EGM gamblers were significantly more likely to agree that gambling harms outweighed benefits compared to problematic online sports bettors, with problematic online race bettors not significantly different to either group (
Problematic online sports bettors were significantly more likely to state that their problems emerged after they first gambled online. Problematic online EGM gamblers were significantly more likely to think they needed help in relation to their gambling compared to problematic online sports bettors, and were significantly more likely to have sought help compared to problematic online sports and race bettors.
In order to account for any overlap between the bivariate analyses comparing problematic online EGM gamblers, sports bettors and race bettors, we conducted three separate binary logistic regressions. The first compared problematic online EGM gamblers to problematic online sports bettors; the second compared problematic online EGM gamblers to problematic online race bettors; and the third compared problematic online sports bettors to problematic online race bettors.
The predictors included in each regression were the variables that showed significant differences for each comparison. For example, attitudes toward gambling differed significantly between problematic online EGM gamblers and problematic online sports bettors, and was thus included in the first regression. However, problematic online race bettors did not differ significantly to either of the other groups, and thus this variable was not included in either of the other regression analyses.
For the regression comparing problematic online EGM gamblers and problematic online sports bettors, gender was an issue, as only three problematic online sports bettors were female, and thus was virtually constant for that group. Gender was therefore dropped from the regression.
The model was significant [χ2(9,
Multivariate logistic regression results predicting problematic online EGM gamblers vs. problematic online sports bettors.
Variable | Wald | OR | 95% C.I. for OR |
||||
---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
Professional status (ref = amateur) | 0.52 | 0.771 | |||||
Semi-professional | 0.28 | 0.42 | 0.45 | 0.503 | 1.32 | 0.58 | 3.01 |
Professional | 0.35 | 1.13 | 0.10 | 0.757 | 1.42 | 0.16 | 12.96 |
PGSI score | -0.01 | 0.03 | 0.10 | 0.747 | 0.99 | 0.93 | 1.06 |
Thought they needed help (ref = no) | -0.14 | 0.36 | 0.15 | 0.697 | 0.87 | 0.43 | 1.75 |
Sought help (ref = no) | -0.43 | 0.33 | 1.73 | 0.188 | 0.65 | 0.34 | 1.24 |
Gambling attitudes | 0.26 | 0.17 | 2.35 | 0.125 | 1.29 | 0.93 | 1.80 |
3.55 | 0.83 | 18.43 | <0.001 | 34.72 |
The model was significant [χ2(6,
Multivariate logistic regression results predicting problematic online EGM vs. problematic online race bettors.
Variable | Wald | OR | 95% C.I. for OR |
||||
---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
PGSI score | -0.04 | 0.03 | 2.25 | 0.134 | 0.96 | 0.91 | 1.01 |
Sought help (ref = no) | -0.49 | 0.30 | 2.58 | 0.108 | 0.62 | 0.34 | 1.12 |
0.78 | 0.55 | 1.99 | 0.158 | 2.18 |
The model was significant [χ2(3,
Multivariate logistic regression results predicting problematic online sports bettors vs. problematic online race bettors.
Variable | Wald | OR | 95% C.I. for OR |
||||
---|---|---|---|---|---|---|---|
Lower | Upper | ||||||
Main language spoken at home (ref = not English) | 0.46 | 0.29 | 2.43 | 0.119 | 1.58 | 0.89 | 2.81 |
Constant | -3.10 | 0.48 | 41.44 | <0.001 | 0.05 |
This paper is the first to our knowledge to identify risk factors specific to problematic gambling on three popular forms of online gambling – EGMs, race betting and sports betting. While previous studies have identified risk factors for problem gamblers who engage in Internet gambling (e.g.,
In relation to online EGM gambling, only a few risk factors emerged that distinguished problematic from non-problematic players. Not surprisingly, more frequent online EGM gambling increased the risk of gambling problems, which aligns with findings from risk curve analyses based on several large representative datasets in Canada (
Our bivariate analyses indicated that problematic online EGM gamblers were significantly more likely to be experiencing psychological distress, compared to non-problematic online EGM players. This result parallels findings for problem gamblers who play land-based EGMs, and who frequently report doing so to escape negative mood states (
The above findings suggest that interventions for problem online EGM players need to discourage frequent gambling on this activity and substance use while gambling. In domestic venues, steps might conceivably be taken by staff to monitor and intervene with patrons displaying these characteristics. However, in the case of online EGM providers, such measures are far more difficult to implement or enforce. High rates of psychological distress and higher PGSI scores in this cohort indicate that interventions involving professional treatment may be the most appropriate to address these underlying issues. In this study, a result of note is that the problematic online EGM gamblers were more likely to think they needed help for their gambling and to have sought help, than their race betting and sports betting counterparts. This increased ability of online EGM players to recognize they have a problem is understandable given the negative mood states more commonly associated with this form of play. The characteristics of problematic online EGM gamblers also suggest that interventions should target both genders and age groups from young to middle aged adults, and take into account the lower educational and income levels of this group. Interventions should also challenge beliefs that one can earn money from gambling, and discourage gambling on multiple gambling activities. Importantly, three-fifths of this cohort had gambling problems before gambling online. Therefore it should be recognized that for most, internet gambling provides a mechanism to sustain a developing dependence, rather than necessarily representing a ‘gateway’ into problematic use. Nevertheless, current Australian regulations outlawing the provision of online EGMs to Australian residents appear prudent, although they remain easily accessible via offshore sites. Given that problem online EGM gamblers also tend to gamble in venues, interventions should also discourage heavy gambling on land-based forms.
Risk factors identified for problematic online sports betting were very similar to those for problematic online race betting. Compared to their non-problematic counterparts, the problematic online sports and race bettors were more likely to be male, younger, and to speak a language other than English at home. This younger male profile of online bettors with gambling problems has also been identified elsewhere (
Compared to the non-problematic online bettors, online bettors (both sports and race) with gambling problems were less likely to speak English at home. Problematic online sports bettors were less likely to have been born in Australia, while the opposite was true for problematic online race bettors. These findings imply that the problematic online sports bettors were more likely to be first generation migrants, and their race betting counterparts to be second generation migrants, from non-English speaking countries. This difference may partially reflect the older age of problematic online race bettors than sports bettors in this study. Regardless, these findings are consistent with ethnic minority status being a common risk factor for gambling problems (
Behavioral risk factors for online bettors in our sample included more frequent online betting, as also found by
The problematic online bettors also had a greater tendency than their non-problematic counterparts to consider themselves to be semi-professional gamblers, and for race bettors, professional gamblers. Previous research has found high rates of problem gambling amongst self-nominated professional gamblers, raising queries over whether self-identifying as a professional gambler is a common but misguided way to rationalize problem gambling (
The preceding results for problematic online bettors imply that interventions need to particularly target young adult males, discourage frequent betting, in-play betting and illicit drug use while betting, and challenge beliefs that one can easily earn money from betting. Public health messages should be available in a range of community languages, given the ethnic diversity of this cohort. Professional treatment that caters for online sports bettors should be encouraged, given their relatively high PGSI scores and psychological distress. These interventions also need to take into consideration the relatively high educational qualifications and income of this group, and also their typical engagement with multiple gambling activities.
Several limitations of this study must be acknowledged. The non-random sample attained means that results may not generalize to the broader population of problematic online gamblers on each of the three forms examined; and the sample size for EGM gamblers was particularly small. The identification of most problematic gambling mode and most problematic gambling form relied on self-report, and the analyses were unable to take into account multiple modes and forms that might be causing gambling problems for some respondents. Further, the study was cross-sectional. While this was adequate to identify risk factors, causal directions between gambling problems and each risk factor could not be ascertained. Finally, some social desirability bias may be present given the survey relied on self-report about a sensitive and stigmatized issue (
This study has identified a range of risk factors for problem and moderate risk gambling on online EGMs, online sports betting and online race betting. Prior studies have generally made comparisons between those who gamble online and those who do not. Because online betters are also likely to gamble more heavily on land-based forms, this has prevented strong inference regarding the likely instrumental role of online betting in contributing to problematic play. That is, to some degree online play may
The detailed pattern of risks tends to vary with regard to different online gambling forms, particularly for EGM gambling when compared to sports betting and race betting. These differences point to the importance of developing and implementing interventions specifically for each online gambling form that are tailored to the characteristics and behaviors of those most at-risk of gambling problems on each of these activities. The findings suggest that interventions for online EGMs could include: general messages on EGM websites and in social marketing that warn of the risks of gambling while under the influence of substances, that challenge beliefs that one can earn money from gambling, and that discourage gambling on multiple gambling activities; dynamic messaging on EGM websites triggered by high frequency of EGM play; and the availability and promotion of limit setting functions on EGM websites that enable gamblers to better restrict their EGM play. These communications need to be tailored to those most at-risk, and to therefore target both genders, age groups from young to middle aged adults, and those from lower educational and income levels. For sports and race bettors, the findings suggest that interventions such as social marketing and warning messages on betting websites need to particularly target young adult males and be available in a range of community languages. These communications should discourage frequent betting, in-play betting and illicit drug use while betting, and challenge beliefs that one can easily earn money from betting. Frequent betting should also trigger dynamic warning messages, while limit setting functions need to be available and prominently promoted on betting websites. Professional treatment catering for online sports bettors should be available, given their relatively high PGSI scores and psychological distress.
NH helped to conceive and design the work, helped to organize the acquisition of data, interpreted the data, drafted the manuscript, approved the final version to be published, and agrees to be accountable for all aspects of the work. AR helped to conceive and design the work, helped to organize the acquisition of data, analyzed the data, critically revised the manuscript for important intellectual content, approved the final version to be published, and agrees to be accountable for all aspects of the work. MB helped with data analysis and interpretation, critically revised the manuscript for important intellectual content, approved the final version to be published, and agrees to be accountable for all aspects of the work.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.