Background and aimsThe effect of internet-based psychological treatment for gambling problems has not been previously investigated by meta-analysis. The present study is therefore a quantitative synthesis of studies on the effects of internet-based treatment for gambling problems. Given that effects may vary according to the presence of therapist support and control conditions, it was presumed that subgroup analyses would elucidate such effects.MethodsA systematic search with no time constraints was conducted in PsycINFO, MEDLINE, Web of Science, and the Cochrane Library. Two authors independently extracted data using a predefined form, including study quality assessment based on the Cochrane risk of bias tool. Effect sizes were calculated using random-effects models. Heterogeneity was indexed by Cochran’s Q and the I2 statistics. Publication bias was investigated using trim and fill.ResultsThirteen studies were included in the analysis. Random effects models at post-treatment showed significant effects for general gambling symptoms (g = 0.73; 95% CI = 0.43–1.03), gambling frequency (g = 0.29; 95% CI = 0.14–0.45), and amount of money lost gambling (g = 0.19; 95% CI = 0.11–0.27). The corresponding findings at follow-up were g = 1.20 (95% CI = 0.79–1.61), g = 0.36 (95% CI = 0.12–0.60), and g = 0.20 (95% CI = 0.12–0.29) respectively. Subgroup analyses showed that for general gambling symptoms, studies with therapist support yield larger effects than studies without, both post-treatment and at follow-up. Additionally, on general gambling symptoms and gambling frequency, there were lower effect sizes for studies with a control group compared to studies without a control group at follow-up. Studies with higher baseline severity of gambling problems were associated with larger effect sizes at both posttreatment and follow-up than studies with more lenient inclusion criteria concerning gambling problems.Discussion and conclusionsInternet-based treatment has the potential to reach a large proportion of persons with gambling problems. Results of the meta-analysis suggest that such treatments hold promise as an effective approach. Future studies are encouraged to examine moderators of treatment outcomes, validate treatment effects cross-culturally, and investigate the effects of novel developments such as ecological momentary interventions.

Pop-up messages utilized by gambling operators are normally presented to gamblers during gambling sessions in order to prevent excessive gambling and/or to help in the appraisal of maladaptive gambling cognitions. However, the effect of such messages on gambling behavior and gambling cognitions has not previously been synthesized quantitatively. Consequently, a meta-analysis estimating the efficacy of pop-up messages on gambling behavior and cognitions was conducted. A systematic literature search with no time constraints was performed on Web of Science, PsychInfo, Medline, PsychNET, and the Cochrane Library. Search terms included “gambling,” “pop-up,” “reminder,” “warning message,” and “dynamic message.” Studies based on randomized controlled trials, quasi-experimental designs and pre-post studies reporting both pre- and post-pop-up data were included. Two authors independently extracted data using pre-defined fields including quality assessment. A total of 18 studies were included and data were synthesized using a random effects model estimating Hedges' g. The effects of pop-ups were g = 0.413 for cognitive measures (95% CI = 0.115–0.707) and g = 0.505 for behavioral measures (95% CI = 0.256–0.746). For both outcomes there was significant between-study heterogeneity which could not be explained by setting (laboratory vs. naturalistic) or sample (gambler vs. non-gamblers). It is concluded that pop-up messages provide moderate effects on gambling behavior and cognitions in the short-term and that such messages play an important role in the gambling operators' portfolio of responsible gambling tools.

Losses disguised as wins (LDWs) appear to reinforce gambling persistence. However, little research has examined this phenomenon with real gamblers in natural gambling settings. We aimed to examine the relationship between within-session outcome size and subsequent gambling persistence. Account-based gambling data of individuals playing LDW games over a randomly selected day (2,035,339 bets made by 8636 individuals) was examined. We used a logistic mixed effects model to examine the relationship between the outcome of the previous bet (loss, LDW and real wins) and the odds of continuing betting in a game session. The odds of continuing betting in a game session were positively associated with the outcome of the previous bet. Compared to LDWs, losses lowered the odds of continuing a game session. In contrast, real wins implied greater odds of continuing a game session compared to LDWs. It is concluded that LDWs increase the likelihood of continuing betting compared to losses, but decrease the likelihood of continuing to gamble compared to real wins. As LDWs increase the number of bets made within a gambling session, and hence within-session gambling persistence, LDWs may potentially play an etiological role in the development of gambling problems over time.

The aim of this study was to examine the relationship between the structural characteristics and gambling behavior among video lottery terminal (VLT) gamblers. The study was ecological valid, because the data consisted of actual gambling behavior registered in the participants natural gambling environment without intrusion by researchers. Online behavioral tracking data from Multix, an eight game video lottery terminal, were supplied by Norsk-Tipping (the state owned gambling company in Norway). The sample comprised the entire population of Multix gamblers (N = 31,109) who had gambled in January 2010. The individual number of bets made across games was defined as the dependent variable, reward characteristics of a game (i.e., payback percentage, hit frequency, size of winnings and size of jackpot) and bet characteristics of a game (i.e., range of betting options and availability of advanced betting options) served as the independent variables. Control variables were age and gender. Two separate cross-classified multilevel random intercepts models were used to analyze the relationship between bets made, reward characteristics and bet characteristics, where the number of bets was nested within both individuals and within games. The results show that the number of bets is positively associated with payback percentage, hit frequency, being female and age, and negatively associated with size of wins and range of available betting options. In summary, the results show that the reward characteristics and betting options explained 27 % and 15 % of the variance in the number of bets made, respectively. It is concluded that structural game characteristics affect gambling behavior. Implications of responsible gambling are discussed.

There is a paucity of longitudinal investigations of gambling behavior in the transition from adolescence to emerging adulthood. We conducted a longitudinal investigation of the associations and patterns of change between mental health symptoms and gambling behavior. A representative sample of Norwegians completed questionnaires containing demographic, mental health, and gambling measures at age 17 (N = 2055), and at ages 18 (N = 1334) and 19 (N = 1277). Using latent class analysis, three classes of gambling behavior were identified: consistent non-gambling (71.1%), consistent non-risk gambling (23.8%), and risky-and-problem gambling (5.1%). Being male, showing higher physical and verbal aggression and having more symptoms of depression were associated with greater odds of belonging to the risky-and-problem gambling class at age 17. Overall, the risky-and-problem gambling class had the highest physical and verbal aggression, anxiety, and depression at 19 years. Our findings elucidate the reciprocal relationship between mental health and gambling behavior in the transition from adolescence to emerging adulthood, and the importance of recognizing these factors in designing targeted interventions.