Background Individuals who gamble online may be at risk of gambling excessively, but internet gambling also provides a unique opportunity to monitor gambling behavior in real environments which may allow intervention for those who encounter difficulties. Objective The objective of this study was to model the early gambling trajectories of individuals who play online lottery. Methods Anonymized gambling‐related records of the initial 6 months of 1152 clients of the French national lottery who created their internet gambling accounts between September 2015 and February 2016 were analyzed using a two-step approach that combined growth mixture modeling and latent class analysis. The analysis was based upon behavior indicators of gambling activity (money wagered and number of gambling days) and indicators of gambling problems (breadth of involvement and chasing). Profiles were described based upon the probabilities of following the trajectories that were identified for the four indicators, and upon several covariates (age, gender, deposits, type of play, net losses, voluntary self-exclusion, and Playscan classification—a responsible gambling tool that provides each player with a risk assessment: green for low risk, orange for medium risk and red for high risk). Net losses, voluntary self-exclusion, and Playscan classification were used as external verification of problem gambling. Results We identified 5 distinct profiles of online lottery gambling. Classes 1 (56.8%), 2 (14.8%) and 3 (13.9%) were characterized by low to medium gambling activity and low values for markers of problem gambling. They displayed low net losses, did not use the voluntary self-exclusion measure, and were classified predominantly with green Playscan tags (range 90%-98%). Class 4 (9.7%) was characterized by medium to high gambling activity, played a higher breadth of game types (range 1-6), and had zero to few chasing episodes. They had high net losses but were classified with green (66%) or orange (25%) Playscan tags and did not use the voluntary self-exclusion measure. Class 5 (4.8%) was characterized by medium to very high gambling activity, played a higher breadth of game types (range 1-17), and had a high number of chasing episodes (range 0-5). They experienced the highest net losses, the highest proportion of orange (32%) and red (39%) tags within the Playscan classification system and represented the only class in which voluntary self-exclusion was present. Conclusions Classes 1, 2, 3 may be considered to represent recreational gambling. Class 4 had higher gambling activity and higher breadth of involvement and may be representative of players at risk for future gambling problems. Class 5 stood out in terms of much higher gambling activity and breadth of involvement, and the presence of chasing behavior. Individuals in classes 4 and 5 may benefit from early preventive measures.
Wagering inducements are part of loyalty/reward programs implemented by online gambling operators to retain or attract consumers. They constitute incentives to bet that are offered to gamblers provided that they perform certain betting-related activities. They are often considered risk factors for gambling problems, but studies exploring the actual impacts of such incentives are scarce. The objective of the present study was to assess the actual impact of wagering inducements on gambling behaviors, cognitions, and emotions of online gamblers. One hundred seventy-one adults (18–65 years old) who gamble on a regular basis on the Internet, including at-risk and recreational gamblers, were recruited through media announcements and in panels from survey institutes. Participants were randomly assigned to one of four experimental conditions, in which a defined amount of money was given to the gambler with a bank e-card system during an experimental gambling session to simulate a wagering inducement (€10, €50, €100, or €200), or the control condition, in which no incentive was given. The experimental gambling session was designed to be as natural as possible (participants gambled with their own gambling account and their own money). Participants completed a pretest interview, took part in the experimental gambling session, and then completed a post-test interview. The impact of wagering inducements was estimated on objective (money wagered and time spent gambling during the gambling session) and subjective (cognitive distortions, enjoyment of gambling, loss of control, and respect of usual gambling habits) gambling endpoints that were compared between conditions. Two-thirds of participants reported having already received wagering inducements at some point of their gambling course. Although no effect was demonstrated on time spent gambling, inducements increased money wagered, gambling-related expectancies and perceived loss of control. In particular, it seems that wagering inducements could lead to extreme expenses, especially for at-risk gamblers. This research suggests that regulating wagering inducements could be helpful for prevention and early intervention. Future research on the impacts of wagering inducements is still needed, especially more ecological studies based on behavioral tracking data and studies assessing the differential impacts of various incentive types. Clinical Trial Registration: NCT01789580 ( ClinicalTrials.gov ).
Aims To estimate whether the use of wagering inducements has a significant impact on the gambling behaviors of on‐line gamblers and describe this temporal relation under naturalistic conditions. Design This longitudinal observational study is part of the second stage of the Screening for Excessive Gambling Behaviors on the Internet (EDEIN) research program. Setting Gambling tracking data from the French national on‐line gambling authority (poker, horse race betting and sports betting) and from the French national lottery operator (lotteries and scratch games). Participants A total of 9306 gamblers who played poker, horse race or sports betting and 5682 gamblers who played lotteries and scratch games completed an on‐line survey. The gender ratio was largely male (between 87.1% and 92.9% for poker, horse race betting and sports betting, and equal to 65.1% for lotteries). Median age ranged from 35 (sports betting) to 53 (horse race betting and lotteries). Measurements The survey used the Problem Gambling Severity Index (PGSI) to determine the status of the gamblers (at‐risk or not). Gambling tracking data included weekly gambling intensity (wagers, deposits), gambling frequency (number of gambling days), proxies of at‐risk gambling behaviors (chasing and breadth of involvement) and use of wagering inducements. Findings The use of wagering inducements was associated with an increase of gambling intensity [β between −0.06 (−0.08; –0.05) and 0.57 (0.54; 0.60)], gambling frequency [β between 0.12 (0.10; 0.18) and 0.29 (0.28; 0.31)] and at‐risk gambling behaviors [odds ratio between 1.32 (1.16; 1.50) and 4.82 (4.61; 5.05)] at the same week of their use. This effect was stronger for at‐risk gambling behaviors and at‐risk gamblers. Conclusions Wagering inducements may represent a risk factor for developing or exacerbating gambling problems.
Background and aims Gambling disorder is characterized by problematic gambling behavior that causes significant problems and distress. This study aimed to develop and validate a predictive model for screening online problem gamblers based on players' account data. Methods Two random samples of French online gamblers in skill-based (poker, horse race betting and sports betting, n = 8,172) and pure chance games (scratch games and lotteries, n = 5,404) answered an online survey and gambling tracking data were retrospectively collected for the participants. The survey included age and gender, gambling habits, and the Problem Gambling Severity Index (PGSI). We used machine learning algorithms to predict the PGSI categories with gambling tracking data. We internally validated the prediction models in a leave-out sample. Results When predicting gambling problems binary based on each PGSI threshold (1 for low-risk gambling, 5 for moderate-risk gambling and 8 for problem gambling), the predictive performances were good for the model for skill-based games (AUROCs from 0.72 to 0.82), but moderate for the model for pure chance games (AUROCs from 0.63 to 0.76, with wide confidence intervals) due to the lower frequency of problem gambling in this sample. When predicting the four PGSI categories altogether, performances were good for identifying extreme categories (non-problem and problem gamblers) but poorer for intermediate categories (low-risk and moderate-risk gamblers), whatever the type of game. Conclusions We developed an algorithm for screening online problem gamblers, excluding online casino gamblers, that could enable the setting of prevention measures for the most vulnerable gamblers.
Background: In recent years, many studies have explored the associations among impulsivity, history of abuse, the emergence of eating disorders with episodes of binge eating (EDBE) and their severity. Nevertheless, factors associated with successful clinical outcomes of EDBE are still unknown. Our study aimed to test the hypothesis that a history of abuse is associated with unsuccessful clinical outcomes of EDBE through an effect mediated by impulsivity. Methods: We assessed patients older than 15 years, 3 months with EDBE at inclusion and at 1 year. Recovery was defined as the absence of eating disorders at 1 year. A mediation analysis was performed by means of structural equation modelling. Results: We included 186 patients in our analyses (54% bulimia nervosa, 29% anorexia nervosa binge eating/purging type and 17% binge-eating disorder); 179 (96%) were female. One-third (n = 63) of patients reported a history of abuse, and recovery was observed for 20% of the sample (n = 38). Contrary to our assumption, a history of abuse was not associated with the absence of recovery of EDBE at 1 year. Factors unfavourable for achieving recovery were anxiety disorders (odds ratio [OR] 0.41), vomiting (OR 0.39), physical hyperactivity (OR 0.29), negative urgency and a lack of perseverance (OR 0.85 for both). Only positive urgency was positively associated with recovery (OR 1.25). Limitations: We excluded 219 patients lost to the 1-year follow-up. Conclusion: Our findings may help to deconstruct the empirical belief that traumatic events may interfere with the successful course of treatment for eating disorders. A high level of positive urgency may be associated with more receptivity to care.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.