Addictology and Psychiatry Department, Centre Hospitalier Universitaire de Nantes, Nantes, France.
SPHERE, INSERM UMR1246, University of Nantes, University of Tours, Nantes, France.
J Med Internet Res. 2020 Aug 12;22(8):e17675. doi: 10.2196/17675.
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.
The objective of this study was to model the early gambling trajectories of individuals who play online lottery.
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.
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.
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.
在网上赌博的人可能存在过度赌博的风险，但网络赌博也提供了一个独特的机会，可以在真实环境中监测赌博行为，从而为遇到困难的人提供干预措施。
本研究的目的是对参与在线彩票赌博的个体的早期赌博轨迹进行建模。
对 2015 年 9 月至 2016 年 2 月期间创建互联网赌博账户的 1152 名法国国家彩票客户的最初 6 个月的匿名赌博相关记录进行了分析，采用两步法，结合增长混合模型和潜在类别分析。分析基于赌博活动的行为指标（下注金额和赌博天数）和赌博问题的指标（参与广度和追号）。根据为四个指标确定的轨迹概率以及几个协变量（年龄、性别、存款、玩法类型、净损失、自愿自我排除和 Playscan 分类——一种提供每个玩家风险评估的负责任的赌博工具：绿色表示低风险，橙色表示中风险，红色表示高风险）来描述特征。净损失、自愿自我排除和 Playscan 分类被用作赌博问题的外部验证。
我们确定了 5 种不同的在线彩票赌博特征。第 1 类（56.8%）、第 2 类（14.8%）和第 3 类（13.9%）的特点是赌博活动低到中度，以及赌博问题的指标值较低。他们的净损失较低，不使用自愿自我排除措施，并且主要被归类为绿色 Playscan 标签（范围 90%-98%）。第 4 类（9.7%）的特点是中到高赌博活动，玩的游戏类型范围更广（1-6 种），并且追号次数很少或没有。他们的净损失很高，但被归类为绿色（66%）或橙色（25%）Playscan 标签，并且不使用自愿自我排除措施。第 5 类（4.8%）的特点是中到非常高的赌博活动，玩的游戏类型范围更广（1-17 种），并且有很高的追号次数（0-5 次）。他们的净损失最高，在 Playscan 分类系统中，橙色（32%）和红色（39%）标签的比例最高，并且是唯一使用自愿自我排除措施的类别。
第 1 类、第 2 类和第 3 类可能被认为代表了娱乐性赌博。第 4 类具有更高的赌博活动和更高的参与广度，可能代表了未来有赌博问题风险的参与者。第 5 类在更高的赌博活动和参与广度以及追逐行为方面表现突出。第 4 类和第 5 类的个体可能受益于早期预防措施。
