Modeling Early Gambling Behavior Using Indicators from Online Lottery Gambling Tracking Data: Longitudinal Analysis

Abstract

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.

Impact of wagering inducements on the gambling behaviors of on‐line gamblers: A longitudinal study based on gambling tracking data

“…Lag+1: When a gambler (whatever the PGSI status) uses a wagering inducement during a specific week, the amount of the deposits during the following week increase by chasing was previously identified as a critical indicator of gambling problems [10,23,41] and the most significant step in the development of gambling disorders [42][43][44]. Moreover, the breadth of involvement (i.e.…”

“…Regarding the distribution of gambling tracking data, they fluctuate considerably over time for a given individual and zero values are largely over‐represented in the data (i.e. frequently, gamblers do not gamble at all during a given period) [ 23 ]. Therefore, it is highly important to take into account zero‐inflated distributions of gambling indicators.…”

“…Moreover, we hypothesized that the use of wagering inducements lead to an increase in gambling frequency, gambling intensity and the occurrence of at‐risk gambling behaviors, i.e. episodes of chasing and higher involvement [ 23 , 24 ]. We thus aimed to estimate whether the use of wagering inducements impacts gambling behavior and quantify this impact.…”

The Effect of a Mandatory Play Break on Subsequent Gambling Behavior among British Online Casino Players: A Large-Scale Real-World Study

“…The present study investigated 1 3 whether players with high losses were more likely to deposit and wager after the mandatory play break was over. Previous studies (i.e., Challet-Bouju et al, 2020;Perrot et al, 2018) have operationalized chasing losses using the metric of frequent depositing (which was also used in the present study). Chasing losses has been described as an important risk factor for problem gambling in several studies (e.g., Campbell-Meiklejohn et al, 2008;Lesieur, 1979).…”

A Change Talk Model for Abstinence Based on Web-Based Anonymous Gambler Chat Meeting Data by Using an Automatic Change Talk Classifier: Development Study

“…Thus, the abstinence process in web-based anonymous self-help group meetings can be clarified through machine-learned classification of utterances. However, the web-based abstinence process is unclear [ 5 ] despite its usefulness in terms of ease-of-use [ 9 ] and few damages caused by prejudice [ 10 ]. To clarify the abstinence process, a change talk model for abstinence was developed in this study based on the utterances of anonymous web-based gamblers through the use of a machine-learned classifier.…”