Key points are not available for this paper at this time.
BACKGROUND: The goal of this study is to identify betting patterns displayed during the first month of actual Internet gambling on a betting site that can serve as behavioural markers to predict the development of gambling-related problems. METHODS: Using longitudinal data, k-means clustering analysis identified a small subgroup of high-risk gamblers. RESULTS: Seventy-three percent of the members of this subgroup eventually closed their account due to gambling-related problems. The characteristics of this high-risk subgroup were as follows: (i) frequent and (ii) intensive betting combined with (iii) high variability across wager amount and (iv) an increasing wager size during the first month of betting. CONCLUSION: This analysis provides important information that can help to identify potentially problematic gamblers during the early stages of gambling-related problems. Public health workers can use these results to develop early interventions that target high-risk Internet gamblers for prevention efforts. However, one study limitation is that the results distinguish only a small proportion of the total sample; therefore, additional research will be necessary to identify markers that can classify larger segments of high-risk gamblers.
Braverman et al. (Wed,) studied this question.