Predicting online problem gambling treatment discontinuation: New evidence from cross-validated models

There are tens of millions of problem gamblers in the world, many of whom either do not seek treatment or fail to commit to it. Dropout rates are high, and not enough is known about factors predicting treatment adherence. We focus on an online cognitive behavioral therapy program for severe problem gambling to determine the likelihood of treatment discontinuation at three different treatment phases: pretreatment, before halfway, and before the end of the program.

Participants were Finnish adults (N = 1,139, 670 males, M_age = 34.5) registered in the program between 2019 and 2021. Using logistic regression and five-fold cross-validated naïve Bayes classification, we predicted discontinuation with demographic-, psychometric-, and other gambling-related variables, including the quality of one's social relations, time spent on the waiting list, and experienced readiness to behavioral change.

The models had acceptable predictive ability (area under the curve [AUC] values from .69 to .745; cross-validated balanced classification accuracy = 63.2%). In logistic regressions, treatment discontinuation was prominently associated with younger age (p = .008), lower education (p < .001), not being ready to change gambling behavior (p < .001), problem gambling severity (p < .0001), longer time spent on the treatment waiting list (p < .0001), and fewer close social relationships (p < .001).

We found significant new real-world evidence on factors statistically predicting treatment discontinuation, which is crucial when existing programs are modified to better serve those in need. (PsycInfo Database Record (c) 2023 APA, all rights reserved).