Cluster analysis based on gambling variables and mental health in a clinical population of gamblers

Abstract

Background

Interest in characterizing individuals involved in addictive behaviors has been growing, which allows tailoring prevention and intervention strategies to the gambler’s needs. The study aimed to 1) identify clusters of gamblers according to gambling-related characteristics and mental health; and 2) analyze differences in psychological variables between the clusters. Methods. A total of 83 participants undergoing treatment for gambling disorder (M_age = 45.52, 51.8 % female) completed a set of questionnaires. Hierarchical cluster analysis was performed to classify gambling based on gambling variables (i.e., gambling severity and gambling motives) and mental health (i.e., depression, anxiety, and hostility). Several ANOVAs were conducted to illustrate the distinguishing features of each cluster, encompassing both the variables included in the cluster analysis and other relevant psychological variables. Results. Findings suggest that gamblers can be classified into three clusters based on these variables: 1) “high gambling severity and good mental health,” 2) “high gambling severity and poor mental health,” and 3) “low gambling severity and good mental health.” These clusters were differentiated as a function of psychological variables, such as emotional dependence, alexithymia, and stressful life events. Conclusions. Classifying gamblers according to their profile provides a better understanding of their needs and problems, allowing for a more tailored approach in terms of prevention and intervention strategies.