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

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 (Mage = 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.

Contingency management for smoking cessation among individuals with substance use disorders: In-treatment and post-treatment effects.

INTRODUCTION: Smokers with substance use disorders (SUDs) show elevated tobacco prevalence, and smoking abstinence rates are considerably low. This randomized controlled trial sought to compare the effect of a cognitive behavioral treatment (CBT) that includes an episodic future thinking (EFT) component with the same treatment protocol plus contingency management (CM). This study aims to examine the effect of CM on smoking outcomes and in-treatment behaviors (i.e., retention, session attendance and adherence to nicotine use reduction guidelines), and to analyze whether these in-treatment variables predicted days of continuous abstinence at end-of-treatment. METHOD: A total of 54 treatment-seeking participants (75.9% males, M = 46.19 years old) were allocated to CBT + EFT (n = 30) or CBT + EFT + CM (n = 24). Intervention consisted of eight weeks of group-based sessions. Tobacco abstinence was verified biochemically by testing levels of carbon monoxide (≤4ppm) and urine cotinine (≤80 ng/ml). RESULTS: CM intervention increased 24-hour tobacco abstinence (50% vs. 20%, χ2(1) = 5.4; p = .021) and days of continuous abstinence (M = 5.92 ± 7.67 vs. 5.53 ± 12.42; t(52) = -0.132; p = 0.89) at end-of-treatment in comparison with CBT + EFT intervention. Although not statistically significant, CBT + EFT + CM enhanced in-treatment behaviors, in terms of retention (83.3% vs. 70%; χ2(1) = 0.255; p = .208), sessions attended (12.29 ± 3.22 vs. 10.93 ± 3.26; t(52) = -1.527; p = .133) and adherence to weekly nicotine use reduction targets (41.07% ± 31.96 vs. 35% ±2 6.28; t(52) = -0.766; p = .447). A higher percentage of samples meeting reduction guidelines (ß = 0.609; p<.001) predicted days of continuous abstinence at end-of-treatment. CONCLUSION: Combining CM with CBT + EFT improves short-term quitting rates. Findings suggest the need to incorporate strategies for improving adherence to nicotine reduction guidelines.