Aggregation Partitioning and Study of Demographic, Medical, and Psychological Predictors Related to Derived Clusters in Cardiac Rehabilitation Patients: A Cross-Sectional Study

Abstract

Background: Classification of high-risk behaviors such as aggression and identifying social, medical, and psychological factors related to it can help the emergence and development of the strategies to prevent these destructive behaviors. Objectives: Thus,thepresentstudywasdonewithtwoobjectives: (i)clusteranalysisoftheaggressioncomponentsandpartitioning cardiac rehabilitation (CR) patients and (ii) determining the demographic, medical, and psychological correlations of each cluster. Methods: The sample of this cross-sectional study was 167 CR patients in western Iran examined from June to December 2017. De-mographicandriskfactorschecklist,Beckanxietyinventory(BAI),Beckdepressioninventory(BDI),andBuss-Perryaggressionques- tionnaire (BPAQ) were used for data collection. The data were analyzed using hierarchical and k-means cluster analysis, Cramer-V test, analysis of variance (ANOVA), and analysis of binary logistic regression. Results: The mean age of the participants (66.5% male) was 59.14 ± 9.03. The model proposed two clusters: (i) patients with mild aggression and (ii) patients with severe aggression. Occupation (P = 0.048), marital status (P = 0.048), anxiety (P = 0.006), and depression (P = 0.001) were the most essential predictors of the unhealthy cluster. Our model could explain 30.7% to 41% of the variance of the unhealthy cluster. Conclusions: Cluster analysis divided patients into two groups with mild and severe aggression. Marital status and occupation are the most important demographic correlates, and depression and anxiety are the most important psychological predictors of the cluster with high aggression. The results of the present study can provide a map of the focus of attention on harm reduction interventions by health professionals.