Latent class analysis for quality of life status, sleep quality and anxiety in patients with type 2 diabetes.

Abstract

Introduction: Type 2 diabetes (T2D) is a chronic metabolic disorder that is associated with reduced sleep quality and anxiety, and can cause a decrease in quality of life in these patients. Despite previous studies investigating these factors, few studies have examined their co-occurrence in these patients. To address this research gap, the present study aimed to determine the subgroups of patients with type 2 diabetes based on quality of life, sleep quality, and anxiety in the subgroups of using latent class analysis (LCA).

Methods: This cross-sectional study was conducted using multistage random sampling. A total of 308 patients with type 2 diabetes were randomly selected from health centers in Ardabil. All participants completed four sets of checklists and questionnaires, (Demographic characteristics, 12-item Short Form survey, the Pittsburgh Sleep Quality Index and Generalized Anxiety Disorder 7-item). Data analysis was performed using Analysis of Variance (ANOVA), chi square and latent class analysis.

Results: Three latent classes were identified: The first class (good status) included 56.4% of the participants. Also, the second (moderate status) and third (poor status) classes described 16.5% and 27.1% of the participants, respectively. In latent class 1, the probability of having good quality of life and good sleep quality was higher. In latent class 2, the probability of having moderate quality of life and poor sleep quality was higher. However, these patients revealed no anxiety. Those with third latent class membership were more likely to have moderate quality of life, poor sleep quality, and severe anxiety.

Conclusion: This study showed that sleep quality and anxiety is positively related to quality of life in patients with type 2 diabetes. In addition, this study indicated the co-occurrence of sleep quality and anxiety in these patients. Based on these findings, effective and targeted interventions can be designed to improve the health status and quality of life of these patients, taking into account sleep quality and anxiety.