Classifying self‐management clusters of patients with mild cognitive impairment associated with diabetes: A cross‐sectional study

Abstract

AbstractAims and ObjectivesThis study aims to propose a self‐management clusters classification method to determine the self‐management ability of elderly patients with mild cognitive impairment (MCI) associated with diabetes mellitus (DM).BackgroundMCI associated with DM is a common chronic disease in old adults. Self‐management affects the disease progression of patients to a large extent. However, the comorbidity and patients' self‐management ability are heterogeneous.DesignA cross‐sectional study based on cluster analysis is designed in this paper.MethodThe study included 235 participants. The diabetes self‐management scale is used to evaluate the self‐management ability of patients. SPSS 21.0 was used to analyse the data, including descriptive statistics, agglomerative hierarchical clustering with Ward's method before k‐means clustering, k‐means clustering analysis, analysis of variance and chi‐square test.ResultsThree clusters of self‐management styles were classified as follows: Disease neglect type, life oriented type and medical dependence type. Among all participants, the percentages of the three clusters above are 9.78%, 32.77% and 57.45%, respectively. The difference between the six dimensions of each cluster is statistically significant.Conclusion(s)This study classified three groups of self‐management styles, and each group has its own self‐management characteristics. The characteristics of the three clusters may help to provide personalized self‐management strategies and delay the disease progression of MCI associated with DM patients.Relevance to clinical practiceTypological methods can be used to discover the characteristics of patient clusters and provide personalized care to improve the efficiency of patient self‐management to delay the progress of the disease.Patient or public contributionIn our study, we invited patients and members of the public to participate in the research survey and conducted data collection.