Characteristics of Diabetes Self-Care Agency in Japan Based on Statistical Cluster Analysis - Eiko Umeda MSN; Yasuko Shimizu PhD; Kyoko Uchiumi PhD; Naoko Murakado MSN; Kumiko Kuroda PhD; Harue Masaki PhD; Natsuko Seto PhD; Hidetoki Ishii PhD (SAGE Publishing, 2020)
Introduction Considering the situation where the number of people with diabetes is increasing, we need to find ways to support more efficient and effective outpatient clinics. Therefore, it is necessary to develop effective support methods and to elaborate a strategy as a system for support after grasping the characteristics of the entire population of people with diabetes. Objective The purpose of this study was to identify the characteristics of the diabetes population in outpatient settings by differences in self-care agency and to examine how to support them based on the recognized characteristics. Methods Participants were 261 people with diabetes under outpatient care in Japanese institutions from whom demographic data on age, gender, HbA1c, and treatment method were collected as well as self-care agency data based on the Instrument of Diabetes Self-Care Agency consisting of 40 items. The data were analyzed using cluster analysis to compare age, gender, HbA1c, duration of diabetes, type of diabetes, and insulin therapy between clusters. Results The analysis identified six clusters, including a group with favorable HbA1c but low total self-care agency scores that were likely to affect their blood glucose control in the future, although accounting for as small a portion as 3% of the total. In addition, a cluster with poor HbA1c and generally low self-care agency was also identified accounting for about a quarter of the total population. These clusters were considered to require further support. Clusters having markedly low self-care agency items, stress-coping ability, or the ability to make the most of the support available were also identified. Conclusion The six clusters need to be assisted in focusing on mental or social support. Accordingly, consideration of the support system for people with diabetes based on an understanding of the cluster characteristics seemed to enable more efficient and effective support.