Purpose: The study aimed to design and develop an automated personalized self-care (APSC) program for patients with type 2 diabetes mellitus. The secondary aim was to present a clinical protocol as a mixed-method research to test the program effects. Methods: The APSC program was developed in the order of analysis, design, implementation, and evaluation according to the software development life cycle, and was guided by the self-regulatory theory. The content validity, heuristics, and usability of the program were verified by experts and patients with type 2 diabetes mellitus. Results: The APSC program was developed based on goal setting, education, monitoring, and feedback components corresponding to the phases of forethought, performance/volitional control, and self-reflection of self-regulatory theory. Using the mobile application, the participants are able to learn from educational materials, monitor their health behaviors, receive weekly-automated personalized goals and feedback messages, and use an automated conversation system to solve the problems related to self-care. The ongoing two-year study utilizes a mixed method design, with 180 patients having type 2 diabetes mellitus randomized to receive either the intervention or usual care. The participants will be reviewed for self-care self-efficacy, health behaviors, and health outcomes at 6, 12, 18, and 24 months. Participants in the intervention group will be interviewed about their experiences. Conclusion The APSC program can serve as an effective tool for facilitating diabetes health behaviors by improving patients’ self-care self-efficacy and self-regulation for self-care. However, the clinical effectiveness of this program requires further investigation.

Purpose: Preventing diabetic complications involves regular outpatient follow-up and maintaining low variability in hemoglobin A1c (HbA1c) levels. This study investigated the factors associated with diabetic complications, with a specific focus on the impact of regular outpatient follow-up and HbA1c variability, among patients with type 2 diabetes. Methods: The study design was secondary data analysis of electronic medical records from a university hospital in Korea. It included patients aged 40–79 with type 2 diabetes who were prescribed diabetes medication within three months of their first HbA1c test by an endocrinologist and were followed up for at least five years. Follow-up regularity, adjusted standard deviation of HbA1c levels, and diabetic complication indices were collected. Data were analyzed using the Chi-square test, independent t-test, repeated measures analysis of variance, and multiple regression analysis. Results: The study included 1566 patients. Lower follow-up regularity was observed in patients of older age, with comorbidities, diabetic complications, insulin treatment, a history of hospitalization, lower baseline estimated glomerular filtration rate (eGFR) and total cholesterol (TC), and higher HbA1c variability. Higher HbA1c variability was observed in younger patients without comorbidity but with insulin treatment, a history of hospitalization, higher baseline blood glucose (BG), HbA1c, TC, and triglyceride levels. HbA1c variability had the strongest influence on BG and HbA1c levels at the five-year follow-up. Baseline eGFR and TC were the most influential factors for their respective levels at the five-year follow-up. Follow-up regularity significantly affected BG, HbA1c, eGFR, and TC at five-year follow-up. Conclusions It has been shown that several variables besides regular follow-up and HbA1c variability have an influence. However, these are the two that can be corrected through nursing intervention and are important, so intervention on these is important.

No abstract available.
Body Mass Index*

Purpose: This study aimed to determine the consistency of 1-day and 3-day average dietary intake using the 24-hour diet recall method and to investigate the relationship of diet intake with physiological indicators potentially associated with diabetic complications in patients with diabetes. Methods: This study conducted a secondary data analysis using pretest data of a nursing intervention study entitled “Development of deep learning based AI coaching program for diabetic patients with high risk and examination of its effects (PNUH, IRB No. 05-2021-030).” Data were analyzed through descriptive analysis, one-way repeated-measures analysis of variance, and Pearson correlation coefficients using SPSS 26.0. Results: The average total daily calorie intake over 3 days was 1,494.48±436.47 kcal/day: 1,510.90±547.76 kcal/day on the first day, 1,414.22±527.58 kcal/day on the second day, 1,558.34±645.83 kcal/day on the third day, showing significant differences (F=3.59, p=.031). The correlation coefficient between the 1-day and 3-day average dietary intake was 0.41–0.77 for each nutrient and 0.62–0.80 for each food group. Vegetable intake showed negative correlations with body mass index (BMI; r=-.19, p=.023) and triglycerides (r=-.18, p=.036), whereas dairy intake was positively associated with low-density lipoprotein-cholesterol (LDL; r=-0.18, p=.034) and triglycerides (r=.40, p<.001). Conclusion This study demonstrated that 1-day dietary intake was highly correlated with 3-day average dietary intake using the 24-hour diet recall method. Food groups showed significant associations with physiological indicators of potential diabetic complications such as BMI, triglycerides, and LDL levels. Further studies are needed to improve the knowledge base on the relationships between physiological indicators and food groups.

This study aimed to explore the participation experiences of patients with type 2 diabetes in an Automated Personalized Self-Care program, assess the changes in self-care behavior and glycemic control, and evaluate the stages of change and readiness to change using the transtheoretical model (TTM). Methods: We examined 16 patients with type 2 diabetes who participated in a diabetes self-care program using a mobile application. Purposive sampling continued until data saturation. Using a mixed method study, we analyzed the participants’ characteristics, self-care behavior, stage of change, and readiness to change quantitatively and analyzed the qualitative data using Elo and Kyngas’s content analysis method. Results: The compliance group (CG) showed improved self-care behavior and glycemic control. In the CG, the proportion of participants in the action stage was higher in the exercise and diet domains and lower in the blood glucose testing and medication domains than in the non-compliance group (NCG). Readiness to change, motivation for health behaviors, and social motivation were higher in the CG, whereas personal motivation was higher in the NCG. In this qualitative study, three categories and 11 subcategories were identified. The findings suggest the CG regarded their experience in the program more frequently as positive, whereas the NCG perceived greater barriers to using the mobile application in the program. Conclusion: Based on the differences identified between the CG and NCG, TTM-based strategies are needed to facilitate the progression of NCG to the action stage.

This study aimed to explore the participation experiences of patients with type 2 diabetes in an Automated Personalized Self-Care program, assess the changes in self-care behavior and glycemic control, and evaluate the stages of change and readiness to change using the transtheoretical model (TTM). Methods: We examined 16 patients with type 2 diabetes who participated in a diabetes self-care program using a mobile application. Purposive sampling continued until data saturation. Using a mixed method study, we analyzed the participants’ characteristics, self-care behavior, stage of change, and readiness to change quantitatively and analyzed the qualitative data using Elo and Kyngas’s content analysis method. Results: The compliance group (CG) showed improved self-care behavior and glycemic control. In the CG, the proportion of participants in the action stage was higher in the exercise and diet domains and lower in the blood glucose testing and medication domains than in the non-compliance group (NCG). Readiness to change, motivation for health behaviors, and social motivation were higher in the CG, whereas personal motivation was higher in the NCG. In this qualitative study, three categories and 11 subcategories were identified. The findings suggest the CG regarded their experience in the program more frequently as positive, whereas the NCG perceived greater barriers to using the mobile application in the program. Conclusion: Based on the differences identified between the CG and NCG, TTM-based strategies are needed to facilitate the progression of NCG to the action stage.

This study aimed to explore the participation experiences of patients with type 2 diabetes in an Automated Personalized Self-Care program, assess the changes in self-care behavior and glycemic control, and evaluate the stages of change and readiness to change using the transtheoretical model (TTM). Methods: We examined 16 patients with type 2 diabetes who participated in a diabetes self-care program using a mobile application. Purposive sampling continued until data saturation. Using a mixed method study, we analyzed the participants’ characteristics, self-care behavior, stage of change, and readiness to change quantitatively and analyzed the qualitative data using Elo and Kyngas’s content analysis method. Results: The compliance group (CG) showed improved self-care behavior and glycemic control. In the CG, the proportion of participants in the action stage was higher in the exercise and diet domains and lower in the blood glucose testing and medication domains than in the non-compliance group (NCG). Readiness to change, motivation for health behaviors, and social motivation were higher in the CG, whereas personal motivation was higher in the NCG. In this qualitative study, three categories and 11 subcategories were identified. The findings suggest the CG regarded their experience in the program more frequently as positive, whereas the NCG perceived greater barriers to using the mobile application in the program. Conclusion: Based on the differences identified between the CG and NCG, TTM-based strategies are needed to facilitate the progression of NCG to the action stage.

This study aimed to explore the participation experiences of patients with type 2 diabetes in an Automated Personalized Self-Care program, assess the changes in self-care behavior and glycemic control, and evaluate the stages of change and readiness to change using the transtheoretical model (TTM). Methods: We examined 16 patients with type 2 diabetes who participated in a diabetes self-care program using a mobile application. Purposive sampling continued until data saturation. Using a mixed method study, we analyzed the participants’ characteristics, self-care behavior, stage of change, and readiness to change quantitatively and analyzed the qualitative data using Elo and Kyngas’s content analysis method. Results: The compliance group (CG) showed improved self-care behavior and glycemic control. In the CG, the proportion of participants in the action stage was higher in the exercise and diet domains and lower in the blood glucose testing and medication domains than in the non-compliance group (NCG). Readiness to change, motivation for health behaviors, and social motivation were higher in the CG, whereas personal motivation was higher in the NCG. In this qualitative study, three categories and 11 subcategories were identified. The findings suggest the CG regarded their experience in the program more frequently as positive, whereas the NCG perceived greater barriers to using the mobile application in the program. Conclusion: Based on the differences identified between the CG and NCG, TTM-based strategies are needed to facilitate the progression of NCG to the action stage.

Purpose: The purpose of this study was to investigate the nutritional intake status, according to the risk of diabetic complications in Type 2 diabetes patients. Methods: This was a secondary data analysis study that included 83 patients. The nutritional intake was assessed, using 24-hour dietary recall. The risk of diabetic complications was measured, using the Framingham Risk Score (FRS) and Michigan Neuropathy Screening Instrument Questionnaire (MNSIQ). The nutritional intake was analyzed using the CAN-pro 4.0 program. Data were analyzed using descriptive statistics, Chi-square test, and Independent t-test, using the SPSS WIN 26.0 program. Results: The mean FRS and MNSIQ scores for the participants was 14.46 ± 4.09 and 2.30 ± 2.22, respectively. Thirty two participants (38.6%) were in the high-risk groups for cardiovascular disease and peripheral neuropathy. The participants consumed high amounts of grain and low amounts of vegetables, fruits, and dairy products when compared to the recommended intake. However, the nutritional intake did not differ according to FRS or MNSIQ levels. Consumption of vegetables and fruits were significantly different between high and low risk groups of MNSIQ. Conclusion It is necessary to consider the composition of dietary intake to improve the imbalanced diet in Type 2 diabetes patients and prevent diabetic complications. Type 2 diabetes patients should reduce the intake of grains and sodium, and increase vegetable intake. More deliberate future studies are needed, to investigate the relationship between food intake and the risk status for diabetic complication.

Purpose: Providing continuous self-care support to the growing diabetes population is challenging. Strategies are needed to enhance engagement in self-care, utilizing innovative technologies for personalized feedback. This study aimed to assess the feasibility of the Automated Personalized Self-Care program among type 2 diabetes patients and evaluate its preliminary effectiveness. Methods: A parallel randomized pilot trial with qualitative interviews occurred from May 3, 2022, to September 27, 2022. Participants aged 40e69 years with type 2 diabetes and HbA1c ! 7.0% were recruited. The three-month program involved automated personalized goal setting, education, monitoring, and feedback. Feasibility was measured by participants' engagement and intervention usability. Preliminary effectiveness was examined through self-care self-efficacy, self-care behaviors, and health outcomes. Qualitative interviews were conducted with the intervention group. Results: A total of 404 patients were screened. Out of the 61 eligible patients, 32 were enrolled, resulting in a recruitment rate of 52.5%. Retention rates at three months were 84.2% and 84.6% in the intervention and control groups, respectively. Among the intervention group, 81.3% satisfied adherence criteria.Mobile application's usability scored 66.25, and participants' satisfaction was 8.06. Intention-to-treat analysis showed improvements in self-measured blood glucose testing, grain intake, and HbA1c in the intervention group. Qualitative content analysis identified nine themes. Conclusion Feasibility of the program was verified. A larger randomized controlled trial is needed to determine its effectiveness in self-care self-efficacy, self-care behaviors, and health outcomes among type 2 diabetes patients. This study offers insights for optimizing future trials assessing clinical effectiveness.