Purpose This study aimed to identify the prevalence of chronic diabetic complications in patients with type 2 diabetes mellitus.
Methods Data for adults aged over 30 years, who were diagnosed with type 2 diabetes mellitus and who had at least one claim for the prescription of antidiabetic medication were extracted from the National Health Insurance Service-National Health Screening Cohort in Korea from 2002 to 2015. Statistical analyses were performed using R version 3.5.1.
Results In total, 1,407 patients with type 2 diabetes mellitus without complications were extracted from the database. Patients were observed for an average of 10.43 years. The prevalence of chronic diabetic complications was 84.7% and was significantly higher for patients who were older women, who lived in the capital, and had diabetes mellitus for a longer time. The prevalence of eye disease was the highest at 42.4%, and cerebrovascular disease was the lowest at 15.1%. Cardiovascular disease, peripheral vascular disease, neuropathy, and foot ulcers often occurred between two and four years, and eye disease and nephropathy often occurred over eight years after the diagnosis of diabetes. Prior to the occurrence of nephropathy, microvascular complications such as neuropathy, peripheral vascular disease, and eye disease occurred.
Conclusion These findings provide compelling evidence of the prevalence of chronic diabetic complications based on a national database. Since a high incidence of diabetic complications occurs within a short period of time after the diagnosis of diabetes, aggressive interventions are required to prevent diabetic complications in the early stages after diagnosis.
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Purpose This study aimed to identify the components of artificial intelligence-based healthcare interventions and determine their effects on health behaviors and physiological, psychological, and cost-effectiveness outcomes among adults. Methods Nine core electronic databases were searched for articles published between January, 2009 and May, 2021 using terms related to artificial intelligence, healthcare, and randomized controlled trials. Qualitative synthesis was then performed. Results Of the 1,194 retrieved articles, 20 were selected for analysis. Many of the studies targeted adults who wanted to change their health behaviors, patients with diabetes, and those aged 20~50 years. The characteristics of the artificial intelligence-based healthcare interventions were analyzed in terms of the following components: external data, artificial intelligence technology, problem solving, and goals. Many interventions offered personalized suggestions by learning participant behavior patterns using machine learning technology and diet and physical activity data. The majority of interventions targeted health behaviors and physiological outcomes. These artificial intelligence-based healthcare interventions were effective in decreasing hospital visits and improving psychological outcomes and health behaviors. Conclusion Artificial intelligence-based healthcare interventions can be an important part of decreasing hospital visits and improving psychological outcomes and health behaviors among adults. The results suggest that there is a need to develop and apply appropriate artificial intelligence algorithms for patients with chronic diseases that require continuous management in Korea.
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Purpose This study aimed to examine the direct and indirect effects of general characteristics, basic psychological needs, health promoting behaviors, and emotional status on sleep quality of the older adults with low back pain.
Methods: We conducted a cross-sectional correlational study in B and Y cities between August and September 2020. A total of 217 older adults participated in the study and their general characteristics (age, gender, duration of back pain, pain intensity, disability, perceived health status, risk for malnutrition), basic psychological needs (autonomy, competence, relatedness), health promoting behavior (physical activity, self care), emotional status (depression, quality of life), and sleep quality were measured. Data were analyzed through descriptive analysis, independent t-test, ANOVA with Scheffé post-hoc test, hierarchical multiple regression, and path analysis using SPSS/WIN 22.0 and AMOS 22.0.
Results: The mean age of the participants was 70.31±5.39 years, the pain intensity was 6.40±1.09, and the duration of back pain was 6.69±6.46 years. The significant factors influencing sleep quality were depression (β=.45, p=.001), gender (β=-.22, p=.001), disability (β=.21, p=.003), perceived health status (β=-.21, p=.001), duration of back pain (β=-.20, p=.001), self care on back pain (β=-.15, p=.009), basic psychological needs (β=-.15, p=.001), and risk for malnutrition (β=.03, p=.028).
Conclusion: The findings of this study suggest that special attention is required for older women with high levels of depression and disability due to back pain, especially those with pain duration of less than 5 years or greater than 10 years.
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