• Nutrition Research and Practice
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• v.12 no.1
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• pp.69-77
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• 2018

BACKGROUND/OBJECTIVES: Metabolic risk factors should be managed effectively in patients with type 2 diabetes mellitus (T2DM) to prevent or delay diabetic complications. This study aimed to compare the self-management levels of diet and metabolic risk factors in patients with T2DM, according to the duration of illness, and to examine the trends in self-management levels during the recent decades. SUBJECTS/METHODS: Data were collected from the Korea National Health and Nutrition Examination Surveys (KNHANES, 1998-2014). In our analysis, 4,148 patients with T2DM, aged ${\geq}30years$, were categorized according to the duration of their illness (< 5 years, 5-9 years, and ${\geq}10years$). Demographic and lifestyle information was assessed through self-administered questionnaires, and biomarker levels (e.g., fasting glucose level, blood pressure, or lipid level) were obtained from a health examination. Dietary intake was assessed by a 24-recall, and adherence level to dietary guidelines (meal patterns and intake levels of calories, carbohydrates, vegetable/seaweed, sodium, and alcohol) were assessed. Multivariable generalized linear regression and unconditional logistic regression models were used to compare the prevalence rates of hyperglycemia, dyslipidemia, and hypertension according to the duration of patients' illness, accounting for the complex survey design of the KNHANES. RESULTS: In the multivariable adjusted models, patients with a longer duration (${\geq}10years$) of T2DM had a higher prevalence of hyperglycemia than those with a shorter duration of T2DM (< 5 years) (odds ratio 2.20, 95% confidence interval 1.61-3.01, P for trend < 0.001). We did not observe any associations of disease duration with the prevalence of hypertension and dyslipidemia. In addition, the adherence levels to dietary recommendations did not significantly differ according to disease duration, except adherence to moderate alcohol consumption. There were significant decreasing trends in the prevalence of hyperglycemia in patients with a duration of illness ${\geq}10years$ (P for trend = 0.004). CONCLUSION: Although the proportion of patients with adequate control of glucose levels has improved in recent decades, poorer self-management has been found in those with a longer disease duration. These findings suggest the need for well-planned and individualized patient education programs to improve self-management levels and quality of life by preventing or delaying diabetic complications.

• Clinical and Experimental Pediatrics
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• v.62 no.2
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• pp.55-61
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• 2019

Purpose: Myotonic dystrophy, also known as dystrophia myotonica (DM), is an autosomal dominant disorder with 2 genetically distinct forms. DM type 1 (DM1) is the more common form and is caused by abnormal expansion of cytosine/thymine/guanine (CTG) repeats in the DM protein kinase (DMPK ) gene. Our study aimed to determine whether the age of onset is correlated with CTG repeat length in a population of pediatric patients with DM1. Methods: We retrospectively identified 30 pediatric patients with DM1 that underwent DMPK testing, of which the clinical data of 17 was sufficient. The cohort was divided into 2 subgroups based on the clinical phenotype (congenital-onset vs. late-onset) and number of CTG repeats (<1,000 vs. ${\geq}1,000$). Results: We found no significant difference between the age of onset and CTG repeat length in our pediatric patient population. Based on clinical subgrouping, we found that the congenital-onset subgroup was statistically different with respect to several variables, including prematurity, rate of admission to neonatal intensive care unit, need for respiratory support at birth, hypotonia, dysphagia, ventilator dependence, and functional status on last visit, compared to the late-onset subgroup. Based on genetic subgrouping, we found a single variable (poor feeding in neonate) that was significantly different in the large CTG subgroup than that in the small CTG subgroup. Conclusion: Clinical variables exhibiting statistically significant differences between the subgroups should be focused on prognosis and designing tailored management approaches for the patients; our findings will contribute to achieve this important goal for treating patients with DM1.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.371-375
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• 2007

In this paper, we have implemented the device management system to upgrade T-PAK software in mobile phone. The hybrid scheduling method and selective upgrade method are proposed. Hybrid scheduling method is based on distribution of delivery data in accordance with network traffic load and service priority from device management server to mobile phone. Selective upgrade method manages DSL classified by T-PAK software version to be upgrade using version management established in OMA DM SCOMO. Key mechanism of selective upgrade method is to only deliver DSL to be replaced to the mobile phone. We made an experiment on two methods using MS-700T terminals. The experimental result shows that the proposed method is faster than normal from delivery time standpoint.

• Journal of Korean Academy of Rural Health Nursing
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• v.19 no.1
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• pp.35-43
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• 2024

Purpose: This study aimed to explore the influence of knowledge, problem-solving processes, and self-efficacy on the clinical competency of nursing students in the home health nursing management of diabetes mellitus (DM). Methods: The subjects of this study were 136 nursing students. Data were collected from April 18 to April 29, 2022, and analyzed using the SPSS 23.0 program. Results: The total mean scores of nursing students' knowledge, problem-solving process, self-efficacy, and clinical competency in DM home health nursing management were 71.24, 3.92, 7.47, and 4.09, respectively. Clinical competency was significantly and positively correlated with the problem-solving process (r=.60, p<.001) and self-efficacy (r=.48, p<.001) but not with knowledge (r=.09, p=.311). The problem-solving process was also positively correlated with self-efficacy (r=.41, p<.001). Regression analysis revealed a 41.4% variance in the nursing student's clinical competency with problem-solving process (β=.47, p<.001) and self-efficacy (β=.28, p<.001). Conclusion: The results of this study provide valuable evidence for the development of educational interventions aimed at enhancing the clinical competency of nursing students in relation to home-visit healthcare services for DM management.

• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.198-209
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• 2014

Diabetes Mellitus (DM) is well known for increasing morbidity and mortality, especially related to their complications. The purpose of this study was to investigate the factors affecting the prevalence rate of DM and provide a fundamental material to develop an intervention strategy to reduce the prevalence rate of DM. The study subjects were adults aged over 19 on the basis of the primitive data of "The Fifth Korean National Health and Nutrition Examination Survey, 2012". Therefore, the data of 5995 participants were analyzed. For data process, the complex sample analysis module of SPSS 18.0 program was employed to add weighting before analysis. According to the analysis, the prevalence rate of DM of the study subjects was 10.5%. Regarding the odds ratio of DM prevalence, the subjects who graduated from middle school had the odds ratio 2.51 times higher than those who graduated from college and more; those in subjective bad health condition 4.77 times higher than those in subjective good health condition; those in obesity 1.44 times higher; those with high blood pressure 2.57 times higher; those with hyperlipidemia 2.63 times higher; those who fail to control their weight 1.31 times higher; those going on a diet 2.75 times higher. This study revealed that a level of education, perceived health status, obesity, high blood pressure, hyperlipidemia, weight control, and dietary therapy were the predictable variables of the prevalence rate of DM, and thereby suggested the nursing direction and research direction to reduce the prevalence rate of DM.

• Asian Journal of Turfgrass Science
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• v.1 no.1
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• pp.18-29
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• 1987

This study was carried out to examine the effect of nitrogen application and clipping interval on the characteristics of several turf components of korean lawngrass for the basic data of lawn management. It was treated by Split plot design with three replications. The main plots were nitrogen levels with 0, 350, and 700kgN / ha, and the sub plots were clipping intervals with 10, 20, and 30 days The results obtained are summarized as follows ; 1. Increasing the rate of nitrogen fertilizer and frequent clipping increased tiller number of korean lawngrass and the maximum number of tillers obtained in October were recorded from 700kgN application and clipping treatment of 10 days interval. Meanwhile, treatment of 350kgN with 10 days clipping interval increased tillers much more than those of 700kgN with 20 and 30 days clipping intervals. 2. The average number of green leaves occurred during the growth period maximized by applying 700 kgN and clipping 10 days interval. 3. Increasing tiller numbers significantly decreased tops DM weight per tiller by clippng plants at interval of 10 and 20 days, irrespective of nitrogen applied, and with nil N, at the interval of 30 days. By applying 700kgN however, tops DM weight per tiller increased as the number of tillers increased consistently. 4. The highest tops DM weight was achieved from late August to early September by applying 350 and 700kgN. 5. During the growth period, nitrogen application increased unders(stolon+root) DM weight, and, at the same level of nitrogen applied, the increase in stolon DM weight enhanced by lengthening the clipping interval to 30 days. 6. Nitrogen efficiency to green leaves, stolon nodes and DM weight of root with high nitrogen was achieved as clipping interval was shortened.

• Journal of Nutrition and Health
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• v.49 no.5
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• pp.304-312
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• 2016

Purpose: The purpose of the study was to investigate the association between food intakes and cardiovascular disease (CVD) risk factors in Korean women with type 2 diabetes mellitus (T2DM). Methods: The data were collected from the 2007~2012 Korea National Health and Nutrition Examination Survey (KNHANES). In this study, subjects were divided into two groups, the normal fasting glucose (NFG) group (n = 7,738) and the T2DM group (n = 225). Dietary intake was derived from the nutrition survey, which was collected by trained dietitians using 24-hour dietary recall through the face-to-face interview method in the sample person's home. Results: After adjustment for confounding factors, mean fruit (p = 0.0265), fruit and vegetable without kimchi (p = 0.0295), and fish (p = 0.0112) intakes were significantly lower in the T2DM group than in the NFG group. In the multiple logistic regression analysis, odds ratio (OR) for risk of high systolic blood pressure (${\geq}140mmHg$) was lower in the over the median compared to under the median for fruit intakes (OR; 0.657, 95% CI; 0.523~0.824). The OR for the risk of hypertriglyceridemia was lower in the over the median compared to under the median for fruit and vegetable without kimchi (OR; 0.828, 95% CI; 0.7111~0.963) and fish (OR; 0.783, 95% CI; 0.673~0.910) intakes. Conclusion: These results show that intakes of fruits, fish, and fruits and vegetables without kimchi have beneficial effects on CVD in Korean women with T2DM.

• Knowledge Management Research
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• v.19 no.2
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• pp.151-162
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• 2018

Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.496-513
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• 2022

Artificial intelligence or big data technologies can benefit finance companies such as those in the insurance sector. With artificial intelligence, companies can develop better customer segmentation methods and eventually improve the quality of customer relationship management. However, the application of AI-based customer segmentation in the insurance industry seems to have been unsuccessful. Findings from our interviews with sales agents and customer service managers indicate that current customer segmentation in the Korean insurance company relies upon individual agents' heuristic decisions rather than a generalizable data-based method. We propose guidelines for AI-based customer segmentation for the insurance industry, based on the CRISP-DM standard data mining project framework. Our proposed guideline provides new insights for studies on AI-based technology implementation and has practical implications for companies that deploy algorithm-based customer relationship management systems.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.