• Nutrition Research and Practice
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• 제5권6호
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• pp.569-577
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• 2011

The purpose of this study was to identify dietary patterns among Korean elementary school girls based on the change in body mass index (BMI), body fat, bone mineral density (BMD), and bone mineral content (BMC) during 22 months and to explore the characteristics of dietary patterns identified. Girls aged 9-11 years were recruited and 3-day dietary data were collected four times. Subjects with a diet record of 8 or more days and anthropometric data measured at baseline and 22 months later were included (n = 198). Reduced rank regression was utilized to derive dietary patterns using a change in BMI, body fat, and calcaneus BMD and BMC as response variables. Two dietary patterns were identified: the "Egg and Rice" dietary pattern and "Fruit, Nuts, Milk Beverage, Egg, Grain" (FNMBEG) dietary pattern. Subjects who had high score on the FNMBEG pattern consumed various food groups, including fruits, nuts and seeds, and dairy products, whereas subjects in the "Egg and Rice' dietary pattern group did not. Both dietary patterns showed a positive association with change in BMI and body fat. However, subjects who had a higher score on the "Egg and Rice" dietary pattern had less of a BMC increase, whereas subjects who had a higher score on the FMBEG dietary pattern had more increased BMC over 22 months after adjusting for age, body and bone mass, and Tanner stage at baseline. Our results provide evidence that a well-balanced diet contributes to lean body mass growth among young girls.

• 한국치위생학회지
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• 제16권1호
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• pp.129-136
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• 2016

Objectives: The purpose of the study is to investigate the relationship between oral health status and body mass index (BMI) in adults over 65 years old. Methods: The study subjects were 4,550 adults over 65 years old from the 5th Korea National Health and Nutrition Examination Survey(KNHANES V) in 2010-2012. Mastication-related oral health status included the number of remaining teeth, and mean number of decayed, missing, and filled permanent teeth(DMFT). Body mass index(BMI, $kg/m^2$) was categorized as underweight(<18.5), normal weight (18.5-22.9), overweight(23.0-24.9), and obese(${\geq}25.0$). Multinomial logistic regression analysis was performed to examine the association of BMI categories with the number of remaining teeth and DMFT. Results: The mean number of DMFT was highest($13.0{\pm}0.7$) in the underweight group and lowest($8.8{\pm}0.3$) in the obese group. Those having less favorable masticatory ability, and fewer number of remaining teeth and no prosthesis, tended to be underweight. Those having a higher number of remaining teeth and prosthetic teeth tended to be overweight or obese. In the multinomial logistic regression analysis, compared with those having 20 or more remaining teeth, including prosthetic teeth, those having less than 20 remaining teeth and no prosthesis had 4.48 times higher odds ratio of being underweight. DMFT was positively associated with underweight, while negatively associated with overweight or obesity. Conclusions: The masticatory ability and dental caries prevention maintained the healthy body weight in adults of old age.

• 대한영양사협회학술지
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• 제22권2호
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• pp.79-87
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• 2016

The aim of this study was to explore the relationship between demographic characteristics and atopic dermatitis, along with adolescents' body mass index. Also, this study explored the association between dietary intakes (fruits, soda, caffeinated beverages, instant food, cracker, vegetables, and milk) of adolescents and atopic dermatitis. Korea Youth Risk Behavior Web-based Survey for 2014 was used for data analysis, in which a total of 3,532,149 middle and high school students participated. Data were analyzed by descriptive analysis and logistic regression based on the complex sample design using SPSS ver.20.0 statistics. The results showed that males had a higher prevalence rate of atopic dermatitis than females. The significant association between body mass index and atopic dermatitis was found (F=46.625, P<0.001). Students who have higher levels of body mass index showed a higher prevalence rate of atopic dermatitis. Finally, the findings showed that the intake of vegetable and milk had associations with atopic dermatitis (F=6.795, P<0.001). Greater vegetable intake was associated with less atopic dermatitis whereas greater milk intake was associated with more atopic dermatitis prevalence. Based on the above results, we found that demographic characteristics, body mass index, and some dietary food intakes of adolescents had influences on prevalence rate of atopic dermatitis.

• 기본간호학회지
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• 제20권3호
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• pp.230-238
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• 2013

Purpose: This study was done to identify the relationship among bone mineral density (BMD), body composition and osteoporosis self-efficacy and to identify predictors of BMD in female nursing students. Method: Participants were 154 nursing students. Osteoporosis self-efficacy was determined by a self-report questionnaire. BMD was measured by ultrasound bone densitometry and body composition by a body composition analyzer. Data were collected between April 1 and 27, 2013 and analyzed using descriptive statistics, ANOVA, Scheff$\acute{e}$ test, Pearson correlation coefficient, and multiple regression with SPSS 18.0. Results: Mean BMD at the calcaneus site was $0.58{\pm}1.31$ (T-score). Incidence of osteopenia was 11.7%. Percentage of body fat (PBF)-defined obesity had higher prevalence than body mass index (BMI)-defined obesity. BMD had significant positive correlations with skeletal muscle mass (r=.226, p=.005) and fat free mass (r=.225, p=.005). The factor predicting BMD was skeletal muscle mass with 4.7% of explained variance. Conclusion: Study results indicate that of body composition components, skeletal muscle mass is the prime predicting factor for BMD. Thus to promote healthy bones, it is important to strengthen the muscles using a program, based on balanced development of all muscles.

• 복식문화연구
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• 제14권1호
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• pp.40-47
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• 2006

Three-dimensional body scanners were used for years in the clothing manufacturing fields. The 3D body surface provide essential data to draft patterns for mass customization, virtual fit model, and computerized patternmaking systems. This research proposed the methods of drafting patterns for men's jacket by using three dimensional body scan data. Eight male subjects were scanned, the surface data was flattened. The differentials of the flattened body surface and the jacket draft were measured, and analyzed the regressions. To verify the fit of the patterns, the jacket was constructed by the regression formulae and tested by experts. The fit of the jacket were significantly improved rather than a ready-made suit especially the shoulder areas. This means that the methods that we proposed were good to improve the fit of the garments and could be used effectively to implement mass customization strategies in the apparel retail industry.

• 대한물리의학회지
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• 제19권1호
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• pp.43-51
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• 2024

PURPOSE: Sarcopenic obesity (SO) is a clinical condition that combines sarcopenia and obesity. This study examined the frequency of SO in young Korean females between 20 and 29 years of age. METHODS: The study involved 1,000 participants. The height, weight, body mass index (BMI), waist circumference, skeletal muscle mass index, fasting glucose, triglyceride, total cholesterol levels, systolic and diastolic blood pressure, alcohol consumption, and smoking status were the research variables. The skeletal muscle mass index was calculated as appendicular skeletal muscle mass (ASM) divided by the BMI. The ASM was assessed using dual X-ray absorptiometry. Complex sampling analysis and multiple logistic regression were used for analysis. RESULTS: A .74(.30-1.80) frequency of SO was observed. The statistically significant risk factors in females were height, weight, BMI, waist circumference, skeletal muscle mass index, total cholesterol, systolic blood pressure, and diastolic blood pressure (p < .05). CONCLUSION: Young Korean adults with SO have a .74(.30-1.80) frequency of occurrence that is linked to specific risk factors. Hence, primary care clinicians and health care professionals should consider these factors when patients require a referral for early detection and treatment. Healthcare professionals and clinicians can identify potential SO patients by acknowledging these risk factors.

• Tuberculosis and Respiratory Diseases
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• 제72권5호
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• pp.433-440
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• 2012

Background: The pulmonary function test is the most basic test method to diagnosis lung disease. The purpose of this study was to research the correlation of the body mass index (BMI), the fat percentage of the body mass (fat%), the muscle mass, the fat-free mass (FFM) and the fat-free mass index (FFMI), waist-hip ratio (WHR), on the forced expiratory volume curve. Methods: Between March and April 2009, a total of 291 subjects were enrolled. There were 152 men and 139 female (mean age, $46.3{\pm}9.92$ years), and they were measured for the following: forced vital capacity (FVC), forced expiratory volume at 1 second ($FEV_1$), and forced expiratory flow during the middle half of the FVC ($FEF_{25-75}$) from the forced expiratory volume curve by the spirometry, and the body composition by the bioelectrical impedance method. Correlation and a multiple linear regression, between the body composition and pulmonary function, were used. Results: BMI and fat% had no correlation with FVC, $FEV_1$ in male, but FFMI showed a positive correlation. In contrast, BMI and fat% had correlation with FVC, $FEV_1$ in female, but FFMI showed no correlation. Both male and female, FVC and $FEV_1$ had a negative correlation with WHR (male, FVC r=-0.327, $FEV_1$ r=-0.36; p<0.05; female, FVC r=-0.175, $FEV_1$ r=-0.213; p<0.05). In a multiple linear regression of considering the body composition of the total group, FVC explained FFM, BMI, and FFMI in order ($r^2$=0.579, 0.657, 0.663). $FEV_1$ was explained only fat% ($r^2$=0.011), and $FEF_{25-75}$ was explained muscle mass, FFMI, FFM ($r^2$=0.126, 0.138, 0.148). Conclusion: The BMI, fat%, muscle mass, FFM, FFMI, WHR have significant association with pulmonary function but $r^2$ (adjusted coefficient of determination) were not high enough for explaining lung function.

• 지식경영연구
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• 제16권3호
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• pp.81-102
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• 2015

With the rapid development of digital media, there has been a huge change in a way of communication, a process of information diffusion and a role of traditional media. Not like mass media, social media enables users to generate and tap into the opinions of a larger world. From that reason, social media is impacting marketing strategies. However, still social media marketing researches just focus on case study, analysis of users motivation or analysis of power user's usage pattern. Word-of-mouth has always been important especially in marketing area. In social media, word-of-mouth depends on each user that's why this research focuses on individual user's activity in SNS. I defined 4 factors (produce, diffusion, network size, activity of network size enlarge) that are effect on activity and verified hypothesis by multiple regression analysis, hierarchical regression analysis and moderated multiple regression.

• 한국정밀공학회지
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• 제21권6호
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• pp.69-76
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• 2004

This paper presents a prediction model for the aerosol generation of cutting fluid in turning process. Experimental studies have been carried out in order to identify the characteristics of aerosol generation in non-cutting and cutting cases. The indices of aerosol generation was mass concentration comparable to number generation, which is generally used fur environment criterion. Based on the experimental data, empirical model for predicting aerosol mass concentration of cutting fluid could be obtained by a statistical analysis. This relation shows good agreement with experimental data.

• Clinical Nutrition Research
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• 제12권2호
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• pp.138-153
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• 2023

The prevalence of metabolic syndrome (MetS) and its cost are increasing due to lifestyle changes and aging. This study aimed to develop a deep neural network model for prediction and classification of MetS according to nutrient intake and other MetS-related factors. This study included 17,848 individuals aged 40-69 years from the Korea National Health and Nutrition Examination Survey (2013-2018). We set MetS (3-5 risk factors present) as the dependent variable and 52 MetS-related factors and nutrient intake variables as independent variables in a regression analysis. The analysis compared and analyzed model accuracy, precision and recall by conventional logistic regression, machine learning-based logistic regression and deep learning. The accuracy of train data was 81.2089, and the accuracy of test data was 81.1485 in a MetS classification and prediction model developed in this study. These accuracies were higher than those obtained by conventional logistic regression or machine learning-based logistic regression. Precision, recall, and F1-score also showed the high accuracy in the deep learning model. Blood alanine aminotransferase (β = 12.2035) level showed the highest regression coefficient followed by blood aspartate aminotransferase (β = 11.771) level, waist circumference (β = 10.8555), body mass index (β = 10.3842), and blood glycated hemoglobin (β = 10.1802) level. Fats (cholesterol [β = -2.0545] and saturated fatty acid [β = -2.0483]) showed high regression coefficients among nutrient intakes. The deep learning model for classification and prediction on MetS showed a higher accuracy than conventional logistic regression or machine learning-based logistic regression.