• Physical Therapy Rehabilitation Science
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• v.10 no.1
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• pp.64-68
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• 2021

Objective: In clinical practice, there are a lot of exercise to reduce body weight or reduce the amount of body fat in order to solve back pain. However, many studies have contradicted the relationship between back pain and weight or body fat mass. The purpose of this study was to investigate the relationship between fat mass, body mass index and low back pain of office worker. Design: Crossed-sectional study Methods: Among the white-collar workers diagnosed with non-specific back pain by doctors, subjects who were not included in the exclusion criteria were selected to measure the subject's body fat mass, body mass index, pain intensity, and disability index due to back pain. The NPRS was used for the intensity of back pain of office workers, and the ODI was used for the degree of disability due to back pain. A body composition analyzer was used to measure the body fat mass and body mass index of white-collar workers. Results: There was no significant difference between the two groups in the comparison between the normal group and the excessive group according to the criteria of fat mass and body mass index. In the correlation analysis of fat mass, body mass index, pain intensity, and disability index, it was found that there was a significant correlation between fat mass and body mass index. However, neither fat mass nor body mass index had a significant correlation with pain intensity and disability index. Conclusions: The fat mass and body mass index of office worker do not affect low back pain.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.5
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• pp.1220-1230
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• 2016

The purpose of this study is to investigate the effect of 20s university student bodybuilders' protein intake differences with resistant exercise(weight training) by 12 weeks on solt lean mass and body composltion. Natural protein(Chicken breast meat) intake group and Whey protein isolates(WPI) intake group are the experimental groups. Conventional meal intake group is the control group. This study proposes a efficient protein diet for weight training. The results were as follows. In the experimental group(natural protein intake), muscle mass and lean body mass was significantly increased, but body fat percentage was significantly decreased. In the experimental group(WPI intake), muscle mass and lean body mass was significantly increased, but body fat percentage was significantly decreased. In the control group(conventional meal intake), muscle mass and lean body mass was insignificantly increased, but body fat percentage was insignificantly decreased. In addition, there was not a significant difference among intake groups, and also not a differentiated effect between natural protein and WPI intake. In conclusion, natural protein and WPI made muscle mass and lean body mass rise, body fat percentage reduced effectively. Only WPI intake(without natural protein intake) was the efficient mean to increase muscle mass and lean body mass, and to decrease body fat percentage.

• Tuberculosis and Respiratory Diseases
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• v.70 no.3
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• pp.235-241
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• 2011

Background: The rising prevalence of asthma may be associated with the rising prevalence of obesity in developed nations. There are several studies showing that obesity increases the risk of asthma in adults. We investigated the association of each body composition scale and bronchial hyper-responsiveness. Methods: This study involved a retrospective review of the existing records for 279 subjects with respiratory symptoms, who underwent a pulmonary function test, a methacholine challenge test and a body composition test between May 2007 and June 2009. Results: Of the 279 subjects, 179 (64%) were female. There was a statistically significant difference in fat free mass and in fat free mass index between the normal bronchial responsiveness group and bronchial hyper-responsiveness group (p=0.036; p=0.000). There was no significant differences in body mass index, in fat mass and fat free mass index in the normal bronchial responsiveness group and bronchial hyper-responsiveness group in males. However in females, body mass index and fat free mass index were increased in the bronchial hyper-responsiveness group (p=0.044; p=0.000). Total body water (kg), fat free mass (kg) and soft lean mass (kg) were significantly different between the normal bronchial responsiveness group and bronchial hyper-responsiveness group (p=0.002; p=0.000; p=0.000). Conclusion: This study showed significant differences in fat free mass and in fat free mass index between the normal bronchial responsiveness group and the bronchial hyper-responsiveness group. In females, BMI, soft lean mass, and total body water showed significant differences between the normal bronchial responsiveness group and the bronchial hyper-responsiveness group. We concluded that bronchial hyper-responsiveness was associated with not only body mass index but also fat free mass index in female bronchial asthma.

• Journal of the Korean Society of Food Culture
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• v.26 no.6
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• pp.727-733
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• 2011

This study was performed to examine the relationship between the body mass index (BMI), the body fat, and the serum lipids of post-menopausal women in rural areas. The subjects were 510 women aged 50 and over. As a result of this study, we found a trend of decreasing BMI as age increased, but body fat increased. In addition, there was a significant decreasing of the lean body mass than an increasing of the body mass index according to increasing age. Therefore, this study confirmed that a main cause of rural women being classified as obese is a decrease in lean body mass, rather than an increase in of body fat. Of all subjects, 36.3% ($18.5{\leq}BMI$ < 23) of all subjects were classified as having normal BMI, whereas only 21.4% were classified as having normal body fat. Out of 190 subjects who were body fat 30% and over, 38 subjects were classified as obese ($BMI{\geq}25$) and 113 subjects were classified as overweight ($23{\leq}BMI$ < 25). The percentile of those with a BMI of $25kg/m^2$ was 70, and they had 30.82% body fat. HDL cholesterol showed a negative correlation with anthropometric factors (height, weight, body mass index, waist circumference, waist hip ratio, body fat), and total cholesterol, LDL cholesterol, and triglycerides showed a positive correlation. Especially, total cholesterol, LDL cholesterol, and hip circumference showed significant correlations. Because of differences in the body fat and lean body mass by age group, it seems difficult to assess obesity via BMI only. The elderly especially should have a higher significance placed on body fat or abdominal fat than only BMI.

• Journal of Korean Public Health Nursing
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• v.18 no.2
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• pp.299-311
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• 2004

Purpose: Somatotype drawing developed by Sorensen et al.(1983) has been evaluated as a simple instrument of obesity level without real somatic measuring as height and weight. This study was designed to validate somatotype drawings for obesity assessment by bioelectrical impedance body component analysis. Method: At first questionnaire of somatotype drawing was done. Subjects were measured body component by bioelectrical impedance analysis as weight, BMI(body mass index), WHR(waist-hip ratio), body water, protein mass, mineral mass, body fat mass, skeletal muscle mass, soft lean mass, fat free mass and percent body fat. We evaluated correlations between these data and somatotype drawings and tried to grouping of somatotype drawings with the means of major body component value. Result: The data were collected from 205 college women whose height and weight were $161.2\pm4.8,\;55\pm8.3$. Spearman's correlation coefficients of somatotype drawing were 0.74 with BMI, 0.68 with weight 0.69 with body fat mass, 0.65 with WHR. 0.64 with percent body fat after adiusted age. The grade of somatotype drawings were grouped as 1-2, 3-4, 5-6. 7-9 by BMI, body fat mass, weight, 1, 2-4, 5-6, 7-9 by WHR and 1-2, 3-4, 5-9 by percent body fat(ANOVA and Duncan's method). Conclusion: So quick instrument using somatotype drawings were useful tools for evaluation of obesity level and is applicable to screen degree of body fat in self-administered questionnaire survey.

• Journal of the East Asian Society of Dietary Life
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• v.16 no.3
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• pp.261-271
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• 2006

To study the correlation with increasing body fat mass for the Standard Guideline for Korean Dietary Life, a nationwide, cross-sectional survey was performed with 178 male college students in december 2005. Among the student subjects, 76 were analyzed for body protein mass, body mineral mass, body fat mass, percent body fat, waist-hip ratio(WHR), body mass index(BMI) and obesity degree using Inbody 3.0(Biospace Co, Seoul, Korea). Body protein mass was at a high level of 92.1%, body mineral mass was at a low level of 94.7%. Body fat mass was at a high level of 28.9%, percent body fat was at a high level of 37.3%, WHR was at a high level of 31.6%, and obesity degree was at a high level of 38.2%, BMI was at a high level of 24.3% and BMR was observed at a high level of 41.8%. WHR was correlated with '7. Prepare food proper amount sanitarily.' and obesity degree, while BMl was were correlated with '4. Increase movement and eat proper amount.' positively. BMR was correlated with '6. Enjoy 3 meal a day regularly.' ArmCircle was correlated with '4. Increase movement and eat proper amount.' and fitness score was corrected with '4. Increase movement and eat proper amount', and '8. Enjoy Korean rice food style.' positively. Body protein mass and body mineral mass were negatively correlated with fruit, ArmCircle was positively correlated with fish, obesity degree was positively correlated with soup and BMI and ArmCircle were positively correlated with sweet. '8. Enjoy Korean rice food style.' was negatively significantly correlated with overweight, over percentage body fat, obesity degree > 120 and BMI. Male students were not practicing the Standard Guideline for Korean Dietary Life well with an average score of 37.7%. However, male students who are within the normal range of body composition analysis value have to notice that over range of weight, protein, percentage body fat, WHI and obesity degree was observed above average score of 30%.

• Journal of The Korean Society of Integrative Medicine
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• v.8 no.2
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• pp.53-61
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• 2020

Purpose : We investigated the correlation between body composition and lung function in healthy adults. Methods : This study included 204 healthy adults in whom all measurements were obtained once, and all data were analyzed using the SPSS software for Windows, version 22.0. Pearson's correlation analysis was performed to determine the correlation between body composition (represented by the total body water, protein mass, soft lean mass, mineral mass, basal metabolic rate, fat-free mass, skeletal muscle mass, and body fat percentage) and lung function (represented by the forced vital capacity [FVC], forced expiratory volume in 1 second [FEV₁], the FEV₁/FVC ratio, maximum voluntary ventilation [MVV], maximum expiratory pressure [MEP], and the maximum inspiratory pressure [MIP]). All measurements were obtained by two investigators to improve reliability. A significance level of α=.05 was used to verify statistical significance. Results : Among the lung function measurements obtained in both men and women, the FVC, FEV₁, MVV, and MIP were positively correlated with the total body water, protein mass, soft lean mass, mineral mass, basal metabolic rate, fat-free mass, and skeletal muscle mass in men (p<.05). The FEV₁/FVC ratio was negatively correlated with the total body water, soft lean mass, mineral mass, basal metabolic rate, fat-free mass and the body fat percentage (p<.05). Notably, the FVC, FEV₁, and MVV were positively correlated with the total body water, protein mass, soft lean mass, mineral mass, basal metabolic rate, fat-free mass, and skeletal muscle mass in women (p<.05). Conclusion : This study showed a significant correlation between body composition and lung function in healthy adults. In combination with future studies on lung function, our results can provide objective evidence regarding the importance of prevention of lung disease, and our data can be utilized in rehabilitation programs for patients with respiratory diseases.

• Nutritional Sciences
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• v.2 no.2
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• pp.93-101
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• 1999

The aim of this study was to investigate influence of body build on body composition, energy metabolic state and insulin concentration of blood. 29 male athletes and 36 male non-athletic students were recruited for the study. Anthropometry including chest depth and breadth, fat mass, fat fee mass, tricep skinfold thickness were measured. fasting glucose, lactate, triglyceride, fee fatty acid, and insulin concentration in serum were measured . Body build was assessed using metric index, which calculated by regression equations of Mohr and Greil. The athletic and non-athletic students were allocated to 3 body build, that is leptomorph, mesomorph, and pyknomorph. Resting metabolic rate was calculated. Respiratory quotient was determined through ratio of measured VO$_2$, and V$CO_2$. Most non-athletes have a leptomorphic body build, in contrast to athletes mesomorphic type. The body build type influenced body composition differently between non-athletic group and athletic group. Weight, body mass index, body fat mass and fat mass proportion (%), and fat-free mass increased from leptomorph to pyknormorph in non-athletic group. Pyknormorphic athletes have a significant higher body mass index, fat mass, fat free mass than other body build type. Serum glucose, triglyceride, lactate, insulin showed significant differences only in non-athletic group between leptomorph and mesomorph. RMR increased significantly from leptomorph to mesomorph in non-athletes. There was no significant difference of RQ among 3 body build types in both athletes and non-athletes. This study gives a coherent data on body build and body composition for athletes and non-athletes students. The influence of body builds on energy metabolic status of serum was different between athletes and non-athletes.

• Journal of Korean Medicine for Obesity Research
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• v.6 no.1
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• pp.69-80
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• 2006

Object : Waist circumference(WC), waist-hip ratio(WHR), waist-stature ratio(WSR), and body mass index(BMI) are commonly used for evaluating obesity. This Research were done to determine what is more sensitive obesity indexes(WC, WHR, WSR, BMI) Correlated with body composition such as body fat mass, body fat(%), visceral fat area, and fat free mass. And what is more sensitively correlated obesity indexes with % changes of body composition during weight reduction treatment. Methods : This clinical retrospective research were carried out 127 cases of female obese outpatients with weight reduction treatment during 1 month. Bioelectrical impedence analysis(for body composition) and body size(for anthropometric obesity indexes) were estimated in pre-treatment and post-treatment to evaluate the obesity indexes. Pearson correlation coefficients were used to select useful obesity index. Result & Conclusion : BMI is useful index for diagnosis and evaluation of obesity. WSR is sensitively correlated with visceral fat area and body fat(%). So, WSR is useful index for evaluating abdominal obesity and risk factors of metabolic syndrome. WC is correlated with both body fat mass and fat free mass. WHR is not optimal for diagnosis and evaluation of obesity.

• Journal of Yeungnam Medical Science
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• v.20 no.1
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• pp.53-61
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• 2003

Background: Body weight is an important factor that influence the bone density in postmenopausal women except estrogen dificiency. However, different results are reported about the relationship between body composition and bone density in the postmenopausal women. We have studied the relationship between age, body weight, body mass index (BMI), waist hip ratio (WHR), muscle mass, fat mass, fat free mass and bone density. Materials and Methods: We have studied 127 persons of postmenopausal women who visited university medical center and examined the inbody 3.0 and Dual Energy X-ray Absorptiometry (DEXA) from Jan, 2001 to Jun, 2002. they didn't have any disease and didn't received hormone therapy, osteoporosis therapy or other medication that influence the bone density. Results: The numbers of study subjects is total 127 persons. Mean age is $56.9{\pm}5.14$, mean weight is $59.3{\pm}8.7kg$, mean BMI is $25.37{\pm}3.16(kg/m^2)$, mean fat mass is $20.02{\pm}5.05kg$, mean muscle mass is $37.49{\pm}4.50kg$, mean fat free mass is $39.80{\pm}4.70$, mean BMD is $0.828{\pm}0.148(g/cm^2$). In the result of linear regression analysis, age, height, weight, muscle mass, fat free mass, fat mass, BMI are significant determinants of BMD. In stepwise multiple regression analysis, age is the most significant determinant of BMD and besides age, fat free mass is the most significant determinant of BMD among body composition. Conclusion: In postmenopausal women, age, height, weight, BMI, muscle mass, fat free mass, fat mass are significant determinants of BMD and besides age, fat free mass is the most significant determinant of BMD among the body composition. So, diet and exercise that increase fat free mass will contribute to bone density increment.