• Nutritional Sciences
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• v.4 no.2
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• pp.112-119
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• 2001

This study was performed fir the purpose of finding the relationship between fat-free mass (FFM) which mainly represents muscle mass change, and muscle strength. The correlation between diet and exercise behaviors and FFM in healthy elderly men over age 55 was also investigated. FFM and corresponding hand grip strength declined significantly with increasing age. However the concentration of serum albumin, the indicator of protein nutritional status, showed no change with increases in age. The subjects were categorized into three groups according to FFM textiles. Anthropometry such as height, BMI, circumferences of waist and hips, body fat mass percent and hand grip strength decreased significantly in the lowest FFM group. But albumin level showed no change relative to FFM level. FFM showed a significant relationship to nutrient intake such as energy and fat. However, no association was found with exercise behavior probably because there were no instances of resistance exercise habits among the subjects studied. The variance in FFM could be attributed 86.2% to height and age. The variance of height-adjusted FFM could be attributed only 40.6% to age and fat intake. In conclusion decreases in FFM may cause reductions in muscle strength in elderly men. Increasing energy and fat intake were associated with increased FFM and may protect elderly men from the risk of sarcopenia. FFM had no association with endurance exercise habits.

• Journal of Nutrition and Health
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• v.34 no.4
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• pp.449-457
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• 2001

This study was performed to show change of fat-free mass(FFM), representing mostly the muscle mass change, and muscle strength with increasing age, and relationship between dietary, exercise behaviors and FFM in healthy middle-and old aged women who are of age over 55 years. The FFM and correspondingly hand grip strength showed significant positive correlation with age. But concentration of serum albumin showed no significant relationship with age. The subjects were categorized into groups according to FFM tertile. The anthropometry such as weight, BMI, fat mass, circumferences of waist and hip, WHR, and hand grip strength decreased significantly in the lowest FFM group. But the albumin level showed no change according to FFM level. The FFM showed significant correlation with nutrient intakes such as energy, carbohydrate, protein, Fe, P, Ca. No association, however, was shown with exercise behavior probably because of no case with resistance exercise habits. The variance of FFM was explained 55.2% by height and carbohydrate intake. The variance of height-adjusted FFM could be explained only 16.2% by intake amount of carbohydrate. In conclusion, the decrease of FFM may cause to reduce muscle strength in female elderly. The increasing nutrient intakes were associated with the increased FFM and may protect from risk of sarcopenia. However, only the carbohydrate intake could influence independently the FFM in middle- and old-aged women. The FFM has no association with endurance exercise habits. (Korean J Nutrition 34(4) : 449∼457, 2001)

• Korean Journal of Exercise Nutrition
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• v.24 no.1
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• pp.9-13
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• 2020

[Purpose] This preliminary study aimed to develop a regression model to estimate the resting metabolic rate (RMR) of young and middle-aged Koreans using various easy-to-measure dependent variables. [Methods] The RMR and the dependent variables for its estimation (e.g. age, height, body mass index, fat-free mass; FFM, fat mass, % body fat, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, and resting heart rate) were measured in 53 young (male n = 18, female n = 16) and middle-aged (male n = 5, female n = 14) healthy adults. Statistical analysis was performed to develop an RMR estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and age were important variables in both the regression models based on the regression coefficients. Mean explanatory power of RMR₁ regression models estimated only by FFM was 66.7% (R²) and 66.0% (adjusted R²), while mean standard errors of estimates (SEE) was 219.85 kcal/day. Additionally, mean explanatory power of RMR₂ regression models developed by FFM and age were 70.0% (R²) and 68.8% (adjusted R²), while the mean SEE was 210.64 kcal/day. There was no significant difference between the measured RMR by the canopy method using a metabolic gas analyzer and the predicted RMR by RMR₁ and RMR₂ equations. [Conclusion] This preliminary study developed a regression model to estimate the RMR of young and middle-age healthy Koreans. The regression model was as follows: RMR₁ = 24.383 × FFM + 634.310, RMR₂ = 23.691 × FFM - 5.745 × age + 852.341.

• Journal of Korean Medicine for Obesity Research
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• v.5 no.1
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• pp.57-66
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• 2005

Objectives : The purpose of this study is to examine accuracy of predicted resting energy expenditure (REE), relationship fat free mass (FFM) and REE. Methods : 60 normal, obese women $(body\;mass\;index\;{\geq}25kg/m^2)$ were recruited for this study, they had low calorie diet for 8 weeks. At week 0, 4, and 8, REE was measured by MedGem (indirect calorimeter), Bioelectrical impedance analysis (BIA) using Cunningham equation, and Harris-Benedict (H-B) equation, FFM was also measured by BIA. Results : The REE predicted by BIA was lower than the REE measured by MedGem (MG) in every measurement. The REE predicted by H-B equation predicted REE was lower than that of MG in the second measurement (p<0.01). The REE measured by MedGem was declined after 8 weeks, BIA and H-B equation predicted REE were declined after 4 weeks (p<0.01). H-B equation predicted REE had more significant correlation with the REE measured by MedGem than that of BIA. There was significant correlation between measured REE and FFM, but measured REE declined after 8 weeks, FFM declined after 4 weeks (p<0.01). We derived a prediction equation as follows : REE = 108.36+31.42 (FFM), $R^2=0.23$.

• Tuberculosis and Respiratory Diseases
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• v.72 no.5
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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.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.2
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• pp.196-201
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• 2005

The aim of this study was to confirm the validity of predictive equations for the calculation of basal energy expenditure (BEE). One hundred twenty female college students were participated in this study. The resting energy expenditure (REE) was measured by indirect calorimetry for 30 minutes following an 12 hour overnight fasting. Among the available equations for predict BEE, Harris-Benedict, WHO/FAO/UNU and Cunningham methods were selected. Body composition was estimated by bioelectrical impedance analysis (BIA) for the equation of predicted BEE. The mean of measured REE was 1257.2$\pm$147.9 kcal/day, while the predicted value by Harris-Benedict, WHO/FAO/UNU and Cunningham were 1373.3$\pm$45.4 kcal/day, 1290.0$\pm$61.7 kcal/day and 1187.6$\pm$49.2 kcal/day, respectively. The Cunningham equation was more closed to measured values than Harris-Benedict and WHO/FAO/UNU equation by the correlation coefficient. Comparing Pearson's correlation coefficients, fat-free mass (FFM), body surface area (BSA) and body weight were higher than others such as height, body mass index (BMI), fat and fat%. The FFM's correlation coefficient was the highest as 0.74. Thus, the conclusion of this study suggested that the main determinant of BEE was FFM, and we derived a prediction equation as follows: BEE=-569.86+48.27 (FFM).

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.35-41
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• 2021

[Purpose] This pilot study aimed to develop a regression model to estimate the excess post-exercise oxygen consumption (EPOC) of Korean adults using various easy-to-measure dependent variables. [Methods] The EPOC and dependent variables for its estimation (e.g., sex, age, height, weight, body mass index, fat-free mass [FFM], fat mass, % body fat, and heart rate_sum [HR_sum]) were measured in 75 healthy adults (31 males, 44 females). Statistical analysis was performed to develop an EPOC estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and HR_sum were important variables in the EPOC regression models of various exercise types. The explanatory power and standard errors of estimates (SEE) for EPOC of each exercise type were as follows: the continuous exercise (CEx) regression model was 86.3% (R²) and 85.9% (adjusted R²), and the mean SEE was 11.73 kcal, interval exercise (IEx) regression model was 83.1% (R²) and 82.6% (adjusted R²), while the mean SEE was 13.68 kcal, and the accumulation of short-duration exercise (AEx) regression models was 91.3% (R²) and 91.0% (adjusted R²), while the mean SEE was 27.71 kcal. There was no significant difference between the measured EPOC using a metabolic gas analyzer and the predicted EPOC for each exercise type. [Conclusion] This pilot study developed a regression model to estimate EPOC in healthy Korean adults. The regression model was as follows: CEx = -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum).

• The Korean Journal of Rehabilitation Nursing
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• v.8 no.1
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• pp.13-19
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• 2005

Purpose: This study was aimed to identify the effects of group exercise on physiological functions in Elderly. Method: The design of research was one group pretest-posttest design. The subjects were sixty two elderly women persons over sixty years old to live in C-GUN, The exercise period was 12-weeks and frequency was three times per week. the exercise duration was sixty minites. In order to evaluate the effect of exercise, we measured %body fat, fat mass(FM), fat free mass(FFM), body mass index(BMI), body metabolic rate(BMR), Total Cholesterol(TC), Triglyceride(TG), high density lipoprotein-c(HDL-C), low density lipoprotein-c(LDL-C) before and after the 12 week's group exercise program. The data analyzed by the paired t-test using SPSS package. Result: There was significant difference in FFM(p=.000), BMR(p=.000), HDL-C(p=.010). There was no significant difference in weight(p=.122), %body fat(p=.062),, FM(p=.704), BMI(p=.136), TC(p=.125), TG(p=.064), LDL-C(p=.575) Conclusion: This group exercise for elderly women can be recommended as an effective nursing intervention for elderly women in a community.

• Journal of Sensor Science and Technology
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• v.26 no.5
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• pp.314-323
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• 2017

Indicators to quantitatively evaluate the body function may help to optimize the effectiveness of rehabilitation therapy for stroke patients. In this study, we analyzed the body composition in the paretic and nonparetic regions of stroke patients with hemiplegia caused by cerebral hemorrhage (7 cases) and cerebral infarction (13 cases) using multifrequency bioelectrical impedance. Specifically, we considered fat mass (FM), fat-free mass (FFM), FFMI index (FFMI), FM/FFM relation, body cell mass (BCM), basal metabolic rate (BMR), and BMR/FFM relation to evaluate the bodily function in the paretic and nonparetic regions. These values showed considerable differences according to grades determined by the stroke causes and the paralysis status. In the paretic regions, the FFM, FFMI, BCM, and BMR were low and the FM was high. In contrast, the nonparetic regions showed a high FFM and low FM. Furthermore, the paretic and nonparetic regions of all patients suitably fit a linear relation (slope: 22.17 kcal/day/kg) between BMR and FFM. Therefore, bio-electrical impedance measurements can be very useful to quantitatively assess paretic and nonparetic regions in hemiplegic stroke patients.

• Journal of Biomedical Engineering Research
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• v.13 no.2
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• pp.133-140
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• 1992

Bioelectrical Impedance method for measuring human body composition is based upon the principle that the electrical conductivity of the fat-free mass (FFM) is far greater than that of the (at. Deter- mination o( impedance was nlade in 30 healthy and obese men aged $40.9{\pm}14.7$yr (mean${\pm}$SD) and 25 healthy and obese women aged $44.0{\pm}10.2$ yr. A commercial impedance body fat analyzer was used with a four electrode arrangement that introduced a painless constant current(ImA at 50 kHz) into the body. Linear relationships were found between impudence values and FFM(r=-0.786) and between weight and FFM(r=0.837). Signigicant increases in the correlation coefficients were ob served when the predicator Hta/z was regressed against FFM(r=0.912) where Ht Is height and Z Is impedance. A linear regression equation, FFM=0.586(Htf/z) +0.317(Weight) -1.674(r=0.977), was found. The correlation coefficient of % body fat between the impedance and infrared methods was 0.898. These data Indicate that the bioelectrical impedance technique is a reliable and valld approach for the estimation of human body composition. This method Is safe, noninvasivr, prouides rapld measure menu, requires little operator skill and subject cooperation, and Os portable. Finally measurement of percent body fat was made possible using both the regression equation and a developed impedance measuring device which measures impedance between wrist and ankle.