The purpose of the study was to determine relationship of abdominal fat, adipocytokine, bone mineral density, and bone turnover markers in obese male adolescents. Twenty four male adolescents (obese: 12, normal: 12) volunteered to participate in the study. Anthropometry and skeletal maturity were measured. Body composition and bone mineral density were estimated by DXA (Hologic, QDR-4500, USA). Abdominal fat with total adipose tissue (TAT), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and visceral adipose tissue to subcutaneous adipose tissue ratio (VSR) were estimated by computed tomography (ECLOS, HITACH, Japan). Blood samples were obtained for and analysis of adipocytokines including leptin and adiponectin. Bone turnover markers, osteocalcin (OC), bone-specific alkaline phosphatase (BALP) for bone formation markers and N-terminal telopeptide (NTx), C-terminal telopeptide (CTx) for bone resorption markers were analysed. All data were analyzed utilizing SAS 9.3 (SAS Institute, NC, USA). Independent t-test was used to evaluate the differences between obese adolescents and normal adolescents. Pearson correlation analysis was applied to figure out the relationship between abdominal fat, adipocytokines, bone mineral density, and bone turnover markers. Multiple regression analysis was used to find out the factors of abdominal fat which influence on bone mineral density. A level of significance was set at p<.05. The results of the study indicated that fat tissue (p<.001), percent body fat (p<0.001), TAT (p<.001), VAT (p<.001), and SAT (p<0.001) were significantly higher in obese adolescents than normal adolescents. However bone mineral contents were significantly higher in normal adolescents. Normal adolescents have significantly higher whole body BMD and lumber BMD than obese adolescents. Abdominal fat including VAT and SAT related negatively with whole body BMD and lumbar BMD. Leptin related negatively with BMD whereas adiponectin related positively with BMD. NTx for bone resorption marker related positively with abdominal fat. Visceral adipose tissue was a predictor for whole body BMD and lumbar BMD in explaining 46% and 32% in adolescents. In conclusion, obese male adolescents have lower whole body BMD and lumbar BMD than normal adolescents. Abdominal fat including VAT and SAT related negatively with whole body BMD and Lumbar BMD. And leptin and adiponectin were closely related with BMD. Finally, visceral adipose tissue was a predictor for whole body and lumbar BMD in adolescents.
This study has been conducted to develop methods and techniques for the analysis of data related to baseball performance using the winning and losing games. The purposes of the study were to examine differences of athlete performance for semi playoff, playoff, and Korean professional baseball series and to develop optimal forecasting model for the short term series. Data used in the study were taken from Korean professional baseball association. Three data sets including semi play off from 1982 to 2012, play off from 1989 to 2012, and Korean series from 1982 to 2012 were used. To compare athlete performance by winning and losing games for short-term series t-test was applied. This study created new parameters by weighted value through the equalization process to calculate skill related variables as a predicted variable. Three predicted models such as discriminant, binary logistic regression and artificial neural network models were developed to clarify the suggested models. The results showed that the number of significant parameters increased as the series continued. In particular, a variable related to error was added as a significant variable at the Korean Series. A third base hit in the play-off and a second base hit were also added as significant parameters in the play-off and the Korean series, respectively. In addition, W/L a major variables affecting a given technology area, the pitching PO, PO, the inertia, KS, the pitching, respectively. An artificial neural network model was finally selected with the highest accuracy and lowest input of estimated parameters in the semi play-off. In the play-off, artificial neural network model that applied technical area parameters by specialist criteria had better accuracy rate than two others. In the Korean series, artificial neural network model that created estimation parameters by applying all parameters was chosen as the final model. When the overall accuracy level of semi-play off, play off and Korean series was figured out, binary logistic regression model had higher accuracy of classification than discriminant model, but artificial neural network model had the higher accuracy of classification than binary logistic regression model.
Purpose The purpose of the study was to determine difference of body composition, bone mineral density and health-related fitness by physical activity level in young women. Methods A total of 90 women aged 19-29 years participated in this study. The subjects were divided into three groups (low, middle, and high level) according to the physical activity level estimated by bone-specific physical activity questionnaire(BPAQ). Body height and weight were measured. Body composition parameters including four sites of bone mineral density(BMD) were estimated by DXA (Hologic, QDR-4500, USA). Health-related fitness tests was assessed using sit & reach, grip strength, sit-ups, and VO2max. Statistical analysis was performed using SAS version 9.4. All data were presented in terms of means and standard deviations. One-way ANOVA was applied to determine difference of dependent variables by physical activity level. Duncan's multiple range test was used as a post-hoc test. The statistical significance level was set at p < .05. Results There were significant differences on body weight(F = 4.867, p = .01), body mass index(F = 5.053, p = .008) and fat-free mass(F = 8.364, p = .0001) among the three groups. Significant differences were found on whole body BMD(F = 16.730, p = .0001), lumbar BMD(F = 11.480, p = .0001), femur BMD(F = 42.182, p = .0001) and forearm BMD(F = 5.560, p = .005) among the three groups. There were also significant differences on sit and reach(F = 11.433, p = .0001), sit-ups(F = 17.972, p = .0001), VO2max(F = 3.106, p = .05) and duration of GXT(F = 7.479, p = .001). Conclusions There were differences on body composition, bone mineral density and health-related physical fitness by physical activity levels. Nevertheless, the questionnaire used in this study was not able to judge participation in various exercise types including aerobic exercise or resistance exercise. Therefore, in the future study, longitudinal study considering various types of physical activity and dietary intake will be needed.
The present study has been carried out with a purpose of a long term estimation for the body size and BMI (Body Mass Index) of Korean children and youth using ARIMA, a time series model. In order to select an estimation model for the optimum time series, among the time series analysis method of SPSS22.0 statistic programs, a multivariate ARIMA (p,d,q) model has been selected that has an input series (physical education facility, time spent for physical education, animal source foods, GDP deflator, animal source food intake ratio), using annual average data of height, weight, and BMI data from 1965 to 2015. Among the several optimal measurements in ARIMA model with estimation variables, an optimal RMSE-based model (RMSE: Root Mean Square Error) has been selected. Using this model, the estimation model and estimated values of children’s height, weight, and BMI have been suggested for each age group. The results are as the following. The trend estimation of height follows a logistic curve, with both male and female groups showing increasing trends. The weight has a trend of increasing ratio higher than height. BMI also shows a trend curve similar to weight. The estimation model has been mostly ARIMA(0,1,0). In particular, the average BMI has been estimated as 22-23 for male students in 6th, 8th, 9th, 11th and 12th grade in 2030. This indicates the recent increasing obesity as children and youth occupy most of daily time for play culture that is far from physical activities, such as computer games, smartphone games, and video games at home.
