On establishing of predictive model of total body water. (II).
- VernacularTitle:体内総水分量（ＴＢＷ）予測式作成の試み ＩＩ 青壮年期の男子について
- Author:
KAZUTOSHI KIKKAWA
;
SHUICHI KOMIYA
- Publication Type:Journal Article
- From:Japanese Journal of Physical Fitness and Sports Medicine
1987;36(3):105-115
- CountryJapan
- Language:Japanese
-
Abstract:
In this paper, a predictive equation for TBW (Total Body Water) from various anthropometric measurements was established. Fifty-seven healthy adult males, aged 19 to 54 years old, volunteered as subjects in this experiment.
Ten anthropometric measurements were taken for each subject such as standing height (HT), body weight (WT), breadth of humerus and femurs (B1, B2), girth of upper arm and calf (G1, G2), and skinfold thickness of triceps, subscaplar, suprailliac and abdomen (S1, S2, S3, S4) along with the amount of ingested deutrium oxide (D2O) .
Total body water was quantitied by the analysis of dilution of orally ingested D2O in urine. The method of forward stepwise regression analysis was adopted to establish the predictive equation. The stopping rule to select the variables was F statistics (F=2.0) . Furthermore, some criteria such as AIC (Akaike's an information criterion), Mallows' Cp, Schwarz's criterion, R* (adjusted for degree of freedom R) were derived as the regression equation was constructed at each step. These criteria contributed to selecting the best and most valid equation of all equations possible. The results obatained was summarized as follows.
1) Firstly, descriptive statistics were derived for all subjects. Mean (±S. D, ) of TBW was 34.85 (±5.38) l. Skewness and kurtosis were not significant. Multico-linearlity was suggested by correlation matrix (10×10) of all independent variables.
2) Six variables were entered into the equation such as sequences WT, S4, HT, G2, S1, B1. The multiple correlation coefficient (R) and standard error of estimates (SEE) of this equation were 0.961 and 1.568, respectively. It was derived as follows:
Y=-34.56+0.170×HT+0.231×WT+0.567×G2+1.37×B1-0.167×S1-0.086×S4
3) Since the analysis of residuals suggested that abnormal values were contained in this sample, the next regression analysis was adopted after deleted the results of 7 subjects whose standardized residuals were over 1.5. Consequently, the regression equation composited from WT, S4, S2, HT, S1 was evaluated as the best equation according to Cp and Schwarz criterion. AIC selected the equation which added S3 as the 6 th variables. The multiple regression equation established at this stage was described as follows, and R and SEE were 0.9857, 0.940, respectively.
Y=8.10+0.4573×WT-0.0839×S4-0.0951×S2+0.1089×HT-0.1368×S1
4) The specific problem was not obtained from statistics of residuals. However, the co-ordinates of eis (standardized residuals) and predicted value suggested a specific changing pattern. The low possibility of existing multico-linearlity was determined from the eigen value of correlation matrix between independent variables.
