Establishment of Age-predictive Equation for Japanese Women Based on Simple Physical Fitness Measurements and Blood Pressure / Journal of Rural Medicine

ABSTRACT

The purpose of this study was to discuss the procedures for establishing indices for physical senility from simple physical fitness tests through linear regression analysis. The fluctuation of residuals obtained from the various regression analyses and the statistical procedures for dealing with the variables when the assumptions concerning the residuals were contradicted, were discussed. In this study, 168 females (mean age 37.7, SD 11.3, range 20 to 65years) who were registered with one occupational insurance organization were examined. They each underwent tests to establish readings for seven variables, including five physical fitness tests standing trunk flexion (FLEX), sit-ups in 30seconds (SIT-UP), vertical jump (VERT), repetitious side step in 20seconds (SIDE), and step test (STEP-ME). Two blood pressure readings while at rest were also taken (SBP, DBP). The results can be summarized as follows. In the forward stepwise regression analysis, the order of entered variables was VERT (R=0.637), SIT-UP (R=0.673), DBP (R=0.696), FLEX (R=0.704), and SIDE (R=0.717). The equation is y=61.38-0.569×(VERT)-0.513×(SITUP)+0.183×(DBP)+0.283×(FLEX)-0.328×(SIDE). Minimum AIC estimates (MAICE) were achieved for this equation. The correlation coefficient between residuals and predicted value was 0.345 (p<0.05). To satisfy the assumptions of the standard regression model, the researchers worked with transformed variables instead of working with original variables. The transformations of the raw data into logarithms and into reciprocals is described. According to the rules of variable elimination using ridge regression analysis, SBP and STEP-ME were eliminated from the set of seven variables. However, an examination of the residuals indicated that there were no advantages with using these transformations compared to the general linear model using raw data. The ratio of the predicted age for each client obtained from the predictive equation and chronological age is regarded as a marker of aging. Therefore, it is necessary for researchers to examine what kind of life styles result in such individual differences.