PURPOSE This study aimed to verify the criterion validity of the two-minute step test in older Korean adults, develop an equation for predicting VO2max, and verify cross-validation. METHODS A submaximal exercise test and the two-minute walk test were performed on 150 older adults (74 males and 74 females) aged 65 years or older. Correlation analysis was performed to confirm criterion validity. An equation for estimating VO2max was developed through multiple regression analysis, and cross-validation was confirmed by performing a correlation analysis between measured and predicted values of VO2max. RESULTS The correlation coefficient between VO2max and the two-minute step test was 0.457 (p<.001). The adjusted R2 of the developed VO2max prediction equation was 0.430 (p<.001), and the explanatory variables finally selected were sex, age, number of steps in the two-minute step test, and percentage of body fat. The correlation coefficient between the measured VO2max (19.08±4.36) and the predicted VO2max (19.73±3.36) was 0.654 (p<.001). CONCLUSIONS This study confirmed the criterion validity of the two-minute step test in older Korean adults, and the cross-validation of the developed VO2max prediction formula was verified. The explanatory variables of the prediction equation will be easy to apply in the field, and more meaningful results will be derived if the validity of the prediction equation developed for a larger number of participants is verified.
PURPOSE The purpose of this study was to develop the estimating equations for 4 types of Wingate Anaerobic test setting. METHODS 80 male elite athletes performed 4 trials of the Wingate Anaerobic test by each type. Subjects were conducted the retest one week later. Data collected from the Wingate Anaerobic test included mean power, peak power, and power drop for 30s were measured. Coefficient of correlation was used for validity of type 1(DOS version) and the other types(ver. 2.24, 3.3.0, and 3.2.1). Pearson’s correlation coefficient was used to examine the reliability of test and retest. Simple regression analysis was used for calculating the estimating equation. RESULTS There was significant correlation for absolute value(Watt, p<.01) and relative value(Watt/kg, p<.01) of mean power, absolute value(W, p<.01), relative value(Watt/kg, p<.05), and power drop rate(%, p<.01). Test and retest reliability was excellent for all test variables(p<.01). CONCLUSIONS From the all results, the estimating equation was calculated to convert all outputs from each type to the other types of the Wingate Anaerobic test setting. These findings suggest that the estimating equations are compatiable to 4 types of Wingate Anaerobic test setting.
Purpose The purpose of this study was to develop the new indirect method assessing maximal oxygen uptake (VO2max) using heart rate (HR) and accelerometer during walk exercise. Methods One hundred seven participants (55 male, 52 female) performed a graded exercise test to determine VO2max and two types of 1,600 m walk exercises (fast walk and pace controlled walk). The equations for estimating VO2max was developed by stepwise multiple regression. The validity of developed equations tested through the correlation between measured VO2max and estimated VO2max, was assessed by predicted residual sum of squares, and Bland-Altman plotting. Results VO2maxwas correlated with time, and HR/activity count per minute (ACM) measured in pace controlled walk exercise at all distance (400 m, 800 m, 1,200 m, 1,600 m). The equations were valid significantly and their multiple correlation coefficients or standard estimated error were similar to that Åstrand-Rhyming cycle ergometer test or Rockport 1 mile walk test. Using HR/ACM in pace controlled walk (400 m), it was possible to estimate VO2max(R2: 0.675, %SEE: 10.7). The equation was: VO2max=121.659+6.656×Gender-0.865×Age-9.540×Time-2460.952×HR/ACM (Gender, 0=female, 1=male: Time, hundredth of a minute: HR, heart rate: ACM, activity count per minute). Conclusion Estimation equations developed in this study are considered to estimate VO2max through a shorter distance, or a lower intensity of walk exercise. It is required studies to target a wide range of ages or to develop walk test on a lower bpm.
Purpose Evaluating the aging of senior and providing optimal sevices are important things for successful aging. This study identified functional fitness related with heath of aged 65 years or older and developed an age scale (longevity fitness age) for assessing their aging. Methods Participants were 458 older people (166 male, 292 female). They were divided into healthy group and disease group. Healthy group was used for the development of the longevity age equation and disease group was for investigating the validity of the equation. Participants completed 13 function fitness variables. The first principal component obtained from a principal component analysis was used to compute the equation. All variables except for grip strength and carrying beans were correlated with chronological aged. Grip strength and variables related lower functional fitness had differences between healthy group and disease group. Finally, 4 variables were selected for the equation. Results It was the following: longevity fitness age=0.942*X1+2, 185*X2+0.673*X3+0.051*X4+0.588*chronological age+58.401, where X1=standing up from a supine position, sec (s), X2=maximum walking (s), X3=standing up and sitting down a chair (s), X4=one leg balance with eyes open (s). The longevity fitness age of healthy group do not have a difference compared to their chronological age but disease group had a difference significantly. Age difference (chronological age-longevity fitness age) of sedentary group in disease group was significantly bigger than its active group. Longevity fitness age could assess an aging of senior. Conclusion We suggest that it can use as the tool for early detecting senior who need the health care service.
