### INTRODUCTION

_{2max}), and 20 min with 70-90% of VO

_{2max}[5]. Despite these guidelines, most adults do not meet the amount of PA to improve their health, and in Korea, one in four adults was found to lack PA [6]. Therefore, efforts to increase the energy consumption of PA or exercise should be emphasized.

### METHODS

### Subjects

### Experimental design

_{2max}60%, 1 × 30 min), AEx (VO

_{2max}60%, 3 × 10 min), and IEx (VO

_{2max}40% or 80%, 26 min) were performed at random, and the next visit was performed at intervals of 1 week. As soon as the CEx, AEx and IEx sessions ended, participants came down from the cycle ergometer and, while sitting on chairs, the EPOC was measured for 60 min.

### Body composition

### Maximal exercise test

_{2max}. The work rate at 50 rpm was 50 W for males and 25 W for females for the first 2 min and increased by 25 W for males and 12.5 W for females every 2 min until exhaustion or until participants were unable to maintain 50 rpm. The criteria used for achieving VO

_{2max}are a plateau in oxygen consumption as workload increases, a respiratory exchange ratio (RER) greater than 1.15, and maximal HR within 10 beats/min of the age-predicted maximum (220 - age). All participants met the first two criteria. Participants’ heart rates were measured using a Polar V800 monitor (Polar Electro, Kempele, Finland).

### EPOC

_{2max}for 1 × 30 min, and AEx was performed for 10 min. AEx was measured three times per day at 10:00, 13:00, and 16:00. When participants completed each AEx morning session, they were allowed to rest until the next exercise session and measurement time. IEx was performed at 80% VO

_{2max}for 2 min during the first time. This was followed by exercise at 40% VO

_{2max}for 1 min and at 80% VO

_{2max}for 3 min, repeated six times for a total of 26 min. Immediately after the exercise, participants were seated in a chair, while relative VO

_{2}, absolute VO

_{2}, kcal, HR, and duration were monitored continuously for the first 60 min of recovery. The criterion for EPOC value determination was set at the time when VO

_{2}, HR, and RER values returned to the resting BASE. When the measurement was completed, all equipment was removed (Figure 1).

### Statistical analysis

*p*-value) was set at 0.05.

### RESULTS

### Correlation between dependent variables and measured EPOC

### Significance of regression models and independent variables

*t*-test was used to determine the significance of the regression coefficients of the independent variables. Based on the exploratory data analysis results, it was statistically significant when we developed an integrated regression model using the independent variables (FFM and HR_sum) selected from the regression analysis results of the EPOC estimation of each exercise type (Table 3).

### Performance evaluation of regression models and regression equations

^{2}), adjusted coefficient of determination (adjusted R

^{2}), and standard errors of estimates (SEE) for the regression model. The regression model of the EPOC of each exercise type was developed as FFM and HR_sum, with the mean explanatory power of the CEx regression model was 86.3% (R

^{2}) and 85.9% (adjusted R

^{2}), and the mean SEE was 11.73 kcal. The mean explanatory power of the IEx regression models was 83.1% (R

^{2}) and 82.6% (adjusted R

^{2}), while the mean SEE was 13.68 kcal. The mean explanatory power of the AEx regression models was 91.3% (R

^{2}) and 91.0% (adjusted R2), while the mean SEE was 27.71 kcal (Table 4).

### Difference between measured and predicted EPOC of Korean adults

*p*= 0.000, IEx: R = 0.912,

*p*= 0.000, and AEx: R = 0.955,

*p*= 0.000) (Figure 2).

### DISCUSSION

^{2}= 0.879). In addition, the studies of Keytel et al. [19] developed a regression model of energy expenditure during submaximal exercise in their 30s using heart rate (EE1 (kJ/min) = -59.3954 + sex × (-36.3781 + 0.271 × age + 0.394 × weight + 0.404 × VO

_{2max}+ 0.634 × heart rate) + (1 - sex) × (0.274 × age + 0.103 × weight + 0.380 × VO

_{2max}+ 0.450 × heart rate), R

^{2}= 0.833, EE2 (kJ/min) = sex × (-55.0969 + 0.6309 × heart rate + 0.1988 × weight + 0.2017 × age) + (1 - sex) × (-20.4022 + 0.4472 × heartrate -0.1263 × weight + 0.074 × age), R

^{2}= 0.734). The correlation coefficient of energy consumption, excluding the maximum oxygen intake, was 0.734; however, other regression models showed a correlation coefficient of 0.80 or higher, showing a high regression model. However, most studies have aimed to estimate energy consumption during exercise or to estimate the maximum oxygen intake. This study is difficult to compare accurately because it is a study to measure energy consumption after exercise, but it is considered comparable to that of a study using heart rate.

_{2}during interval exercise have a significant linear relationship. As such, heart rate and FFM influence each other and are considered effective variables in estimating EPOC energy consumption in various types of exercises. However, this study has some limitations as a preliminary study. The sample size was small, and we were unable to develop a regression model for the sexes, and validity tests could not be performed. Therefore, further research is required to overcome these limitations.