This pilot study aimed to develop a regression model to estimate the excess post-exercise oxygen consumption (EPOC) of Korean adults using various easy-to-measure dependent variables.
The EPOC and dependent variables for its estimation (e.g., sex, age, height, weight, body mass index, fat-free mass [FFM], fat mass, % body fat, and heart rate_sum [HR_sum]) were measured in 75 healthy adults ( 31 males, 44 females). Statistical analysis was performed to develop an EPOC estimation regression model using the stepwise regression method.
We confirmed that FFM and HR_sum were important variables in the EPOC regression models of various exercise types. The explanatory power and standard errors of estimates (SEE) for EPOC of each exercise type were as follows: the continuous exercise (CEx) regression model was 86.3% (R2) and 85.9% (adjusted R2), and the mean SEE was 11.73 kcal, interval exercise (IEx) regression model was 83.1% (R2) and 82.6% (adjusted R2), while the mean SEE was 13.68 kcal, and the accumulation of short-duration exercise (AEx) regression models was 91.3% (R2) and 91.0% (adjusted R2), while the mean SEE was 27.71 kcal. There was no significant difference between the measured EPOC using a metabolic gas analyzer and the predicted EPOC for each exercise type.
This pilot study developed a regression model to estimate EPOC in healthy Korean adults. The regression model was as follows: CEx = -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum).
Regular physical activity (PA) and exercise are the most important factors for maintaining or improving health, regardless of age or sex [
Physical activity occurs as energy expenditure increases due to physical movement caused by skeletal muscle contraction, and the PA category includes activities necessary for daily life (standing, walking slowly, lifting objects, etc.) and exercise of various intensities. The guidelines on PA recommend at least 150 min of moderate-intensity aerobic activity per week and 75 min of vigorous-intensity aerobic activity per week [
To increase energy consumption, the type, frequency, intensity, and time on various forms of PA and exercise are considered, and the higher the intensity and time invested, the higher the energy consumption. Studies that are effective against promoting fat metabolism according to the intensity and form of exercise have reported that higher intensity is more effective, regardless of the form of exercise. Thus, it can be seen that various aerobic exercises, such as low-intensity to high-intensity, sustained liquor, and interval workouts, increase energy consumption and effective in improving physical function [
In addition to the effective energy consumption generated by exercise, excess post-exercise oxygen consumption (EPOC) is highlighted as oxygen consumption increases due to constant metabolic action to return increased metabolic activity to a stable level during exercise [
However, to measure energy consumption during and after exercise, a metabolic gas analyzer must be used to measure energy consumption by analyzing oxygen and carbon dioxide concentrations [
As described above, to analyze the energy consumption during and after exercise, it is difficult to apply the measured value to the actual field because the laboratory obtains the measured value using the metabolic gas analysis method. Therefore, it is important to develop a simple and accurate energy consumption estimation equation to measure and evaluate various activities during and after exercise. Currently, both domestically and internationally, regression models for estimating energy consumption during exercise have been reported, but there are almost no studies presenting a regression model after estimating the energy consumption of EPOC after exercise. As a regression model was developed using weight and heart rate, it is important to develop a regression model using various dependent variables for EPOC.
Therefore, this study proposes a regression model that estimates EPOC energy consumption using heart rate and various variables based on measuring the energy consumption of EPOC after various types of exercise in Korean adults (male and female). It can be used for effective exercise programs and PA guidelines for health promotion and obesity prevention and can be easily applied in the field.
A total of 75 participants (31 males and 44 females) were included in the present study (
To test the EPOC during and after continuous exercise (CEx), interval exercise (IEx), and accumulation of short-duration exercise (AEx), we used a crossover design. This approach entailed gathering data from the completed three training sessions by the participants on separate test days in a randomized order. Each participant visited the laboratory four times. On the first visit, body composition tests (InBody 770, Biospace Ltd, Seoul, Korea) and maximal cardiopulmonary exercise test (Quark CPET, Cosmed, Italy) were performed. At the second visit after at least 72 h, CEx (VO2max 60%, 1 × 30 min), AEx (VO2max 60%, 3 × 10 min), and IEx (VO2max 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 height, body mass index (BMI), body weight, fatfree mass (FFM), fat mass, and % body fat were measured using bioelectrical impedance analysis equipment (InBody 770, InBody, Seoul, Korea). The participants were asked to wear light clothing and remove metal items and were measured by standing upright barefoot on the machine platform, placing their feet on the electrode of the platform, while their hands gripped the wires on the handles.
The participants underwent a maximal aerobic exercise test using a cycle ergometer (Aerobike, Combi 75 XL, Tokyo, Japan) to determine VO2max. 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 VO2max 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).
All subjects were required to avoid vigorous exercise within 48 h and arrive at the laboratory early in the morning (8:00 AM) after overnight fasting (≥8 h) and rested for 30 min. Next, a standardized breakfast (two pieces of bread (200 kcal), one boiled egg (80 kcal), one cup of orange juice (120 kcal), and one cup of water) was eaten, and the experiment was conducted after resting for 2 h. The ambient room temperature was maintained at 23 ± 1°C. After 10 min of quiet sitting, VO2, ventilation, and RER were measured for 5 min, with the average used as the baseline.
The participants performed CEx, AEx, and IEx on a cycle ergometer. To measure CEx, AEx, and IEx, after entering the speed according to the participant’s exercise intensity on the bicycle, a gas analyzer was worn. CEx was performed at 60% of VO2max 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% VO2max for 2 min during the first time. This was followed by exercise at 40% VO2max for 1 min and at 80% VO2max for 3 min, repeated six times for a total of 26 min. Immediately after the exercise, participants were seated in a chair, while relative VO2, absolute VO2, 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 VO2, HR, and RER values returned to the resting BASE. When the measurement was completed, all equipment was removed (
The means and standard deviations were calculated for all measured parameters. The Shapiro-Wilk test verified the normal distribution of all outcome variables. To perform the linear regression analysis, we verified the independent variables by checking the regression coefficient (β-value). Regression analysis using the stepwise method was used to predict the EPOC based on sex, age, height, weight, BMI, FFM, fat mass, % body fat, and heart rate_sum (HR_sum). A two-tailed Student’s paired t-test was used to detect the differences between the measured and predicted EPOC. The authors rigorously conformed to the basic assumptions. Bias was calculated as the difference between the measured and predicted EPOCs. The authors rigorously conformed to the basic assumptions of a regression model (linearity, independence, continuity, normality, homoscedasticity, autocorrelation, and outlier). Statistical Package for the Social Sciences (SPSS) version 25.0 (IBM Corporation, Armonk, NY, USA) was used for the statistical analysis, and the level of significance (
The absolute value of the standard residual was set to ≥3 to confirm the singular value, and there were no outlier data in the EPOC prediction model. Our study estimated the regression model using a stepwise method, and the correlation between the measured EPOC and the dependent variables is shown in
The importance of each model was determined using an F test, and a
This study calculated the coefficients of determination (R2), adjusted coefficient of determination (adjusted R2), 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% (R2) and 85.9% (adjusted R2), and the mean SEE was 11.73 kcal. The mean explanatory power of the IEx regression models was 83.1% (R2) and 82.6% (adjusted R2), while the mean SEE was 13.68 kcal. The mean explanatory power of the AEx regression models was 91.3% (R2) and 91.0% (adjusted R2), while the mean SEE was 27.71 kcal (
In this study, there was no significant difference between EPOC for each type of exercise measured using a metabolic gas analyzer and the EPOC predicted by the equation. The mean bias between the measured EPOC and predicted EPOC equations were CEx = 0.06 kcal, IEx = -0.08 kcal, and AEx = -0.04 kcal, respectively (
Regular exercise is an effective way to consume energy, and EPOC after exercise also has an important effect on health promotion. EPOC refers to oxygen deficiency during exercise and the energy used for body recovery after exercise [
In this study, a preliminary study was conducted to develop a regression model to estimate the energy expenditure of EPOC in Korean adults using various dependent variables that are easy to measure. Based on the collected data, we developed a regression model of EPOC for each exercise type (CEx = -37.128 + 1.003 × (FFM) + 0.016 × ((HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum)).
It is important to eliminate anomaly values that increase prediction errors before developing regression models that estimate the EPOC. In the regression, we used the absolute value of the standardized residuals to determine outliers, and as a result, no outliers were observed in this study; therefore, it demonstrated clear linearity between the independent and dependent variables.
When examining the correlation between EPOC energy consumption of various exercise types and various dependent variables in Korean adults, heart rate and various measurement variables showed a significant correlation with EPOC (e.g., sex, age, height, weight, BMI, FFM, % body fat, and HR_sum) in various exercise types (
Previously, Charlot et al. [
In addition, the most common method of estimating energy consumption is using an accelerometer, and some studies using heart rate monitoring equipment have also been reported. In the case of accelerometers, energy expenditure measurements show a high correlation between actual measurements, but it has been reported that the evaluation of low PA or restricted movement is underestimated or overestimated [
In this study, a regression model with a high correlation coefficient was proposed using the FFM and heart rate among various variables. In a study by Bot & Hollander [
In conclusion, through preliminary experiments, we developed a regression model using heart rate and various variables to estimate the energy expenditure of various exercise types of EPOC in healthy Korean adults. The developed model is as follows: CEx = -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × ((HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum). The bias between the estimated EPOC and measured EPOC (CEx = 0.06, IEx = -0.08, and AEx = -0.04), and correlation (CEx: R = 0.929, IEx: R = 0.912, and AEx: R = 0.955) were reasonable.
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5B8099542).
Exercise protocol by type of exercise.
Scatter plot between measured and predicted EPOC.
Note: CEx, continuous exercise; IEx, interval exercise; AEx, accumulation of short-duration exercise; EPOC, excess post-exercise oxygen consumption.
Characteristics of subjects
Variables | Both (n = 75) | Males (n = 31) | Females (n = 44) |
---|---|---|---|
Age (yrs) | 33.71 ± 11.00 | 30.32 ± 9.01 | 36.09 ± 11.73 |
Height (cm) | 167.31 ± 9.96 | 177.04 ± 6.56 | 160.46 ± 4.93 |
Weight (kg) | 64.18 ± 13.50 | 77.35 ± 9.57 | 54.90 ± 6.06 |
Body mass index (kg/m2) | 22.69 ± 2.74 | 24.62 ± 2.19 | 21.34 ± 2.24 |
Fat-free mass (kg) | 48.03 ± 12.46 | 61.26 ± 7.48 | 38.72 ± 3.64 |
Fat mas (kg) | 16.07 ± 4.88 | 15.97 ± 5.33 | 16.13 ± 4.60 |
Percent body fat (%) | 25.35 ± 7.23 | 20.44 ± 5.60 | 28.80 ± 6.21 |
VO2max (ml/kg/min) | 33.18 ± 6.64 | 38.50 ± 5.39 | 29.35 ± 4.47 |
CEx (kcal) | 183.60 ± 66.51 | 243.20 ± 41.01 | 141.27 ± 25.63 |
IEx (kcal) | 181.47 ± 65.72 | 245.01 ± 47.73 | 136.71 ± 29.70 |
AEx (kcal) | 182.66 ± 59.90 | 240.59 ± 42.65 | 141.85 ± 27.94 |
CEx EPOC (kcal) | 55.96 ± 31.28 | 75.43 ± 31.13 | 42.24 ± 23.27 |
IEx EPOC (kcal) | 77.62 ± 32.83 | 102.85 ± 27.29 | 59.85 ± 23.53 |
AEx EPOC (kcal) | 126.73 ± 92.50 | 155.82 ± 97.15 | 106.23 ± 84.26 |
Note: Values are expressed as mean ± SD. VO2, oxygen consumption; CEx, continuous exercise; IEx, interval exercise; AEx, accumulation of short-duration exercise; EPOC, excess post-exercise oxygen consumption.
Correlation between dependent variables and measured EPOC for estimating the regression model
CEx | IEx | AEx | ||
---|---|---|---|---|
Sex | Correlation | -.526 |
-.649 |
-.266 |
.000 | .000 | .021 | ||
Age (yrs) | Correlation | -.188 | -.176 | .109 |
p -value | .106 | .130 | .353 | |
Height (cm) | Correlation | .477 |
.609 |
.191 |
p -value | .000 | .000 | .100 | |
Weight (kg) | Correlation | .455 |
.659 |
.365 |
p -value | .000 | .000 | .001 | |
Body mass index (kg/m2) | Correlation | .319 |
.522 |
.451 |
p -value | .005 | .000 | .000 | |
Fat-free mass (kg) | Correlation | .486 |
.694 |
.271 |
p -value | .000 | .000 | .019 | |
Fat mass (kg) | Correlation | .020 | .044 | .327 |
p -value | .864 | .708 | .004 | |
Percent body fat (%) | Correlation | -.317 |
-.422 |
.060 |
p -value | .006 | .000 | .608 | |
HR_sum | Correlation | .840 |
.741 |
.908 |
p -value | .000 | .000 | .000 |
Note:
Significant correlation between the measured EPOC and dependent variables (
CEx, continuous exercise; IEx, interval exercise; AEx, accumulation of short-duration exercise; EPOC, excess post-exercise oxygen consumption; HR, heart rate.
Significance level of the regression coefficient of the independent variable for each estimated regression model
Model | F-value | Unstandardized coefficients | Standardized coefficients | ||||
---|---|---|---|---|---|---|---|
CEx EPOC (kcal) | (constant) | 227.262 | .000 |
-37.128 | -6.458 | .000 |
|
FFM | 1.003 | 0.400 | 9.117 | .000 |
|||
HR_sum | 0.016 | 0.797 | 18.176 | .000 |
|||
IEx EPOC (kcal) | (constant) | 177.207 | .000 |
-49.265 | -6.946 | .000 |
|
FFM | 1.442 | 0.547 | 10.970 | .000 |
|||
HR_sum | 0.013 | 0.609 | 12.216 | .000 |
|||
AEx EPOC (kcal) | (constant) | 376.188 | .000 |
-100.942 | -7.338 | .000 |
|
FFM | 2.209 | 0.298 | 8.542 | .000 |
|||
HR_sum | 0.020 | 0.916 | 26.301 | .000 |
Note:
Statistically significant,
CEx, continuous exercise; IEx, interval exercise; AEx, accumulation of short-duration exercise; EPOC, excess post-exercise oxygen consumption; FFM, fat-free mass; HR, heart rate.
Estimated regression equations are predicting EPOC of Korean adults
Model | R | R2 | Adjusted R2 | SEE | |
---|---|---|---|---|---|
CEx EPOC (kcal) | .929 | .863 | .859 | .000* | 11.73 |
= -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum) | |||||
IEx EPOC (kcal) | .912 | .831 | .826 | .000* | 13.68 |
= -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum) | |||||
AEx EPOC (kcal) | .955 | .913 | .910 | .000* | 27.71 |
= -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum) |
Note:
Statistically significant,
CEx, continuous exercise; IEx, interval exercise; AEx, accumulation of short-duration exercise; EPOC, excess post-exercise oxygen consumption; FFM, fat-free mass; HR, heart rate; SEE, standard error of estimates.
Estimated regression equations are predicting EPOC of Korean adults
Model | Mean | SD | Bias | ||
---|---|---|---|---|---|
Predicted CEx EPOC (kcal) | 57.05 | 29.68 | 0.06 | 0.050 | 0.960 |
Measured CEx EPOC (kcal) | 56.98 | 31.74 | |||
Predicted IEx EPOC (kcal) | 77.55 | 29.54 | -0.08 | -0.053 | 0.957 |
Measured IEx EPOC (kcal) | 77.62 | 32.83 | |||
Predicted AEx EPOC (kcal) | 126.69 | 88.26 | -0.04 | -0.013 | 0.990 |
Measured AEx EPOC (kcal) | 126.73 | 92.50 |
Note: CEx, continuous exercises; IEx, interval exercise; AEx, accumulation of short-duration exercise. Bias = measured EPOC − predicted EPOC.