### INTRODUCTION

### METHODS

### Subjects

### Experimental design

### Body composition

### Blood pressure and resting HR

### NEAT measurement

_{2}) before the measurements. The measurement room had controlled humidity (50%) and temperature (23 ± 1 °C). Sitting, leg jiggling, standing, and walking were performed for 10 min each, and walking was performed on a treadmill (S25T, STEX, Seoul, Korea) at a speed of 4.5 km/h and 6.0 km/h. Stair climbing was performed with a gas analyzer and a stair height of 20 cm stepmill (StairMaster Gauntlet, Core Health, and Fitness, Washington, D.C.), and climbing up one stair and climbing up two stairs was carried out for 1 min each. After the measurement of each item was completed, sufficient rest was provided, and when the energy metabolism returned to the stable level, the measurement was started again [13,17].

### Statistical analysis

*t*-test was used to detect the differences between the measured and predicted NEAT. Bias was calculated as the difference between the measured and predicted NEAT values. 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 (

*p*-value) was set at 0.05.

### RESULTS

### Correlation between dependent variables and measured NEAT

### Significance of regression models and the independent variables

*t*-test to verify the significance of the regression coefficients of the independent variables. The results of the regression analysis for estimating the NEAT for each motion based on the results of the exploratory data analysis are shown in Table 3. The regression coefficients of the selected independent variables (age, weight, HR_average, weight × HR_average, weight × HR_sum, SBP × HR_rest, fat ÷ height

^{2}, gender × HR_average, and gender × weight × HR_sum) for each motion were statistically significant when the integrated regression model was developed using the stepwise method.

### Performance evaluation of regression models and regression equations

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

^{2}), and standard errors of estimates (SEE) were calculated for the regression model. The mean explanatory power of the sitting EE regression models developed by age, weight × HR_average, SBP × HR_rest, and Gender × HR_average were 58.4% (R

^{2}) and 55.9% (adjusted R

^{2}), while the mean SEE was 0.32. The mean explanatory power of the leg jiggling EE regression models developed by age, weight, and gender × HR_average were 56.1% (R

^{2}) and 54.2% (adjusted R

^{2}), and the mean SEE was 0.34 kcal/min. The mean explanatory power of the standing EE regression models developed by age and gender × weight × HR_sum was 59.4% (R

^{2}) and 58.2% (adjusted R

^{2}), and the mean SEE was 0.32. The mean explanatory power of the 4.5 km/h walking EE regression models developed by weight and weight × HR_sum was 70.2% (R

^{2}) and 69.3% (adjusted R

^{2}), and the mean SEE was 0.52 kcal/min. The mean explanatory power of the 6.0 km/h walking EE regression models developed by HR_average and weight × HR_average was 76.5% (R

^{2}) and 75.8% (adjusted R

^{2}), and the mean SEE was 0.62 kcal/min. The mean explanatory power of the climbing-up-1-stair EE regression models developed by age, weight × HR_average, SBP × HR_rest, and fat ÷ height

^{2}were 56.4% (R

^{2}) and 53.7% (adjusted R

^{2}), and the mean SEE was 0.59 kcal/min. The mean explanatory power of the climbing-up-2-stairs EE regression models developed by age, SBP × HR_rest, fat mass ÷ height

^{2}, and weight × HR_sum was 60.8% (R

^{2}) and 58.5% (adjusted R

^{2}), and the mean SEE were 0.74 kcal/min (Table 4).

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

*p*= 0.000; leg jiggling: R = 0.749,

*p*= 0.000; standing: R = 0.771,

*p*= 0.000; 4.5 km/h walking: R = 0.838,

*p*= 0.000; 6.0 km/h walking: R = 0.874,

*p*= 0.000; climbing up 1 stairs: R = 0.751,

*p*= 0.000; and climbing up 2 stairs: R = 0.780,

*p*= 0.000).

### DISCUSSION

_{2}) is considered the most accurate variable for measuring the EE of physical activity and can be measured directly in the laboratory using a metabolic cart or respiratory gas analyzer. Portable devices are available for field measurements, but only for a limited period of time and with a limited number of targets. Therefore, efforts are being made to find a more feasible way to estimate VO

_{2}in field studies [18,19]. In particular, it has been reported that individual characteristics such as age, sex, and weight should be considered. However, easily measurable HR is used as a way to estimate VO

_{2}[20]. Most of the studies using HR have been used in regression models that estimate EE of exercise in active energies. None of the regression models that estimate the EE of NEAT has been studied using HR. Therefore, we suggest ways to estimate NEAT EE using HR.

^{2}); and climbing up 2 stairs EE = 1.442 - 0.023 × age - 0.000093 × (SBP × HR_rest) - 0.121 × (fat mass ÷ height

^{2}) + 0.0000624 × (weight × HR_sum)).

^{2}; gender × HR_average; and gender × weight × HR_sum) for each motion. Previously, Park et al. [21] developed a regression model of the EE of an exercise stress test using the HR of college students in their 20s (EE 1 (cal/min) = 100.127 + (s × - 8577.731) + (w × 106.729) + (h × 12.580) + ((s × w) × 113.209) + ((w × h) × 38.847) + ((s × h) × 1.251) + ((s × h × w) × - 0.23), where s = sex : male-1, female-0, h = heart rate : beat/min, w = weight: kg, R

^{2}= 0.85; and EE 2 (cal/min) = 15289.276 + (s × 117.083) + (w × 102.905) + (h × 1883.398), where s = sex : male-1, female-0, h = heart rate : beat/min, w = weight : kg, R

^{2}= 0.82). In addition, the studies of Charlot et al. [22] developed a regression model to estimate the EE of exercise using HR (EE [kcal · h−1] = 171.62 + 6.87 × HR (bpm) + 3.99 × height (cm) + 2.30 × weight (kg) −139.89 × sex (1 or 2) − 4.26 × resting HR (bpm) − 4.87 × HRmax (bpm), R

^{2}= 0.879). Both estimation expressions show a high regression model with a correlation coefficient of 0.80 or higher, but it was a study to estimate the EE of exercise rather than NEAT activity.

^{2}); and climbing up 2 stairs EE = 1.442 - 0.023 × age - 0.000093 × (SBP × HR_rest) - 0.121 × (fat mass ÷ height

^{2}) + 0.0000624 × (weight × HR_sum). Bias between estimated NEAT and measured NEAT (sitting = - 0.003; leg jiggling = 0.004; standing = 0.003; 4.5 km/h walking = - 0.005; 6.0 km/h walking = 0.003; climbing up 1 stair = 0.007; and climbing up 2 stairs = 0.004) and correlation (sitting: R = 0.764; leg jiggling: R = 0.749; standing: R = 0.771; 4.5 km/h walking: R = 0.838; 6.0 km/h walking: R = 0.874; climbing up 1 stair: R = 0.751; and climbing up 2 stairs: R = 0.780) was reasonable.