Development of a Heart Rate Variability Prediction Equation Through Multiple Linear Regression Analysis Using Physical Characteristics and Heart Rate Variables

Heart rate variability (HRV) is an effective tool for objectively evaluating physiological stress indices in psychological states. This study aimed to develop multiple linear regression equations to predict HRV variables using physical characteristics, body composition, and heart rate (HR) variables (eg, sex, age, height, weight, body mass index, fat-free mass, percent body fat, resting HR, maximal HR, and HR reserve) in Korean adults. Six hundred eighty adults (male, n = 236, female, n = 444) participated in this study. HRV variable estimation multiple linear regression equations were developed using a stepwise technique. The regression equation’s coefficient of determination for time-domain variables was significantly high (SDNN = adjusted R(2): 73.6%, P < .001; RMSSD = adjusted R(2): 84.0%, P < .001; NN50 = adjusted R(2): 98.0%, P < .001; pNN50 = adjusted R(2): 99.5%, P < .001). The coefficient of determination of the regression equation for the frequency-domain variables was high without VLF (TP = adjusted R(2): 75.0%, P < .001; LF = adjusted R(2): 77.6%, P < .001; VLF = adjusted R(2): 30.1%, P < .001; HF = adjusted R(2): 71.3%, P < .001). Healthcare professionals, researchers, and the general public can quickly evaluate their psychological conditions using the HRV variables prediction equation.