预测健康韩国成年人的心率变异性参数：一项初步研究。
Predicting Heart Rate Variability Parameters in Healthy Korean Adults: A Preliminary Study.
机构信息
Physical Activity and Performance Institute, 34965Konkuk University, Seoul, Republic of Korea.
Department of Sports Medicine and Science, Graduate School, 34965Konkuk University, Seoul, Republic of Korea.
出版信息
Inquiry. 2021 Jan-Dec;58:469580211056201. doi: 10.1177/00469580211056201.
The purpose of the study was to examine the development of a multiple linear regression model to estimate heart rate variability (HRV) parameters using easy-to-measure independent variables in preliminary experiments. HRV parameters (time domain: SDNN, RMSSD, NN50, pNN50; frequency domain: TP, VLF, LF, HF) and the independent variables (e.g., sex, age, body height, body weight, BMI, HR, HR, HRR) were measured in 75 healthy adults (male n = 27, female n = 48) for estimating HRV. The HRV estimation multiple linear regression model was developed using the backward elimination technique. The regression model's coefficient of determination for the time domain variables was significantly high (SDNN = : 72.2%, adjusted : 69.8%, < .001; RMSSD = : 93.1%, adjusted : 92.1%, < .001; NN50 = : 78.0%, adjusted : 74.9%, < .001; pNN50 = : 89.1%, adjusted : 87.4%, < .001). The coefficient of determination of the regression model for the frequency domain variable was moderate (TP = : 75.6%, adjusted : 72.6%, < .001; VLF = : 41.6%, adjusted : 40.3%, < .001; LF = : 54.6%, adjusted : 49.2%, < .001; HF = : 67.5%, adjusted : 63.4%, < .001). The coefficient of determination of time domain variables in the developed multiple regression models was shown to be very high (adjusted : 69.8%-92.1%, < .001), but the coefficient of determination of frequency domain variables was moderate (adjusted : 40.3%-72.6%, < .001). In addition to the equipment used for measuring HRV in clinical trials, this study confirmed that simple physiological variables could predict HRV.
本研究旨在探讨使用初步实验中易于测量的自变量建立估计心率变异性 (HRV) 参数的多元线性回归模型。在 75 名健康成年人（男性 n=27，女性 n=48）中测量 HRV 参数（时域：SDNN、RMSSD、NN50、pNN50；频域：TP、VLF、LF、HF）和自变量（例如，性别、年龄、身高、体重、BMI、HR、HR、HRR），以估计 HRV。使用向后消除技术开发 HRV 估计多元线性回归模型。时间域变量的回归模型决定系数显著较高（SDNN=：72.2%，调整后：69.8%，<0.001；RMSSD=：93.1%，调整后：92.1%，<0.001；NN50=：78.0%，调整后：74.9%，<0.001；pNN50=：89.1%，调整后：87.4%，<0.001）。频域变量回归模型的决定系数为中度（TP=：75.6%，调整后：72.6%，<0.001；VLF=：41.6%，调整后：40.3%，<0.001；LF=：54.6%，调整后：49.2%，<0.001；HF=：67.5%，调整后：63.4%，<0.001）。所建立的多元回归模型中时域变量的决定系数非常高（调整后：69.8%-92.1%，<0.001），但频域变量的决定系数为中度（调整后：40.3%-72.6%，<0.001）。除了临床试验中用于测量 HRV 的设备外，本研究还证实了简单的生理变量可以预测 HRV。
Zotero 插件