Department of Computing, University of Turku, Turku, Finland.
Department of Nursing Science, University of Turku, Turku, Finland.
JMIR Mhealth Uhealth. 2022 Jun 3;10(6):e33458. doi: 10.2196/33458.
Heart rate variability (HRV) is a noninvasive method that reflects the regulation of the autonomic nervous system. Altered HRV is associated with adverse mental or physical health complications. The autonomic nervous system also has a central role in physiological adaption during pregnancy, causing normal changes in HRV.
The aim of this study was to assess trends in heart rate (HR) and HRV parameters as a noninvasive method for remote maternal health monitoring during pregnancy and 3-month postpartum period.
A total of 58 pregnant women were monitored using an Internet of Things-based remote monitoring system during pregnancy and 3-month postpartum period. Pregnant women were asked to continuously wear Gear Sport smartwatch to monitor their HR and HRV extracted from photoplethysmogram (PPG) signals. In addition, a cross-platform mobile app was used to collect background and delivery-related information. We analyzed PPG signals collected during the night and discarded unreliable signals by applying a PPG quality assessment method to the collected signals. HR, HRV, and normalized HRV parameters were extracted from reliable signals. The normalization removed the effect of HR changes on HRV trends. Finally, we used hierarchical linear mixed models to analyze the trends of HR, HRV, and normalized HRV parameters.
HR increased significantly during the second trimester (P<.001) and decreased significantly during the third trimester (P=.006). Time-domain HRV parameters, average normal interbeat intervals (IBIs; average normal IBIs [AVNN]), SD of normal IBIs (SDNN), root mean square of the successive difference of normal IBIs (RMSSD), normalized SDNN, and normalized RMSSD decreased significantly during the second trimester (P<.001). Then, AVNN, SDNN, RMSSD, and normalized SDNN increased significantly during the third trimester (with P=.002, P<.001, P<.001, and P<.001, respectively). Some of the frequency-domain parameters, low-frequency power (LF), high-frequency power (HF), and normalized HF, decreased significantly during the second trimester (with P<.001, P<.001, and P=.003, respectively), and HF increased significantly during the third trimester (P=.007). In the postpartum period, normalized RMSSD decreased (P=.01), and the LF to HF ratio (LF/HF) increased significantly (P=.004).
Our study indicates the physiological changes during pregnancy and the postpartum period. We showed that HR increased and HRV parameters decreased as pregnancy proceeded, and the values returned to normal after delivery. Moreover, our results show that HR started to decrease, whereas time-domain HRV parameters and HF started to increase during the third trimester. The results also indicated that age was significantly associated with HRV parameters during pregnancy and postpartum period, whereas education level was associated with HRV parameters during the third trimester. In addition, our results demonstrate the possibility of continuous HRV monitoring in everyday life settings.
心率变异性(HRV)是一种反映自主神经系统调节的非侵入性方法。HRV 的改变与不良的心理或身体健康并发症有关。自主神经系统在怀孕期间的生理适应中也起着核心作用,导致 HRV 的正常变化。
本研究旨在评估心率(HR)和 HRV 参数的趋势,作为一种非侵入性的方法,用于在怀孕期间和产后 3 个月进行远程母婴健康监测。
共有 58 名孕妇在怀孕期间和产后 3 个月使用基于物联网的远程监测系统进行监测。要求孕妇连续佩戴 Gear Sport 智能手表,以监测其 HR 和从光体积描记图(PPG)信号中提取的 HRV。此外,还使用跨平台移动应用程序收集背景和分娩相关信息。我们分析了夜间收集的 PPG 信号,并通过应用 PPG 质量评估方法对收集的信号进行了处理,丢弃了不可靠的信号。从可靠的信号中提取 HR、HRV 和归一化 HRV 参数。归一化消除了 HR 变化对 HRV 趋势的影响。最后,我们使用分层线性混合模型分析了 HR、HRV 和归一化 HRV 参数的趋势。
HR 在孕中期显著增加(P<.001),在孕晚期显著下降(P=.006)。时域 HRV 参数,平均正常心搏间期(AVNN)、正常心搏间期标准差(SDNN)、正常心搏间期连续差的均方根(RMSSD)、归一化 SDNN 和归一化 RMSSD 在孕中期显著下降(P<.001)。然后,AVNN、SDNN、RMSSD 和归一化 SDNN 在孕晚期显著增加(P=.002、P<.001、P<.001 和 P<.001)。一些频域参数,低频功率(LF)、高频功率(HF)和归一化 HF 在孕中期显著下降(P<.001、P<.001 和 P=.003),HF 在孕晚期显著增加(P=.007)。在产后期间,归一化 RMSSD 下降(P=.01),LF 与 HF 比值(LF/HF)显著增加(P=.004)。
本研究表明了怀孕期间和产后期间的生理变化。我们发现,随着妊娠的进行,HR 增加,HRV 参数下降,分娩后恢复正常。此外,我们的结果表明,HR 开始下降,而时域 HRV 参数和 HF 在孕晚期开始增加。结果还表明,年龄与怀孕期间和产后期间的 HRV 参数显著相关,而教育水平与孕晚期的 HRV 参数相关。此外,我们的结果表明,在日常生活环境中进行连续 HRV 监测是可能的。