Wearable-Derived Maternal Heart Rate Variability As A Novel Digital Biomarker of Preterm Birth

Abstract

Despite considerable health consequences from preterm births, their incidence remains unchanged over recent decades, due partially to limited screening methods and limited use of extant methods. Wearable technology offers a novel, noninvasive, and acceptable way to track vital signs, such as maternal heart rate variability (mHRV). Previous research observed that mHRV declines throughout the first 33 weeks of gestation in term, singleton pregnancies, after which it improves. The aim of this study was to explore whether mHRV inflection is a feature of gestational age or an indication of time to delivery. This retrospective case-control study considered term and preterm deliveries. Remote data collection via non-invasive wearable technology enabled diverse participation with subjects representing 42 US states and 16 countries. Participants (N=241) were recruited from the WHOOP (Whoop, Inc.) userbase and wore WHOOP straps during singleton pregnancies between March 2021 and October 2022. Mixed effect spline models by gestational age and time until birth were fit for within-person mHRV, grouped into preterm and term births. For term pregnancies, gestational age (AIC = 26627.6, R2m = 0.0109 , R2c= 0.8571) and weeks until birth (AIC =26616.3, R2m = 0.0112 , R2c = 0.8576) were representative of mHRV trends, with significantly stronger fit for weeks until birth (relative log-likelihood ratio = 279.5). For preterm pregnancies, gestational age (AIC =1861.9, R2m = 0.0016, R2c = 0.8582) and time until birth ( AIC = 1848.0, R2m = 0.0100, R2c = 0.8676) were representative of mHRV trends, with significantly stronger fit for weeks until birth (relative log-likelihood ratio= 859.4). This study suggests that wearable technology, such as the WHOOP strap, may provide a digital biomarker for preterm delivery by screening for changes in nighttime mHRV throughout pregnancy that could in turn alert to the need for further evaluation and intervention.

Preterm Birth Risk Stratification through Longitudinal Heart Rate and HRV Monitoring in Daily Life

“…However, in our study, RMSSD showed the least influence in estimating PTB. Furthermore, Jasinski et al 62 explored maternal HRV trends and their association with gestational age and time until birth. While this study showed a strong fit for weeks until birth in predicting maternal HRV changes in both term and preterm pregnancies, our study extends this understanding by discovering distinct patterns in PTB abnormality scores…”

“…Assessed continuously using the Oura ring 61 during the second and third pregnancy trimesters, the result of this study supports the potential of tracking HRV for PTB detection. Additionally, Jasinski et al 62 have explored the relationship between maternal HRV and gestational weeks and time until birth, enrolling 241 pregnant women wearing WHOOP straps 63 . While these approaches highlight the significance of maternal HRV monitoring, they have not investigated the potential of PTB estimation during pregnancy.…”

“…While these approaches highlight the significance of maternal HRV monitoring, they have not investigated the potential of PTB estimation during pregnancy. It is noteworthy that these studies 60,62 have been limited to one HRV parameter, i.e., the root mean square of successive NN interval difference (RMSSD). Therefore, they overlooked the analysis of HRV from different perspectives, which might potentially improve PTB estimation.…”

Phenotyping the autonomic nervous system in pregnancy using remote sensors: potential for complication prediction

“…In our study, low RMSSD in placental abruption as compared to healthy individuals showed HRV's potential for prediction application. Placental abruption is a significant contributing factor to preterm birth, a condition associated with sympathetic predominance of the ANS in the existing literature (Aye et al, 2018;Jasinski et al, 2022). Placental abruption involves the partial or complete separation of the placenta from the uterine wall before the onset of labor.…”

Does anyone fit the average? Describing the heterogeneity of pregnancy symptoms using wearables and mobile apps

“…With the ubiquity of the smartphone [15], and the increasing popularity of wearable smart devices [16] such as the smartwatch and the smart ring, the use of such tools for monitoring pregnancy-related signs and symptoms and greatly transforming current knowledge of this important metamorphosis is a potential reality. While the body of research involving personal DHTs in pregnancy is small, a number of small preliminary studies have shown potential utility in using wearables for tracking activity, sleep, and physiologic metrics such as HR and HRV as predictors of pregnancy-related outcomes such as preterm birth and delivery readiness [17][18][19][20][21][22][23].…”