Non-invasive wearable devices might be able to predict preterm birth

Even though preterm births have severe health impacts, their frequency hasn’t changed much in recent years. One reason is that we need effective ways to screen for them, and our methods are used less than they are. But there’s hope on the horizon with wearable technology.

These devices provide a new, noninvasive, and easy way to monitor vital health indicators, like a mother’s heart rate variability. Previous studies have shown that this heart rate variability decreases during the first 33 weeks of a normal pregnancy but improves afterward.

A new study aims to determine if changes in maternal heart rate variability (HIV) during pregnancy are linked to how far along the pregnancy is or if they’re a sign of how soon delivery might happen. This study looked at both term and preterm deliveries.

Researchers used the WHOOP Strap, a wearable device that tracks various health metrics, including HRV, to gather data. Previous research using this device showed that HRV decreases during pregnancy until around 33 weeks, after which it starts to increase until delivery. However, all the pregnancies in that study ended at term, so they didn’t explore what happens in preterm births.

In this new study, scientists looked at changes in mHRV during pregnancy, specifically in preterm births, to see if there’s a consistent pattern and if it can predict how soon delivery might happen. If they find that a certain point in pregnancy is linked to changes in mHRV and time to delivery, it could become a useful digital marker for identifying preterm births.

Participants were recruited via a reproductive health survey from the existing WHOOP member base in March 2022 if they were pregnant or delivered between March 2021 and March 2022.

In this study, preterm births are defined as deliveries that happen between 20 and 37 weeks of gestational age, while term births are deliveries between 37 and 42 weeks of gestational age. Any births occurring after 42 weeks of gestational age, known as post-term births, were not included in the analysis. These definitions follow the standard guidelines set by the American College of Obstetricians and Gynecologists.

All data were analyzed using the R programming language. Modeling was performed using the stats package and lmerTest package. Data were expressed as mean ± standard deviation (SD), and differences were tested by a two-tailed t-test.

In this study, participants were divided into preterm and term cohorts. All available heart rate variability (HRV) values recorded during sleep episodes were included for each participant.

Two mixed-effect spline models were fitted for each cohort to examine the point where HRV changes during pregnancy. These models looked at the average weekly HRV within each subject based on gestational age and weeks from birth.

A spline model was chosen because HRV tends to have a nonlinear pattern during the third trimester of pregnancy. A mixed-effect model was also used to account for the natural variations in HRV among individuals.

Considering the longitudinal nature and size of the data, the underlying covariance structure for the mixed-effects model was set to an unstructured covariance. This helped to capture the relationships between HRV measurements over time within each participant.

The study used mixed-effect spline models to analyze maternal heart rate variability (mHRV) based on gestational age and time until birth. They grouped the data into preterm and term deliveries.

For term pregnancies, the researchers found that the trend in mHRV was better represented by the time until birth rather than gestational age.

Similarly, for preterm pregnancies, the trend in mHRV was better captured by the time until birth rather than gestational age.

These findings suggest that wearable technology like the WHOOP strap could serve as a digital marker for predicting preterm delivery. By monitoring changes in nighttime mHRV throughout pregnancy, the device could signal the need for further evaluation and intervention, potentially improving outcomes for at-risk pregnancies.

This study is the first to demonstrate that maternal HRV during pregnancy differs in preterm and term births in singleton pregnancies. Wrist-worn, noninvasive wearable devices present an exciting opportunity for monitoring, allowing for continuous tracking of health metrics such as maternal HRV.

Journal Reference:

Jasinski SR, Rowan S, Presby DM, Claydon EA, Capodilupo ER (2024) Wearable-derived maternal heart rate variability as a novel digital biomarker of preterm birth. PLoS ONE 19(1): e0295899. DOI: 10.1371/journal.pone.0295899

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