Key result
Wearable-tracked maternal HRV trends track weeks until birth, serving as a potential digital preterm delivery biomarker.
Why the study?
Preterm birth incidence remains unchanged partly due to limited screening methods and underuse of existing approaches, motivating exploration of wearable-derived maternal heart rate variability as a novel biomarker.
Does wearable-derived maternal heart rate variability predict time to delivery and preterm birth in pregnant women?
Case-Control (n=241)
Yes
Does wearable-derived maternal heart rate variability predict time to delivery and preterm birth in pregnant women?
Effect estimate: relative log-likelihood ratio = 859.4
Wearable-derived maternal heart rate variability inflection points align more closely with time until birth than gestational age, suggesting its potential as a digital biomarker for preterm delivery.
mHRV trends may predict preterm delivery timing; observational data leaves open clinical utility pending prospective validation.
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 retroactively identified 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 (Akaike information criterion (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.
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Jasinski et al. (2024) conducted a case-control in Preterm birth (n=241). Wearable-derived maternal heart rate variability (mHRV) vs. Term pregnancies was evaluated on Model fit for mHRV trends by weeks until birth vs gestational age (relative log-likelihood ratio = 859.4). Maternal heart rate variability trends in preterm pregnancies showed a significantly stronger fit when aligned by weeks until birth rather than gestational age (relative log-likelihood ratio = 859.4), suggesting its potential as a digital biomarker for preterm delivery.
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