Association between wearable sensor signals and expected hormonal changes in pregnancy.

IF 10.8 1区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
EBioMedicine Pub Date : 2025-09-01 Epub Date: 2025-08-27 DOI:10.1016/j.ebiom.2025.105888
Giulia Milan, Lauren Ariniello, Katie Baca-Motes, Arij Faksh, Jacqueline K Kueper, Jay A Pandit, Tolúwalàṣẹ Àjàyí, Giorgio Quer
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引用次数: 0

Abstract

Background: The rising maternal health crisis in the United States necessitates innovative approaches to pregnancy monitoring. This observational cohort study aimed to assess whether wearable sensors can effectively track physiological and behavioural changes during pregnancy and examine their associations with pregnancy-related hormonal fluctuations.

Methods: This longitudinal cohort study recruited participants via a bilingual mobile research platform. Eligible participants were aged ≥16 years, pregnant or within eight weeks postpartum, residing in the United States, and consented to share data collected by their own wearable device (Apple, Garmin, or Fitbit). Self-reported survey responses and wearable sensor data were collected from 99 participants who experienced a live birth pregnancy, from three months pre-pregnancy to six months postpartum. Key outcomes included changes in resting heart rate (RHR), physical activity, and sleep patterns, analysed in relation to expected hormonal fluctuations during pregnancy.

Findings: In live birth pregnancies, RHR initially decreased between weeks 5-9, followed by a steady increase until 8-9 weeks before delivery, then declined until birth, dropping below pre-pregnancy levels postpartum before stabilising at six months. Total sleep time increased in the first trimester but decreased throughout the remainder of pregnancy. A strong correlation was observed between RHR fluctuations and pregnancy-induced hormonal changes (R2 = 0.93). Pregnancies ending in adverse outcomes displayed distinct RHR patterns compared to live birth pregnancies.

Interpretation: These findings suggest that wearable sensors provide a non-invasive method to monitor pregnancy-related physiological and behavioural changes, which align with hormonal shifts. This study provides population-level insights in live birth pregnancies, and an exploratory analysis of adverse outcomes reflecting the feasibility of recruiting and capturing physiological signals also in these cases. This approach may enable early risk assessment for adverse pregnancy outcomes, including miscarriage and preterm birth.

Funding: This work was supported by the National Center for Advancing Translational Sciences (UM1TR004407) and from the Patrick J. McGovern Foundation.

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可穿戴传感器信号与妊娠期预期激素变化之间的关系。
背景:美国日益严重的孕产妇健康危机需要创新的妊娠监测方法。这项观察性队列研究旨在评估可穿戴传感器是否能有效跟踪怀孕期间的生理和行为变化,并研究它们与妊娠相关激素波动的关系。方法:纵向队列研究通过双语移动研究平台招募参与者。符合条件的参与者年龄≥16岁,怀孕或产后8周内,居住在美国,并同意分享他们自己的可穿戴设备(Apple, Garmin或Fitbit)收集的数据。自我报告的调查反馈和可穿戴传感器数据收集自99名经历活产妊娠的参与者,从孕前3个月到产后6个月。主要结果包括静息心率(RHR)、身体活动和睡眠模式的变化,这些变化与怀孕期间预期的激素波动有关。研究结果:在活产妊娠中,RHR最初在5-9周期间下降,随后稳步上升至分娩前8-9周,然后下降至分娩前,产后降至孕前水平以下,6个月时稳定。总睡眠时间在怀孕的前三个月增加,但在怀孕的其余时间减少。RHR波动与妊娠引起的激素变化之间存在很强的相关性(R2 = 0.93)。与活产妊娠相比,以不良结局结束的妊娠表现出明显的RHR模式。解释:这些发现表明,可穿戴传感器提供了一种非侵入性的方法来监测与怀孕相关的生理和行为变化,这些变化与激素变化相一致。本研究提供了活产妊娠人群水平的见解,并对不良后果进行了探索性分析,反映了在这些情况下招募和捕获生理信号的可行性。这种方法可以对不良妊娠结局进行早期风险评估,包括流产和早产。资助:本研究由国家促进转化科学中心(UM1TR004407)和Patrick J. McGovern基金会支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
EBioMedicine
EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
17.70
自引率
0.90%
发文量
579
审稿时长
5 weeks
期刊介绍: eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.
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