孕期Oura环数据的大规模分析:睡眠、温度趋势、心肺和活动指标的时间轨迹。

IF 6.2 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Rebecca Adaimi, Nina Thigpen, Alicia Clausel, Neta Gotlieb, Ketan Patel, Massimiliano de Zambotti
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引用次数: 0

摘要

背景:怀孕和分娩涉及重大的健康挑战,包括可预防的孕产妇死亡、严重并发症以及与社会决定因素有关的差异,强调需要改善孕产妇保健。妊娠可以从更全面、持续的护理模式中受益,这种模式可以捕捉动态变化并提高母胎结局。目的:这项大规模、真实、高密度的研究使用可穿戴数据来调查导致流产、早产和足月分娩的孕妇生物行为轨迹,探索年龄和体重指数(BMI)等人口统计学因素如何影响这些轨迹。方法:回顾性观察分析10318例18-51岁Oura Ring女性使用者的妊娠情况,其中早产324例,足月5039例,妊娠20周前流产4955例。通过广义估计方程(GEE)统计模型分析孕前8周、孕期和产后64周的孕鼠生物行为数据。结果:胎龄成为足月妊娠所有领域的重要因素(P 15 ms,镜像模式)。呼吸频率在第9周左右达到峰值,此后下降。在怀孕的前三个月,步数下降,在第8周左右减少了2000步。在怀孕中期略有反弹之后,运动量再次下降,在分娩前后达到最低点,比怀孕前减少了约2500步。结论:这些发现强调了怀孕期间参与者睡眠、体温趋势、心肺和活动数据变化的复杂动态,涉及广泛的适应。更深入地关注规范变化可以推进母胎医学,改善临床结果,并解决科学差距。临床试验:
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large-Scale Analysis of Oura Ring Data During Pregnancy: Temporal Trajectories in Sleep, Temperature Trends, Cardiorespiratory, and Activity Metrics.

Background: Pregnancy and childbirth involve significant health challenges, including preventable maternal deaths, severe complications, and disparities tied to social determinants, emphasizing the need for improved maternal care. Pregnancy could benefit from a more comprehensive, continuous care model that captures dynamic changes and enhances maternal-fetal outcomes.

Objective: This large-scale, real-world, high-density study uses wearable data to investigate maternal biobehavioral trajectories for pregnancies leading to loss, preterm, and term births, exploring how demographics factors like age and body mass index (BMI) affect these trajectories.

Methods: Retrospective observational analysis of pregnancies from a sample of 10,318 18-51-year-old female Oura Ring users (324 preterm births, 5039 term births, 4955 pregnancies ending in loss before 20 weeks of gestation). Oura biobehavioral data were analyzed across a 64-week window encompassing 8 weeks pre-pregnancy, through pregnancy and postpartum, via Generalized Estimating Equation (GEE) statistical modeling.

Results: Gestational age emerged as a significant factor across all domains among term pregnancies (P < .001). During the first trimester, participants experienced marked sleep changes, peaking around week 9 and characterized by more time in bed (+30 min), asleep (+15 min), and awake (+15 min) compared to pre-pregnancy. Metrics declined and stabilized in the second trimester; by the third, time in bed returned to baseline, while sleep remained reduced and wakefulness elevated. At birth, time in bed and wakefulness peaked, and sleep duration reached its minimum, with nighttime wake exceeding 3 standard deviations above baseline. Temperature changes were more pronounced, sustained, and occurred earlier than sleep changes-becoming evident by week 4, peaking at +0.3 °C above baseline by week 9, and showing a steady decline until birth. A secondary, modest increase (+0.1 °C) was observed near birth, followed by a decline postpartum. Heart rate (HR) increased steadily, peaking at +10 bpm above baseline at week 32, while HR variability (HRV) declined by >15 ms in a mirrored pattern. Respiratory rate peaked around week 9 and declined thereafter. Step count declined in the first trimester, with a ~2,000-step reduction at around week 8. After a slight rebound mid-pregnancy, activity declined again, reaching its lowest point near birth, with >2,500 fewer steps than pre-pregnancy. Age and BMI showed significant but modest interaction effects (all P <.01). In pregnancies ending in loss, deviations emerged up to two weeks prior. Time in bed decreased starting ~2 weeks before loss (P <.001), followed by reductions in sleep duration (P <.001), temperature trends (P <.001), respiratory rate (P =.019), HR (P =.005), and awake time (P =.033).

Conclusions: These findings highlight the complex dynamics underlying changes in participants' sleep, temperature trends, cardiorespiratory, and activity data throughout pregnancy, involving extensive adaptations. A deeper focus on normative changes can advance maternal-fetal medicine, improve clinical outcomes, and address scientific gaps.

Clinicaltrial:

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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