SCH: INT: Individualizing Care in Pregnancy and Childbirth through Digital Phenotyping

SCH：INT：通过数字表型分析实现妊娠和分娩的个性化护理

• 批准号: 1838901
• 负责人: Kelly Gaither
• 金额: $ 120万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838901&HistoricalAwards=false
• 关键词: SCH; INT; Individualizing; Care; Pregnancy

Bringing together researchers and physicians from the Department of Women's Health, the Texas Advanced Computing Center, and the Institute for Computational Engineering & Sciences at The University of Texas at Austin, the goal of this project is to develop a digital phenotype of pregnancy to better understand factors influencing pregnancy outcomes. Women's health represents one of the most pressing health-policy issues impacting our nation. In no medical specialty are the deficiencies of medical evidence more pronounced than in women's health, especially in obstetrics. Over the course of the human life span, birth is one of the most dangerous health episodes for both mother and baby. Worldwide, between 2.6 and 4 million pregnancies result in stillbirth annually. Unlike other leading causes of mortality, birth-related deaths are largely preventable. Today, however, most adverse pregnancy outcomes are not predictable, and cannot be prevented. In this project, the research team will passively monitor a cohort of one thousand pregnant women from their first prenatal visit to six weeks post-partum. To accomplish this, participants will download the HealthyPregnancy smartphone app developed in this project to collect in situ social and behavioral data. The application passively captures participant's interactions with people and places via sensors and software throughout pregnancy. Analysis of this large collection of digital data, in combination with traditional medical monitoring data collected via participant's medical records will result in a digital phenotype of pregnancy. The digital phenotype allows for a more complete understanding of pregnancy at the macro scale and for more detailed understanding of outcomes as a continuum rather than isolated discrete events. It is widely understood that activity, social support, sleep, and cognitive function are important markers of health, particularly during pregnancy. Maternal obesity is associated with a number of complications in pregnancy including gestational diabetes, pre-eclampsia, macrosomia, caesarean delivery and stillbirth. Lack of social support and social interaction is also an important risk factor and has been shown to have adverse effects on pregnancy outcomes. Sleep disturbances are associated with poor health outcomes, particularly cardiovascular disease and inflammatory responses. Additionally, short sleep duration is associated with an increased incidence in diabetes and obesity and has been associated with an increase in mortality. Research suggests that women who experience pre-eclampsia more frequently report daily cognitive failures and increased emotional dysfunction years later. With the ubiquitous use of smartphones, it is now possible to collect lived experiences or data reflecting markers of pregnancy in the wild. Collecting accelerometer and GPS over time provide an indication of physical mobility and gross motor activity. Call and text message logs detail communication events and contribute to a view of social interaction and social contacts. Additionally, power state, screen time and touch events can be used to understand potential sleep disruption. Of greater significance is the analysis of this data in aggregate over the course of pregnancy. This longitudinal view and analysis of pregnancy in the wild using machine learning and mathematical models provides both an individual's digital phenotype of pregnancy and an aggregate digital phenotype of pregnancy. By gathering and analyzing these two products, they can be used to better understand outcomes and the continuum of events leading up to pregnancy outcomes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个项目汇集了来自女性健康部门、德克萨斯先进计算中心和德克萨斯大学奥斯汀分校计算工程与科学研究所的研究人员和医生，该项目的目标是开发一个数字怀孕表型，以更好地了解影响怀孕结果的因素。妇女健康是影响我国的最紧迫的卫生政策问题之一。在任何医学专业中，医学证据的缺陷都比不上妇女健康，尤其是在产科。在人类的一生中，分娩对母亲和婴儿来说都是最危险的健康事件之一。在世界范围内，每年有260万至400万例怀孕导致死产。与其他主要死亡原因不同，与出生相关的死亡基本上是可以预防的。然而，今天，大多数不良妊娠结局是不可预测的，也无法预防。在这个项目中，研究小组将被动地监测1000名孕妇从第一次产前检查到产后六周的情况。为了做到这一点，参与者将下载在该项目中开发的Healthy Pregnancy智能手机应用程序，以收集现场社交和行为数据。该应用程序被动地捕捉参与者在怀孕期间通过传感器和软件与人和地点的互动。对这些大量的数字数据进行分析，结合通过参与者的医疗记录收集的传统医学监测数据，将得出怀孕的数字表型。数字表型允许在宏观尺度上更完整地了解怀孕，并更详细地了解作为一个连续体而不是孤立的离散事件的结局。人们普遍认为，活动、社会支持、睡眠和认知功能是健康的重要标志，尤其是在怀孕期间。孕妇肥胖与妊娠期间的一些并发症有关，包括妊娠期糖尿病、先兆子痫、巨大儿、剖腹产和死产。缺乏社会支持和社会互动也是一个重要的风险因素，已被证明对妊娠结局有不利影响。睡眠障碍与不良的健康结局有关，特别是心血管疾病和炎症反应。此外，睡眠时间短与糖尿病和肥胖症的发病率增加有关，也与死亡率的增加有关。研究表明，经历先兆子痫的女性更频繁地报告日常认知失败，并在多年后增加情感功能障碍。随着智能手机的无处不在的使用，现在可以收集反映野外怀孕标志的真实经历或数据。随着时间的推移，收集加速度计和GPS可以提供身体活动和总运动活动的指示。通话和短信日志详细记录通信事件，并有助于查看社交互动和社交联系。此外，还可以使用电源状态、屏幕时间和触摸事件来了解潜在的睡眠中断。更重要的是对怀孕期间的这些数据进行汇总分析。这种利用机器学习和数学模型对野外怀孕的纵向观察和分析既提供了个人怀孕的数字表型，也提供了妊娠的总体数字表型。通过收集和分析这两个产品，它们可以用来更好地了解结果和导致怀孕结果的事件的连续性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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• DOI: 10.1145/3437359.3465603
• 发表时间: 2021
• 期刊: PEARC '21: Practice and Experience in Advanced Research Computing
• 作者: Packard, Michael;Stubbs, Joe;Drake, Justin;Garcia, Christian
• 通讯作者: Garcia, Christian

Evaluation of Clustering Techniques for GPS Phenotyping Using Mobile Sensor Data

• DOI: 10.1145/3311790.3396665
• 发表时间: 2020-07
• 期刊: Practice and Experience in Advanced Research Computing
• 作者: Zachary S. Tschirhart;K. Schulz

Collecting and analyzing smartphone sensor data for health

收集和分析智能手机传感器数据以促进健康

• DOI: 10.1145/3437359.3465599
• 发表时间: 2021
• 期刊: PEARC '21: Practice and Experience in Advanced Research Computing
• 作者: Drake, Justin;Schulz, Karl;Bukowski, Radek;Gaither, Kelly
• 通讯作者: Gaither, Kelly