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.

将妇女健康系，德克萨斯州高级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师汇集在一起​​，该项目的目标是开发一种数字化怀孕表型，以更好地了解影响怀孕的影响因素。妇女健康是影响我们国家的最紧迫的健康政策问题之一。在没有医学专业的情况下，医学证据的缺陷比妇女的健康更为明显，尤其是在妇产科中。在整个人类的生命过程中，出生是母亲和婴儿最危险的健康情节之一。在全球范围内，每年2.6至400万怀孕导致死产。与死亡的其他主要原因不同，与出生有关的死亡是可以预防的。然而，如今，大多数不良怀孕的结果是无法预测的，不能预防。在这个项目中，研究小组将被动监视一千名孕妇的同类，从首次访问产前访问到产后六周。为此，参与者将下载该项目中开发的HealthyPregnancy智能手机应用程序，以收集原位社交和行为数据。该应用被动地捕获参与者在整个怀孕期间通过传感器和软件与人和地方的互动。分析大量数字数据，结合通过参与者的医疗记录收集的传统医学监测数据，将导致怀孕的数字表型。数字表型可以在宏观量表上更加完整地了解怀孕，并更详细地理解结果是连续体而不是孤立的离散事件。众所周知，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。孕产妇肥胖与怀孕的许多并发症有关，包括妊娠糖尿病，前宾夕法尼亚，宏观疾病，剖腹产和死产。缺乏社会支持和社会互动也是一个重要的危险因素，已被证明对怀孕结果产生不利影响。睡眠障碍与健康状况不佳有关，尤其是心血管疾病和炎症反应。此外，短睡眠持续时间与糖尿病和肥胖症的发病率增加有关，并且与死亡率的增加有关。研究表明，经历预先启动前的女性更频繁地报告每天的认知失败并增加情绪功能障碍。随着智能手机无处不在的使用，现在可以收集反映野外怀孕标志的生活经验或数据。随着时间的流逝，收集加速度计和GPS提供了物理活动和总体运动活动的指示。呼叫和短信记录详细的通信事件，并有助于社交互动和社交联系。此外，功率状态，屏幕时间和触摸事件可用于了解潜在的睡眠中断。在怀孕过程中，对这些数据的分析具有更大的意义。使用机器学习和数学模型对野外怀孕的这种纵向观点和分析既提供了一个人的妊娠数字表型，又提供了妊娠的数字表型。通过收集和分析这两种产品，它们可以用来更好地理解结果和导致怀孕成果的事件的连续性。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得支持的审查标准。

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