Digital Phenotyping and Cardiovascular Health

数字表型与心血管健康

基本信息

批准号：
10224795
负责人：
Raina Merchant
金额：
$ 78.48万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10224795
关键词：
Affect American American Heart Association Area Attention Behavior Behavioral Blood Pressure Cardiovascular Diseases Cardiovascular system Cellular Phone Cholesterol Clinical Data Code Collection Communication Consent Coronary heart disease Data Data Analyses Data Sources Databases Development Devices Diabetes Mellitus Diagnosis Disease Economic Burden Ethics Exercise Feedback Future Genes Genetic Genome Goals Grant Habits Health Health behavior Healthcare Healthy People 2020 Heart Heart Diseases Human Human Genome Individual Information Dissemination Investigation Laboratories Lead Learning Life Style Logistics Marketing Measures Mediating Methodology Modeling Morbidity - disease rate National Heart, Lung, and Blood Institute Nucleotides Patients Persons Phenotype Physical activity Physicians Population Prevention Primary Prevention Privacy Privatization Process Recording of previous events Research Retrospective cohort Risk Risk Estimate Risk Factors Role Shapes Signal Transduction Smoking Status Social Interaction Source Structure Technology Uncertainty United States Dept. of Health and Human Services United States National Institutes of Health Weight Work cardiovascular health cardiovascular risk factor care costs cohort complex data cost data mining data sharing digital digital media disease phenotype food consumption frontier good diet handheld mobile device health care service utilization health record heart disease risk improved innovation insight interest mortality novel strategies patient oriented personalized medicine phenotypic data precision medicine prediction algorithm predictive modeling response sensor smoking cessation social social determinants social media

项目摘要

Digital data from social media, online searches, and smartphones can reveal a detailed narrative about an individual's day-to-day activities. Information about lifestyle and health behaviors (e.g. exercise habits, food consumption, smoking status) are often revealed with significant detail through these electronic platforms. Of importance, many commonly shared health behaviors may be associated with cardiovascular disease, treatment, and management. Digital phenotypes derived from these electronically mediated data sources can shape our assessment of human illness and have substantial value beyond our traditional approaches to characterizing a disease phenotype (e.g. physical exam, laboratory values), and ultimately expand our ability to identify and diagnose health conditions and predict healthcare utilization. Central to this proposal is the recognition that person-to-person communication and online activities that were previously private are now observable. It is the observability of these new communication channels that provides both innovation and promise to this area of inquiry. Our first aim will entail consenting patients to share access to their digital data (e.g. social, search, and mobile data) and merge this information with validated health record data in a research database. We will then extensively process the digital data so that it is in an interpretable format that can be incorporated in traditional predictive models. Aim 2 will focus on assessing the incremental benefit of adding digital data to the Framingham risk score to evaluate the contribution of digital data for predicting cardiovascular risk. In the future, this data could inform patients about their personalized risk and ways to concretely change that risk. The digital platforms used to post or share data could also be used to directly provide feedback to patients on the medium they use and in direct response to their stated inputs. The third aim will focus on incorporating digital data in models to predict cost of care. This approach offers promise for better understanding the factors contributing to healthcare utilization, which correlate with morbidity, mortality, and the economic burden of cardiovascular disease. Through this project we seek to learn new insights about collecting and analyzing digital data while being attentive to issues of ethics and privacy that may be associated with these data. We will incorporate digital data in models to predict important targets like coronary heart disease risk and healthcare use. Overall, the areas of focus for this grant represent new frontiers in precision medicine and digital phenotyping for cardiovascular health.

来自社交媒体、在线搜索和智能手机的数字数据可以揭示有关某个事件的详细叙述。个人的日常活动。有关生活方式和健康行为的信息（例如运动习惯、食物消费、吸烟状况）通常通过这些电子平台提供重要的细节。的重要的是，许多常见的健康行为可能与心血管疾病有关，治疗、管理。从这些电子介导的数据源衍生的数字表型可以塑造我们对人类疾病的评估，并具有超越我们传统方法的巨大价值描述疾病表型（例如体检、实验室值），并最终扩展我们的能力识别和诊断健康状况并预测医疗保健利用率。该提案的核心是认识到以前私人的人与人之间的交流和在线活动现在已经成为私人的可观察到的。正是这些新的沟通渠道的可观察性提供了创新和承诺对此领域的调查。我们的首要目标是同意患者共享其数字数据的访问权限（例如社交、搜索和移动数据）并将这些信息与经过验证的健康记录数据合并到研究数据库。然后，我们将对数字数据进行广泛处理，使其成为可解释的格式可以合并到传统的预测模型中。目标 2 将侧重于评估增量效益将数字数据添加到弗雷明汉风险评分中，以评估数字数据对预测的贡献心血管风险。将来，这些数据可以告知患者他们的个性化风险以及治疗方法具体改变这种风险。用于发布或共享数据的数字平台也可用于直接向患者提供有关他们使用的媒介的反馈，并直接响应他们所说的输入。第三个目标将侧重于将数字数据纳入模型中以预测护理成本。这种方法提供了有望更好地了解影响医疗保健利用的因素，这些因素与心血管疾病的发病率、死亡率和经济负担。通过这个项目我们寻求学习关于收集和分析数字数据同时关注道德和隐私问题的新见解可能与这些数据相关。我们将把数字数据纳入模型中来预测重要目标例如冠心病风险和医疗保健使用。总体而言，这笔赠款的重点领域代表了新的心血管健康精准医学和数字表型分析的前沿。