SCH: Wearables for Health and Disease Knowledge (W4H)

SCH：健康和疾病知识可穿戴设备 (W4H)

基本信息

批准号：
10436398
负责人：
Cyrus Shahabi
金额：
$ 30万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-14 至 2025-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10436398
关键词：
Age Ambulatory Monitoring Apache Apple watch Area Arrhythmia Atrial Fibrillation Behavioral Big Data COVID-19 pandemic Calories Cancer Cluster Cancer Patient Car Phone Cellular Phone Characteristics Clinic Clinical Clinical Data Clinical Trials Clinics and Hospitals Collaborations Communities Community Health Computer software Contact Tracing Controlled Environment Custom Data Detection Devices Diagnosis Diagnostic Disease Drug Delivery Systems Early identification Effectiveness Electrocardiogram Emergency response Encapsulated Ensure Environment Environmental Health Epilepsy Evaluation Evolution Expenditure Goals Growth Health Health Professional Heart Diseases Home Home Care Services Hospitals Hour Impairment Individual Infant Intervention Knowledge Leg Life Location Malignant Neoplasms Measurable Measurement Medical Mind Modeling Monitor Movement Names Outcome Parkinson Disease Patient Care Patient Monitoring Patients Performance Performance Status Persons Phase Play Quality of life Recording of previous events Research Research Personnel Rest Risk Role SARS-CoV-2 transmission Scientist Series Serious Adverse Event Sleep Disorders Source Stream Stroke Symptoms Technology Testing Time Training United States National Institutes of Health Variant Visit Work base clinical care cluster computing data management data streams deep learning design distributed data distributed memory falls fitbit fitness glucose monitor health application health care model health data hospital analysis improved improved outcome indexing infancy infant monitoring insight mHealth mobile sensor open source tool search engine sensor social social media spatiotemporal standard of care time use trend wearable device wearable sensor technology

项目摘要

Project Description 1 Introduction More than any other phenomena in recent history, the COVID-19 pandemic has challenged how we approach patient-care due to the huge burden it has placed on hospitals, clinics, and health professionals. The health community has responded to this trend with research and technology leveraging data that goes beyond what is customarily thought of as “health data”, such as commu- nity and contextual data, social media, traffic, and mobility data. For example, Nsoesie et al.[84] analyzed hospital traffic and search engine data in Wuhan to infer early disease activity in Fall 2019. These new efforts, including our own work in utilizing mobility data to forecast COVID- 19’s transmission risk [94], uses what this NSF call-for-proposal refers to as “non-traditional health data”. In this proposal, we focus on one specific type of non-traditional health data, wearable data, which are also fast becoming an important source of health and disease data as they inform on a variety of personal, behavioral, social, contextual, and environmental health-relevant factors. Wearables have been primarily used for activity tracking [96, 15, 20, 80] and gained popularity with fitness applications; however, more recently, these devices have been used in an increasing number of health applications, including health monitoring, clinical-care, remote clinical-trials, drug delivery, and disease characterization to name a few. In fact, wearables have been found useful in a num- ber of applications and diseases (e.g., Parkinson’s disease, epilepsy and stroke [57], sleep disor- ders [12], cardiac disorders [90, 63] and cancer [75]). This trend is accelerating with the COVID-19 epidemic, e.g., smartphones have been proposed to track symptoms [64], monitor effectiveness of non-pharmaceutical interventions, assess potential spread, and support contact tracing [45]. Wearable measurements differ from traditional clinical measurements. When a patient visits a clinic, vitals and lab tests are collected in a “controlled” environment in a short duration of time using multiple devices. We define this monitoring in the controlled environment as Snapshot In-Clinic monitoring, abbreviated as SIC. Meanwhile, the recent growth and accessibility of the wearable devices such as smartphones and watches [97] with embedded activity and mobile sensors [114] enables the continuous monitoring of patients’ vital signs and other health indicators over a long duration of time. Patient monitoring using wearable devices typically happens in an “uncontrolled” setup at home or at work in a non-intrusive fashion with only a few sensors. This trend has also been encapsulated by the NIH mHealth’s initiatives, resulting in the evolution of new healthcare models such as “home healthcare” [9, 40] and “minute clinic” [125], which goes hand in hand with both ubiquitous sensors in smartphones and custom sensors like glucose monitors [62]. We define this monitoring in the uncontrolled environment as Longitudinal In-Field monitoring, abbreviated as LIFE. Clearly these are wordplay, i.e., SIC is for “sick” capturing patients’ state of mind when they visit a clinic/hospital vs. LIFE for when patients live their normal “life” at home and at work. LIFE monitoring makes up for greater than 99% of patients’ time, enabling outpatient monitoring of the effects of disease and its therapy on patient performance and quality of life. In fact, our preliminary data show that in some cases, such as assessment of performance status in cancer patients, LIFE data outperform in-office SIC assessments [82]. SIC monitoring is the current standard of care and is driven by improving outcomes in measurable Page 72

项目描述 1 简介与近代史上的任何其他现象相比，新冠肺炎 (COVID-19) 大流行对如何应对疫情提出了更大的挑战。我们对病人进行护理是因为它给医院、诊所和健康部门带来了巨大的负担卫生界通过研究和技术来应对这一趋势。利用超出通常认为的“健康数据”范围的数据，例如通信数据以及上下文数据、社交媒体、流量和移动数据，例如 Nsoesie 等人[84]。分析武汉的医院流量和搜索引擎数据，以推断秋季的早期疾病活动 2019年。这些新的努力，包括我们自己在利用移动数据来预测新冠病毒方面的工作—— 19 的传播风险 [94]，使用 NSF 提案征集所指的“非传统健康数据”。在本提案中，我们重点关注一种特定类型的非传统健康数据，即可穿戴数据，也迅速成为健康和疾病数据的重要来源，因为它们提供了各种信息个人、行为、社会、背景和环境健康相关因素。主要用于活动跟踪 [96,15,20,80]，并在健身领域广受欢迎然而，最近，这些设备已被用于越来越多的领域。健康应用，包括健康监测、临床护理、远程临床试验、药物输送、事实上，可穿戴设备在很多方面都非常有用。应用和疾病的程度（例如帕金森病、癫痫和中风[57]、睡眠障碍） ders [12]、心脏疾病 [90, 63] 和癌症 [75]），随着 COVID-19 的出现，这一趋势正在加速。流行病，例如，有人建议使用智能手机来跟踪症状[64]、监测有效性非药物干预措施、评估潜在传播并支持接触者追踪[45]。可穿戴测量不同于传统的临床测量。使用以下方法在短时间内在“受控”环境中收集临床、生命体征和实验室测试我们将受控环境中的这种监控定义为诊所内快照。监控，缩写为 SIC 同时，可穿戴设备的最新增长和可访问性。具有嵌入式活动和移动传感器 [114] 的智能手机和手表 [97] 等设备能够长期持续监测患者的生命体征和其他健康指标使用可穿戴设备进行患者监测通常发生在“不受控制”的情况下。这种趋势也已在家里或工作场所以非侵入式方式仅使用几个传感器进行设置。 NIH mHealth 的举措概括了这一点，从而推动了新医疗保健的发展 “家庭医疗”[9, 40]和“分钟诊所”[125]等模式，与我们定义了智能手机中无处不在的传感器和血糖监测仪等定制传感器[62]。这种在不受控制的环境中进行的监测称为纵向现场监测，缩写为显然，这些都是文字游戏，即 SIC 是“生病”时捕捉患者的心理状态。他们去诊所/医院与患者在家和工作中过着正常“生活”时的生活。 LIFE 监测占据了患者 99% 以上的时间，可实现门诊监测事实上，我们了解疾病及其治疗对患者表现和生活质量的影响。初步数据显示，在某些情况下，例如癌症表现状态评估患者，LIFE 数据优于办公室 SIC 评估 [82]。 SIC 监测是当前的护理标准，其驱动力是改善可衡量的结果第72页