SCH: Wearables for Health and Disease Knowledge (W4H)

SCH：健康和疾病知识可穿戴设备 (W4H)

• 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
• 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

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引言 COVID-19大流行比近代历史上任何其他现象都更挑战了 由于病人护理给医院、诊所和卫生部门带来了巨大负担， 专业人士卫生界已经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”的数据，例如通信数据， 自然和情境数据、社交媒体、流量和移动性数据。例如，Nsoesie et al. [八十四] 分析了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.这些新的努力，包括我们自己利用流动数据预测COVID-19的工作， 19的传播风险[94]，使用了NSF的呼吁提案所指的“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，它 也正在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。穿戴设备 已主要用于活动跟踪[96，15，20，80]，并获得了与健身普及 然而，最近，这些设备已经被用于越来越多的应用中。 健康应用，包括健康监测、临床护理、远程临床试验、药物递送， 和疾病特征等等。事实上，可穿戴设备在许多领域都很有用， 应用和疾病的BER（例如，帕金森氏病、癫痫和中风[57]、睡眠障碍- [12]，心脏疾病[90，63]和癌症[75]）。随着COVID-19的爆发，这一趋势正在加速 流行病，例如，智能手机已被提议用于跟踪症状[64]，监测有效性 非药物干预，评估潜在的传播，并支持接触者追踪[45]。 可穿戴测量不同于传统的临床测量。当一个病人去看一个 诊所，生命体征和实验室测试是在一个“受控”的环境中在短时间内收集使用 多个设备。我们将受控环境中的这种监测定义为诊所内快照 监测，简称SIC。与此同时，可穿戴设备的最近增长和可及性 智能手机和手表等设备[97]，内置活动和移动的传感器[114] 能够长期持续监测患者的生命体征和其他健康指标， 持续时间。使用可穿戴设备的患者监测通常发生在“不受控制”的环境中。 只需几个传感器即可以非侵入式方式在家中或工作中进行设置。这股风潮也 被NIH mHealth的倡议所封装，导致了新医疗保健的发展 诸如“家庭医疗保健”[9，40]和“分钟诊所”[125]等模式，与 智能手机中无处不在的传感器和葡萄糖监测仪等定制传感器[62]。我们定义 这种在非受控环境中的监测称为纵向现场监测，缩写为 作为生命。显然，这些都是文字游戏，即，SIC是用于“生病”捕捉患者的心理状态， 当病人在家里和工作中过着正常的“生活”时，他们访问诊所/医院与LIFE。 LIFE监测占患者时间的99%以上，实现门诊监测 疾病及其治疗对患者表现和生活质量的影响。其实我们 初步数据显示，在某些情况下，例如评估癌症患者的体能状况， 患者，LIFE数据优于门诊SIC评估[82]。 SIC监测是当前的护理标准，其驱动因素是改善可衡量的 第72页