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简介 与最近历史上的任何其他现象相比，Covid-19的大流行都挑战了如何 由于它在医院，诊所和健康状况上放置了巨大的烧伤，我们进行了患者护理 专业人士。卫生社区对研究和技术的这一趋势做出了回应 利用超出通常被认为是“健康数据”的数据，例如 NITH和上下文数据，社交媒体，流量和移动性数据。例如，Nsoesie等人[84] 分析了武汉的医院交通和搜索引擎数据，以推断秋季的早期疾病活动 2019年。这些新努力，包括我们自己利用移动性数据来预测Covid-的工作 19的传输风险[94]，使用此NSF呼叫呼叫所指的内容是“非传统健康 数据”。 在此提案中，我们专注于一种特定类型的非传统健康数据，可穿戴数据，该数据 当它们告知各种各样的健康和疾病数据的重要来源 个人，行为，社会，上下文和环境健康与与之相关的因素。可穿戴设备 主要用于活动跟踪[96、15、20、80]，并随着健身而越来越受欢迎 申请；但是，最近，这些设备已在越来越多的 健康应用程序，包括健康监测，临床护理，远程临床审查，药物输送， 和疾病的表征仅举几例。实际上，已发现可穿戴物在数字中有用 应用和疾病的BER（例如，帕金森氏病，癫痫和中风[57]，睡眠迷惑 - DERS [12]，心脏疾病[90，63]和癌症[75]）。这种趋势正在加速与19号 流行病，例如，已经提出了智能手机来跟踪符号[64]，监视有效性 非药物干预措施，评估潜力扩散和支持接触跟踪[45]。 可穿戴测量与传统临床测量不同。当患者拜访 使用诊所，生命值和实验室测试在短时间内在“受控”环境中收集 多个设备。我们将这种监视在受控环境中定义为快照 监测，缩写为SIC。同时，可穿戴的最近的增长和可及性 带有嵌入式活动和移动传感器的智能手机和手表[97]等设备[114] 可以长期对患者的生命体征和其他健康指标进行连续监测 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”中 在家中或以非侵入性方式进行设置，只有几个传感器。这种趋势也有 由NIH MHealth的举措封装，导致新医疗保健的发展 诸如“家庭医疗保健” [9、40]和“ Minute Clinic” [125]之类的模型，与 智能手机和葡萄糖监测器等自定义传感器的无处不在传感器[62]。我们定义 在不受控制的环境中进行的监视，如纵向的场地监测，缩写 作为生活。显然，这些是单词玩法，即SIC是针对“病态”捕获患者心态的 他们访问诊所/医院与患者在家中正常的“生活”和工作时的生活。 生活监测占患者时间的99％以上，以实现门诊监测 疾病及其治疗对患者表现和生活质量的影响。实际上，我们的 初步数据表明，在某些情况下，例如评估癌症的绩效状况 患者，生活数据表现优于办公室SIC评估[82]。 SIC监测是当前的护理标准，是通过改善可测量的结果来驱动的 第72页