Topic 412: CHAMP-WARE: Continuous Health Monitoring and Predictions using a Wearables-Agnostics Platform for Cancer Patients, Phase I

商用现成(COTS)可穿戴设备，可客观跟踪 生理变量为患者的健康提供了丰富的信息来源 临床医生和肿瘤学研究人员，以促进早期不良事件检测和 后续管理，可以降低医疗成本，提高患者质量 生活的一部分。当前或未来生成的被动的、持续测量的数据流 COTS传感器将允许直接/间接测量癌症进展及其 症状。通过这些工具增加门诊患者和临床医生的参与度 允许在癌症缓解后以及癌症缓解期间更精确地提供癌症护理。 最终，这些被动传感平台的数字生物标志物数据将负担得起 临床医生：1)更客观的治疗反应指标；2)控制和自动报告 症状及其波动；3)监测药物的副作用 试验性或标准护理疗法；以及4)更具生态有效性的临床 终端，都通过增加生理连续性来减轻评估负担 门诊环境中的测量采样和患者环境。此外， 这些数据提供了从大范围衡量基于人口的统计数据的机会 通过目前可用的或正在使用的无数设备对癌症患者进行分类 发展起来的。 不幸的是，尽管有无数的COTS可穿戴设备能够 测量生理变量及其用于远程癌症患者监测或 非临床癌症研究尚未成为主流。有相当大的需求 用于可扩展的信息学工具，允许自动数据聚合、集成和 机器学习/人工智能(AI)/预测分析 跨COTS设备供应商的完全不同的数据集，并可以灵活地添加新的 正在制定的措施。此外，还需要一个中央软件平台 可以获取可穿戴设备或外部设备数据，并统一比较/对比/组合 数据流以了解关于时间的生理学与患者环境：例如 临床医生，这种独特的方法将大大促进帮助癌症患者的能力 评估和临床试验设计。 这项工作旨在克服这些瓶颈，并提供一种工作流程和 用于门诊远程患者监控和在线研究的基础设施 合作推进以人口为基础的研究。通过开发一款软件 系统，由智能手机应用程序、数据库和Web门户组成，可以) 收集大量可穿戴设备的原始传感器数据并将其标准化，b)执行 智能多传感器数据分析，实时提供临床相关结果 时间，c)将这些数据存储在公共存储库中，以及d)提供在线界面以供查看 并分析数据，拟议的努力将显著促进临床外癌症 研究和病人监测。

Commercially available off-the-shelves (COTS) wearables that objectively track physiological variables offer a rich source of information about a patient’s health to clinicians and oncology researchers, to facilitate early adverse-event detection and subsequent management, which can decrease healthcare costs and improve patient quality of life. The passive, continuously measured data streams generated by current or future COTS sensors will allow direct/indirect measures of cancer progression and its symptoms. Increased out-of-clinic patient and clinician engagement via these tools will allow more precise delivery of cancer care after as well as during cancer remission. Ultimately, these passive sensing platforms’ data for digital biomarkers will afford clinicians: 1) more objective metrics of response to therapeutics; 2) control and autoreporting of symptoms and their fluctuations; 3) monitoring of side-effects of experimental or standard of care therapies; and 4) more ecologically valid clinical endpoints, all decreasing assessment burden via increased continuity of physiological measurement sampling and patient context in the ambulatory setting. Furthermore, such data present an opportunity to measure population-based statistics from large cohorts of cancer patients by way of the myriad of devices currently available or being developed. Unfortunately, despite the availability of a myriad of COTS wearables capable of measuring physiological variables, their use for remote cancer patient monitoring or for out-of-clinic cancer research is yet to become mainstream. There is a considerable need for scalable informatics tools that allow automated data aggregation, integration and machine learning/artificial intelligence (AI)/predictive analytics that can pull from disparate data sets across COTS device vendors and have the flexibility to add new measures as they are developed. Furthermore, a central software platform is needed that could obtain wearable or external device data and uniformly compare/contrast/couple data streams to understand physiology versus patient context with respect to time: such a capability will substantially advance this unique approach to aid cancer patients, clinician assessment and clinical trial design. This work seeks to overcome these bottlenecks and provide a workflow and an infrastructure for out-of-clinic remote patient monitoring and online research collaboration for advancing population-based research. By developing a software system, comprised of a smartphone app, database, and a Web portal, which can a) collect and standardize raw sensor data from multitude of wearables, b) perform intelligent multi-sensor data analytics to provide clinically relevant outcomes in real time, c) store these data in a common repository, and d) provide online interfaces to view and analyze data, the proposed effort will significantly advance out-of-clinic cancer research and patient monitoring.