Automated Contact Tracing System for Rapid High-Accuracy Assessment to Management Dementia Patients and Staff Exposed to Infectious Diseases in Long Term Care Facilities

自动接触者追踪系统，用于对长期护理机构中暴露于传染病的痴呆症患者和工作人员进行快速高精度评估

• 批准号: 10254018
• 负责人: Suneel Ismail Sheikh
• 金额: $ 44.91万
• 依托单位: ASTER LABS, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10254018
• 关键词: 2019-nCoV; Address; Administrator; Affect; Age; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Back; Behavior; COVID-19; COVID-19 pandemic; Car Phone; Caregivers; Caring; Cellular Phone; Communicable Diseases; Communication; Communities; Complex; Computer software; Contact Tracing; Data; Data Storage and Retrieval; Dementia; Devices; Diagnosis; Disease; Disease Management; Disease Outbreaks; Elderly; Electronics; Engineering; Environment; Epidemiology; Evaluation; Event; Exhibits; Exposure to; Florida; Focus Groups; Health; Health care facility; Human; Human Resources; Individual; Infection Control; Infrastructure; Intelligence; Interview; Investments; Laboratories; Lead; Life; Light; Location; Logistics; Long-Term Care; Manuals; Measures; Methods; Modification; Motion; Movement; Nursing Homes; Outcome; Patients; Performance; Persons; Phase; Population; Predisposition; Prevention; Process; Production; Recording of previous events; Reporting; Research; Research Personnel; Research Training; Resources; Retrieval; Risk; Risk Assessment; Route; Safety; Secure; Series; Shoes; Small Business Innovation Research Grant; Specific qualifier value; Symptoms; System; Technology; Testing; Time; Tracer; Training; United States; Universities; Virus; Work; aging in place; base; community living; community setting; comparative; contagion; cost; data access; data management; design; distrust; efficacy evaluation; efficacy study; experience; experimental study; functional independence; health care settings; healthcare-associated infections; high risk; human study; memory care; operation; patient home care; phase 1 testing; privacy protection; programs; prototype; response; sensor; success; system architecture; tool; wearable device; web app; web platform

Project Abstract In this Fast-Track SBIR project, ASTER Labs will address an urgent, immediate, need for accessible and accurate contact tracing to aid management of infectious disease outbreaks in long-term care facilities of patients with Alzheimer’s disease and related dementias. An estimated 5.8 million Americans in 2020 live with Alzheimer’s dementia and nearly half of nursing home and other long-term care residents have been diagnosed with a form of dementia. Challenges in infectious disease management in these care settings are increased by frequent interactions between patients, caregivers, staff, and visitors, as well as higher risk from contagion due to age and underlying health conditions. The COVID-19 pandemic has only exacerbated the issue due to the susceptibility of the elderly to the virus. Contact tracing to identify, test, and isolate those who may have been exposed to an infected person has traditionally been a manual, labor-intensive process. Many care facilities lack the financial, human, and logistical resources to track sufficient detail to retrace movements. For dementia patients in particular, limited recall of recent events and interactions, inability to respond to tracing questions, and potential distrust of tracers risk inaccurate data. Investment in human resources to perform contact tracing is costly, and ad hoc tracing can limit effective outcomes. Existing methods to automate contact tracing have largely been smartphone software applications, widely criticized for lack of privacy protection, and in long-term care settings, guarantees on mobile phone use are limited. The few available solutions for care facilities rely on non-discreet wearable devices that, due to unfamiliarity, may be unacceptable to dementia patients, and often require large infrastructure additions with limitations on range. Therefore, there exists a significant unmet need to support care facilities by providing an unobtrusive, on- demand, accurate, and automated contact tracing solution that works both indoors and outdoors with little to no infrastructure requirements, and that addresses the special needs of dementia patients. ASTER Labs’ proposed Activtrace system leverages intelligent processing of WiFi, GPS, cellular, and inertial sensor data from a small hardware suite concealed in a shoe insole, unnoticeable to the wearer, that achieves high- precision location and duration of close-contact encounters in complex indoor and outdoor environments. In Phase I, the prototype system will be assembled, and feasibility will be demonstrated by determining the positional and temporal accuracy of the device in timed motion experiments, and through a focus group study with contact tracers and caregivers in senior care settings. Phase I testing will provide the success criteria for the start of the Phase II program. In Phase II, the pre-production insoles and comprehensive software application will be assembled, and the integrated system evaluated in a study with research actors wearing the insoles, along with confederate targets. Comparisons of results done manually by trained contact tracers and the automated Activtrace solution in multiple planned scenarios will establish the system’s efficacy.

项目摘要 在这个快速通道SBIR项目中，ASTER实验室将解决一个紧急的，直接的，对可访问和 准确的接触者追踪，以帮助管理长期护理设施中的传染病爆发， 阿尔茨海默病和相关痴呆症患者。据估计，到2020年， 老年痴呆症和近一半的养老院和其他长期护理居民已被 被诊断为痴呆症这些护理环境中传染病管理的挑战是 患者、护理人员、工作人员和访客之间的频繁互动以及 由于年龄和潜在的健康状况而引起的传染。COVID-19疫情只会加剧 由于老年人对病毒的易感性，接触者追踪，以识别、测试和隔离那些 传统上，对可能接触过受感染者的人进行检查是一个手工劳动密集型过程。许多 护理设施缺乏财政、人力和后勤资源，无法追踪足够的细节，以追溯人员流动。 特别是对于痴呆症患者，对最近事件和互动的回忆有限，无法对 追踪问题以及对追踪者的潜在不信任，可能会导致数据不准确。人力资源投资， 执行接触者追踪成本很高，并且临时追踪可能会限制有效的结果。的现有方法 自动化联系人追踪在很大程度上是智能手机软件应用程序，由于缺乏 隐私保护，以及在长期护理环境中，对移动的电话使用的保证有限。为数不多 用于护理设施的可用解决方案依赖于非谨慎的可穿戴设备，由于不熟悉， 这对于痴呆症患者来说是不可接受的，并且通常需要具有范围限制的大型基础设施添加。 因此，通过提供一个不显眼的、在... 一种需求、准确和自动化的接触者追踪解决方案，可在室内和室外使用， 基础设施的要求，并解决痴呆症患者的特殊需要。ASTER实验室 建议的Activtrace系统利用WiFi、GPS、蜂窝和惯性传感器数据的智能处理 从一个隐藏在鞋垫里的小硬件套件，穿着者不注意，实现了高- 在复杂的室内和室外环境中精确定位和持续近距离接触。在 第一阶段，将组装原型系统，并通过确定 定时运动实验中设备的位置和时间精度，并通过焦点小组研究 与接触者追踪器和护理人员在高级护理设置。第一阶段测试将提供成功的标准， 第二阶段计划的开始。在第二阶段，预生产鞋垫和综合软件 应用程序将被组装，并在一项研究中与穿着 鞋垫，沿着同盟国的目标。由经过培训的接触者追踪者手动完成的结果比较， 在多个计划场景中的自动化Activtrace解决方案将建立系统的功效。