Implementing and Scaling the STEADI Fall Prevention Algorithm Using a Conversational Relational Agent for Community-Dwelling Older Adults with and without Mild Cognitive Impairment (MCI).

使用对话关系代理为社区居住的患有或不患有轻度认知障碍 (MCI) 的老年人实施和扩展 STEADI 跌倒预防算法。

• 批准号: 10822816
• 负责人: Chantal M Kerssens
• 金额: $ 54.42万
• 依托单位: CARE.COACH CORPORATION
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-26 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10822816
• 关键词: Accident and Emergency department; Accidents; Adherence; Adoption; Age; Age Years; Algorithms; American; Artificial Intelligence; Balance training; British; Caring; Case/Control Studies; Centers for Disease Control and Prevention (U.S.); Cessation of life; Clinical; Clinical Practice Guideline; Clinical Protocols; Cognition; Cognitive; Communities; Complex; Computer Vision Systems; Computer software; Country; Death Rate; Delirium; Dementia; Effectiveness; Elderly; Ensure; Exercise; Fall prevention; Gait; Geriatrics; Health; Health Benefit; Health Professional; Home; Home environment; Hospitalization; Hospitals; Human; Human Resources; Individual; Infrastructure; Injury; Ink; Intervention; Learning; Life; Loneliness; Manuals; Medical; Medical Care Costs; Medicare/Medicaid; Mental Depression; Modeling; Monitor; Outcome; Performance; Personal Satisfaction; Persons; Phase; Play; Populations at Risk; Prevention program; Protocols documentation; Quality of life; Randomized; Randomized, Controlled Trials; Research; Risk; Risk Factors; Risk Reduction; Role; Safety; Small Business Innovation Research Grant; Societies; Tablets; Technology; Testing; Time; Training; Translating; Visit; Work; attentional control; care coordination; care costs; care providers; commercialization; cost; design; digital; digital intervention; disability; efficacy testing; evidence base; exercise program; exercise regimen; fall injury; fall risk; falls; fear of falling; feasibility testing; feature extraction; improved; injury-related death; innovation; mild cognitive impairment; modifiable risk; primary outcome; programs; prospective; prototype; psychosocial; remote monitoring; screening; sensor; standard care; strength training; technology platform; usability; virtual health; Accident and Emergency department; Accidents; Adherence; Adoption; Age; Age Years; Algorithms; American; Artificial Intelligence; Balance training; British; Caring; Case/Control Studies; Centers for Disease Control and Prevention (U.S.); Cessation of life; Clinical; Clinical Practice Guideline; Clinical Protocols; Cognition; Cognitive; Communities; Complex; Computer Vision Systems; Computer software; Country; Death Rate; Delirium; Dementia; Effectiveness; Elderly; Ensure; Exercise; Fall prevention; Gait; Geriatrics; Health; Health Benefit; Health Professional; Home; Home environment; Hospitalization; Hospitals; Human; Human Resources; Individual; Infrastructure; Injury; Ink; Intervention; Learning; Life; Loneliness; Manuals; Medical; Medical Care Costs; Medicare/Medicaid; Mental Depression; Modeling; Monitor; Outcome; Performance; Personal Satisfaction; Persons; Phase; Play; Populations at Risk; Prevention program; Protocols documentation; Quality of life; Randomized; Randomized, Controlled Trials; Research; Risk; Risk Factors; Risk Reduction; Role; Safety; Small Business Innovation Research Grant; Societies; Tablets; Technology; Testing; Time; Training; Translating; Visit; Work; attentional control; care coordination; care costs; care providers; commercialization; cost; design; digital; digital intervention; disability; efficacy testing; evidence base; exercise program; exercise regimen; fall injury; fall risk; falls; fear of falling; feasibility testing; feature extraction; improved; injury-related death; innovation; mild cognitive impairment; modifiable risk; primary outcome; programs; prospective; prototype; psychosocial; remote monitoring; screening; sensor; standard care; strength training; technology platform; usability; virtual health

ABSTRACT Falls are a serious threat to the health and well-being of older Americans, especially those with mild cognitive impairment (MCI) and dementia due to the role cognition plays in gait. Falls are the leading cause of injury- related deaths among Americans ≥ 65 years of age (older adults), and the age-adjusted rate of deaths from falls is increasing. One in three older adults falls each year, and over 3 million are treated in hospitals for fall injuries every year. Those who fall once are two to three times more likely to fall again, and older adults with MCI/dementia fall two to three times more than cognitive healthy older adults. Effective fall prevention could substantially reduce disability, hospitalizations, and loss of independence in older adults, and improve their quality of life. The STEADI (Stopping Elderly Accidents, Deaths & Injuries) Initiative by the Centers for Disease Control and Prevention (CDC) combines fall risk screening, assessments and intervention based on the clinical practice guidelines (2010) by the American and British Geriatrics Societies. As a comprehensive, multifactorial intervention, STEADI can reduce the risk and rate of falling, but is challenging to implement using traditional dissemination models (pen and ink, in-person visits, human adherence monitoring and care coordination) that require extensive infrastructure and human resources to significantly impact the population at risk. The proposed Fast-Track SBIR by care.coach corporation (Millbrae, CA) in partnership with clinical, academic, and community partners, aims to test the feasibility and efficacy of a digital STEADI intervention including an evidence-based exercise program (Otago) to offer older adults with and without MCI strength and balance training in the comfort of their home, and remotely monitor their gait as an early clinical indicator of fall risk and decline. To deliver STEADI remotely, care.coach will adapt its already successful conversational technology platform with virtual health assistant (avatar) to screen community-dwelling older adults for fall risk and modifia- ble risk factors, and to intervene using an effective, personalized exercise regimen and coaching program that learns and adapts over time. The digital intervention is executed with the help of artificial intelligence (AI) and is overseen by a 24x7 team of human staff who manage the avatars remotely, offer companionship, and escalate as needed to care providers. The deliverable is a rigorously tested, scalable digital STEADI intervention opti- mized for ongoing fall risk monitoring at home, customized intervention and exercise, and timely care coordina- tion with health professionals. Upon completion of this work, the program will be ready for national implementa- tion and commercialization via existing and new customers.

抽象的 跌倒是对年长美国人的健康和福祉的严重威胁，尤其是那些有轻度认知的人 障碍（MCI）和痴呆症是由于步态中认知的作用而引起的。跌倒是受伤的主要原因 - 美国人的相关死亡≥65岁（老年人），以及年龄调整的死亡率 瀑布正在增加。每年三分之一的老年人跌倒，秋季在医院接受了超过300万的治疗 每年受伤。那些曾经跌倒的人再次跌倒的可能性高两到三倍，老年人 MCI/痴呆症是认知健康的老年人的两到三倍。有效的预防跌倒可能 大大减少了残疾，住院和老年人的独立性丧失，并改善了他们的 生活质量。疾病中心的稳定（阻止老年人事故，死亡和伤害）倡议 控制与预防（CDC）结合了基于临床的跌倒风险筛查，评估和干预 美国和英国老年医学社会的实践指南（2010年）。作为全面的多因素 干预，Steadi可以降低跌倒的风险和速度，但要使用传统的挑战 传播模型（笔和墨水，面对面的访问，人类的依从性监测和护理协调） 需要广泛的基础设施和人力资源，以显着影响处于危险中的人群。 Care.Coach Corporation（CA Millbrae，CA）提议的快速轨道SBIR与临床，学术， 和社区合作伙伴，旨在测试数字稳定干预的可行性和效率，包括 循证锻炼计划（Otago）为具有MCI力量和平衡的老年人提供 在舒适的家中进行培训，并远程监测他们的战斗，作为跌倒风险的早期临床指标和 衰退。为了远程交付稳定，Care.Coach将改编其已经成功的对话技术 与虚拟健康助理（Avatar）的平台，用于筛选居住在社区的老年人，以降低风险和Modifia- 发生风险因素，并使用有效的个性化锻炼方案和教练计划进行干预， 随着时间的流逝学习和适应。数字干预是在人工智能（AI）的帮助下进行的，并且是 由24x7人类员工团队监督，他们远程管理头像，提供合作伙伴关系并升级 根据需要照顾提供者。可交付的是经过严格测试，可扩展的数字稳定干预措施的选择。 为持续的秋季风险监测，定制干预和锻炼以及及时关心坐标 - 与卫生专业人员在一起。完成这项工作后，该计划将为国家实施做好准备 通过现有和新客户进行商业化和商业化。