Sense4Safety: Supporting socially vulnerable older adults

摘要 对于65岁和65岁的成年人来说，跌倒和跌倒相关伤害是重大的公共卫生问题 年纪更大了。超过三分之一的老年人每年跌倒，10%-20%的跌倒会导致严重的 受伤，如骨折和头部创伤。美国每年的直接医疗费用作为 坠落造成的损失估计超过500亿美元，这一估计不包括间接 残疾、依赖和生活质量下降的代价。该项目以社区为目标 居住在有轻度认知障碍(MCI)的OA患者中，他们是社会上的弱势群体。MCI是领先的 骨性关节炎跌倒的危险因素。大约15%-20%的OA患有MCI，超过60%的OA患有MCI MCI每年都会下降--是那些没有认知障碍的人的两到三倍。社交 居住在住房条件差的低资源社区的弱势OA患者是 有坠落的危险。我们开发了一种以创新技术为支撑的护理驱动 称为Sense4Safe的干预措施1)通过在家实时识别跌倒风险的不断上升 被动传感器监控(包括深度和运动传感器)；2)使用机器学习 通知跌倒风险的个性化警报；以及3)将社会上易受伤害的老年人与 指导他们实施循证个性化计划的护士远程培训 降低跌倒风险。本研究的目的是提炼基于以下内容的Sense4安全干预 低收入OA与MCI、他们的家人或信任的其他人以及 临床医生为了确保被动监测系统能够预测跌倒风险的骨性关节炎 MCI可以生成可操作的、量身定做的信息，这些信息可以被这些 利益相关者团体。

Abstract Falls and fall-related injuries are significant public health issues for adults 65 years of age and older. Over a third of older adults (OA) fall each year and 10-20% of falls result in serious injuries such as fractures and head trauma. The annual direct medical costs in the US as a result of falls are estimated to exceed $50 billion, and this estimate does not include the indirect costs of disability, dependence, and decreased quality of life. This project targets community dwelling OA with mild cognitive impairment (MCI) who are socially vulnerable. MCI is a leading risk factor for falls in OA. Approximately 15%-20% of OA have MCI, and over 60% of OA with MCI fall annually – two to three times the rate of those without cognitive impairment. Socially vulnerable OA living in low-resource neighborhoods with poor housing conditions have twice the risk of falling. We have developed an innovative technology-supported nursing-driven intervention called Sense4Safety to 1) identify escalating risk for falls real-time through in-home passive sensor monitoring (including depth and motion sensors); 2) employ machine learning to inform individualized alerts for fall risk; and 3) link `at risk' socially vulnerable older adults with a nurse tele-coach who will guide them in implementing evidence-based individualized plans to reduce fall-risk. The purpose of this study is to refine the Sense4Safety intervention based on extensive feedback by low income OA with MCI, their family members or trusted others and clinicians in order to ensure that the passive monitoring system to predict fall risk for OA with MCI can generate actionable and tailored information that can be used effectively by these stakeholder groups.