Sense4Safety: Supporting socially vulnerable older adults

Sense4Safety：支持社会弱势老年人

• 批准号: 10093338
• 负责人: George Demiris
• 金额: $ 8.1万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10093338
• 关键词: Adult; Adverse effects; Age-Years; Attitude; Caregiver Burden; Caring; Cognitive; Communities; Computer software; Controlled Environment; Country; Craniocerebral Trauma; Data; Data Collection; Dementia; Dependence; Detection; Deterioration; Disabled Persons; Discipline of Nursing; Early Diagnosis; Elderly; Ensure; Environment; Equilibrium; Event; Face; Facilities and Administrative Costs; Fall prevention; Family health status; Family member; Feedback; Fracture; Funding Opportunities; Gait; Gait speed; Health Personnel; Healthcare; Home environment; Housing; Human Activities; Impaired cognition; Income; Infrastructure; Injury; Intervention; Link; Low income; Machine Learning; Medical; Medical Care Costs; Modeling; Monitor; Morbidity - disease rate; Neighborhoods; Nurses; Pattern; Perception; Performance; Prevention; Prevention strategy; Preventive Intervention; Primary Prevention; Privacy; Protocols documentation; Public Health; Quality of life; Research; Resources; Risk; Risk Assessment; Risk Factors; Role; Safety; Services; Signal Transduction; Social support; System; Time; Trust; base; cognitive ability; dashboard; data visualization; disability; evidence base; experience; fall risk; falls; functional decline; functional status; health care availability; health care service utilization; improved functioning; innovative technologies; mild cognitive impairment; motion sensor; physical conditioning; preference; prevent; research study; sensor; sensor technology; social vulnerability

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.

摘要 福尔斯和跌倒相关伤害是65岁以上成年人的重要公共卫生问题， 老了每年超过三分之一的老年人（OA）跌倒，10-20%的福尔斯跌倒导致严重的 骨折和头部创伤等伤害。美国每年的直接医疗费用 福尔斯的结果估计超过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。该项目针对社区 患有轻度认知障碍（MCI）的居住OA患者，他们是社会弱势群体。MCI是领先的 OA患者福尔斯跌倒的风险因素。大约15%-20%的OA患有MCI，超过60%的OA患有MCI。 MCI每年都会下降，是没有认知障碍的人的两到三倍。社会 生活在住房条件差的低资源社区的脆弱OA有两倍于 跌倒的风险。我们开发了一个创新的技术支持的护理驱动 一种名为Sense 4Safety的干预措施，用于1）通过家庭实时识别不断升级的福尔斯跌倒风险 被动传感器监测（包括深度和运动传感器）; 2）采用机器学习， 告知跌倒风险的个性化警报;以及3）将“处于风险中”的社会弱势老年人与 护士远程教练将指导他们实施基于证据的个性化计划， 降低跌倒风险。本研究的目的是根据以下因素完善Sense 4Safety干预措施： 低收入OA与MCI、他们的家庭成员或信任的其他人的广泛反馈， 临床医生，以确保被动监测系统预测OA的跌倒风险， MCI可以生成可操作和定制的信息，这些信息可以有效地用于 利益攸关方群体。