A clinician-in-the-loop smart home to support health monitoring and intervention for chronic conditions: Supplement to focus on Alzheimer's and/or other dementias

支持健康监测和慢性病干预的临床医生智能家居：专注于阿尔茨海默氏症和/或其他痴呆症的补充

• 批准号: 10086759
• 负责人: Diane Joyce Cook
• 金额: $ 36.67万
• 依托单位: WASHINGTON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10086759
• 关键词: Address; Adherence; Adoption; Adult; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Awareness; Behavior; Behavior Therapy; Behavior monitoring; Behavioral; Caregiver Burden; Caregiver support; Caregivers; Caring; Chronic; Cognitive; Complex; Data; Data Analyses; Data Analytics; Dementia; Detection; Development; Early Intervention; Elderly; Emotional; Environment; Evaluation; Event; Exhibits; Family; Financial Hardship; Generations; Goals; Health; Health Personnel; Health Status; Healthcare; Home Nursing Care; Home environment; Individual; Intelligence; Intervention; Knowledge; Label; Learning; Life; Machine Learning; Methods; Monitor; Outcome; Parents; Participant; Patients; Personal Satisfaction; Persons; Population; Public Health; Quality of Care; Quality of life; Research; Sampling; Self Management; Societies; Subgroup; System; Techniques; Technology; Time; Training; Work; aging in place; aging population; algorithm training; base; behavior change; brain behavior; brain health; care costs; design; functional independence; health assessment; improved; innovation; intervention effect; learning algorithm; learning strategy; machine learning algorithm; novel; personalized intervention; physical conditioning; research clinical testing; rural area; sensor; smart home; smart watch; study population; technology validation; therapy design

PROJECT SUMMARY/ABSTRACT The world's population is aging and the increasing number of older adults with Alzheimer's disease and related dementias (ADRDs) is a challenge our society must address. While the idea of smart environments is now a reality, there remain gaps in our knowledge about how to scale smart homes technologies for use in complex settings and to use machine learning and activity learning technologies to design automated health assessment and intervention strategies. The primary objective of the parent study is to design a “clinician in the loop” smart home to empower individuals in managing their chronic health conditions by automating health monitoring, assessment, and evaluation of intervention impact. This supplement extends our design to monitor, assess, and intervene for individuals with ADRDs and their caregivers. Building on our prior work, the approach will be to generate analytics describing an individual's behavior routine using smart homes, smartwatches, and activity learning (Aim 1). Our trained clinicians will use the analytics to train algorithms for health assessment (Aim 2). In addition, we will introduce brain health interventions to extend brain health and objectively capture intervention both adherence and caregiver support (Aim 3). Clinician guidance is used to train machine learning algorithms to automatically recognize health events (Aim 4). Given the unique challenges that will arise when we include individuals with ADRDs, we also introduce novel methods to track multiple residents in smart environments (Aim 5) and compare behaviors between individuals with ADRDs and the original sample of older adults with chronic health conditions (Aim 6). These contributions are significant because they can extend the health self-management of our aging society through proactive health care and real-time intervention, and reduce the emotional and financial burden for caregivers and society. Given nursing home care costs, the impact of family-based care, and the importance that people place on staying at home, technologies that increase functional independence and thus support aging in place while improving quality of life for both individuals and their caregivers are of significant value to both individuals and society.

项目总结/摘要 世界人口正在老龄化，越来越多的老年人患有阿尔茨海默病， 相关性痴呆（ADRDs）是我们社会必须应对的挑战。虽然智能环境的概念是 现在成为现实，我们对如何扩展智能家居技术的知识仍然存在差距， 复杂的设置，并使用机器学习和活动学习技术来设计自动化的健康 评估和干预策略。母研究的主要目的是设计一个“临床医生， 循环”智能家居，使个人在管理他们的慢性健康状况，通过自动化 健康监测、评估和干预效果评价。此补充将我们的设计扩展到 监测、评估和干预ADRD患者及其护理人员。在我们先前工作的基础上， 方法将是使用智能家居生成描述个人行为例程的分析， 智能手表和活动学习（目标1）。我们训练有素的临床医生将使用分析来训练算法， 健康评估（目标2）。此外，我们将引入大脑健康干预措施，以延长大脑健康， 客观地捕获干预依从性和护理人员支持（目标3）。临床医生指导用于 训练机器学习算法以自动识别健康事件（目标4）。鉴于独特的 当我们纳入ADRD患者时会出现的挑战，我们还引入了新的方法来跟踪 智能环境中的多个居民（目标5），并比较ADRD个体之间的行为 以及患有慢性健康状况的老年人的原始样本（目标6）。这些捐款 重要的是，他们可以通过积极的健康， 护理和实时干预，并减轻护理人员和社会的情感和经济负担。 考虑到疗养院护理的成本，家庭护理的影响，以及人们对 呆在家里，增加功能独立性的技术，从而支持原地老化， 改善个人及其照顾者的生活质量对两个人都有重要价值 和社会