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

项目摘要/摘要 世界人口正在老龄化，患有阿尔茨海默病的老年人和 相关痴呆症(ADRDS)是我们社会必须应对的挑战。虽然智能环境的想法是 现在，在如何扩展智能家居技术以用于 并使用机器学习和活动学习技术来设计自动化健康 评估和干预策略。家长研究的主要目标是设计一位“临床医生 Loop智能家居通过自动化使个人能够管理他们的慢性健康状况 健康监测、评估和干预影响的评估。本增刊将我们的设计扩展到 对ADRDS患者及其照顾者进行监测、评估和干预。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常规的分析， 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培训算法 健康评估(目标2)。此外，我们将推出大脑健康干预措施，以延长大脑健康和 客观地采取干预措施，包括坚持和照顾者支持(目标3)。临床医生的指导用于 训练机器学习算法以自动识别健康事件(目标4)。鉴于独一无二的 当我们包括患有ADRD的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)，并比较患有ADRD的个人之间的行为 和患有慢性病的老年人的原始样本(目标6)。这些贡献是 意义重大，因为它们可以通过积极主动的健康扩展我们老龄化社会的健康自我管理 护理和实时干预，并减轻照顾者和社会的情感和经济负担。 考虑到疗养院护理成本、基于家庭的护理的影响以及人们对 呆在家里，增加功能独立性的技术，从而支持就地老化 提高个人及其照顾者的生活质量对两个人都有重要的价值 和社会。

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.