Utilizing Technology and AI Approaches to Facilitate Independence and Resilience in Older Adults

利用技术和人工智能方法促进老年人的独立性和适应能力

• 批准号: 10652026
• 负责人: Peter M. Abadir
• 金额: $ 35.58万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10652026
• 关键词: 3-Dimensional; Alzheimer' s Disease; Alzheimer' s disease patient; Attention; Behavior; Behavior Therapy; Biological Markers; Caregivers; Cognition; Cognitive; Data; Dementia; Diagnostic; Ecological momentary assessment; Elderly; Environment; Future; Health; Heart Rate; Home; Impaired cognition; Incidence; Individual; Instruction; Measurement; Measures; Memory; Music; Nature; Neurobehavioral Manifestations; Neurobiology; Patients; Pattern; Psychometrics; Reporting; Sampling; Sleep; Technology; Test Result; Time; Work; auditory stimulus; base; cognitive change; cognitive testing; diaries; evidence base; executive function; insight; mobile computing; new therapeutic target; resilience; response; sensor; statistical and machine learning; wearable device

Abstract Dementia research has focused almost exclusively on the deleterious aspects of AD, including memory loss, cerebral atrophy, and the molecular consequences of Aβ/tau pathology. Unfortunately, despite advances in our understanding of AD, the toolbox of viable treatments is no greater today than 20 years ago. An astonishing, yet underappreciated, aspect of AD/ADRD is the moment-to-moment fluctuation in cognitive health. Cognitive fluctuations, defined as spontaneous alterations in attention, arousal, and cognition, are evaluated based on the occurrence of symptoms such as impaired consciousness or confusion. However, the exact features and causes of cognitive fluctuations are still unclear. A greater understanding of the factors that lead to cognitive fluctuations and the precise behaviors that are present would allow us to identify strategies for preventing and managing dementia symptoms. We plan to provide a robust framework for quantifying cognitive fluctuations and lucid intervals in individuals with Alzheimer’s disease (AD) using a mobile technology platform applied within one’s home or local environment. Psychometric test results collected using a diagnostic app will be integrated with caregiver reports of cognitive changes to determine the features and incidence of these moments in AD patients (Aim 1). We then aim to identify reliable predictors of cognitive fluctuations and lucid intervals using wearable health sensors to uncover data-driven patterns of cognitive change (Aim 2). This three-dimensional view of cognition (in-home cognitive testing, caregiver reports, health sensors) will provide unprecedented insight into the nature of cognitive decline, cognitive fluctuations, and potentially incidences of lucidity.

摘要 痴呆症研究几乎完全集中在AD的有害方面，包括记忆力 损失、脑萎缩和Aβ/tau病理学的分子后果。不幸的是，尽管 随着我们对AD认识的进步，目前可行的治疗方法的工具箱不超过20年 前AD/ADRD的一个令人惊讶但未被充分认识的方面是， 认知健康。认知波动，定义为注意力、唤醒和 认知，根据症状的发生进行评估，如意识受损或 混乱.然而，认知波动的确切特征和原因仍然不清楚。更大 了解导致认知波动的因素以及 目前将使我们能够确定预防和管理痴呆症症状的策略。我们计划 为量化个体的认知波动和清醒间隔提供了一个强大的框架 使用应用于家中或本地的移动的技术平台， 环境使用诊断应用程序收集的心理测试结果将与护理人员集成 认知变化的报告，以确定AD患者中这些时刻的特征和发生率 (Aim 1）。然后，我们的目标是确定可靠的预测认知波动和清醒的时间间隔，使用 可穿戴健康传感器，以揭示认知变化的数据驱动模式（目标2）。这种认知的三维视图（家庭认知测试，护理报告，健康传感器）将提供 对认知能力下降、认知波动的性质以及潜在的 清醒的机会