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

摘要 痴呆症的研究几乎完全集中在阿尔茨海默病的有害方面，包括记忆 丢失，脑萎缩，以及Aβ/Tau病理的分子后果。不幸的是，尽管 我们对阿尔茨海默病的理解的进步，今天是可行治疗的工具箱，不会超过20年 那是以前的事了。AD/ADRD的一个惊人但未得到重视的方面是 认知健康。认知波动，定义为注意力、觉醒和 认知，是根据出现的症状进行评估的，如意识受损或 我很困惑。然而，认知波动的确切特征和原因仍不清楚。一个更伟大的 了解导致认知波动的因素和准确的行为 目前将使我们能够确定预防和管理痴呆症症状的战略。我们计划 为量化个体的认知波动和清醒间隔提供可靠的框架 阿尔茨海默病(AD)患者使用家中或本地应用的移动技术平台 环境。使用诊断应用程序收集的心理测试结果将与照顾者集成 关于AD患者认知改变以确定这些时刻的特征和发生率的报道 (目标1)。然后，我们的目标是确定认知波动和清醒间隔的可靠预测因素，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)。这一认知的三维视图(家庭认知测试、照顾者报告、健康传感器)将提供 对认知衰退的本质、认知波动和潜在的 清醒的偶然性。

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.