Development and Validation of Cognitive Status Assessments in Older Adults Utilizing Conversational Agent Technology (CAT) Delivered on Smart Displays - the CAUCAT Study

项目总结： 轻度认知障碍(MCI)以及阿尔茨海默病和相关痴呆症(ADRD)仍然存在 可悲的是诊断不足。尽管有许多科学验证的筛查方法可用，但这还是存在的。 医疗器械和最近对医生的经济激励。这种“证据-实践”的差距至少在很大程度上是 因为现有的筛查技术管理起来很麻烦、昂贵或太耗时 在日常临床护理中经常使用。此外，目前的仪器通常需要训练有素的讲解员管理， 和/或护理者确认，并且通常在人工临床环境中给药，而不是 在损害影响日常生活的环境中进行监测，引入潜在的回忆偏差。相反， 数字化评估的尝试通常不是针对目标人口优化的形式和功能， 需要额外的计算机知识和技能或广泛的用户技术培训，并限制用户 接受。我们已经开发并演示了会话代理技术的潜在用途 (CAT)在智能扬声器和智能显示器上提供，用于连续监测认知和日常生活 老年人在家中的地位。760名老年人(60-95岁)的初步数据表明，他们有能力 部署由语音和屏幕管理支持的认知筛选工具，而不受 不熟悉的技术界面或对临床管理员的依赖，所有这些都在受试者的舒适 居住环境。配套的智能手机应用程序可以在需要的地方对照顾者进行评估。跟随 文献综述和专家小组输入，我们已经确定认知评估测试来自井- 经过验证的NIH工具箱认知电池(NIHTCB)是最好的适应目标和潜在的广泛规模 临床和商业用途。本研究有两个目标：1)创建一个居家对话代理 管理综合认知评估平台，利用NIHTCB的评估测试；以及 2)定义CAT适应认知测验的心理测量学特性，并与标准进行比较 评估。在我们的第1A阶段研究中，具体目标是在30个队列中证明可行性和可用性 从我们目前的商业用户群中招募的患者。基于令人满意的技术可行性证据， 我们将进行一项1B阶段的研究，具体目的是确定适应 100例患者-照顾者对偶认知评定量表。CAT管理的评估将是 与当前标准评估相比，评估同时标准的有效性。我们假设 与当前标准评估相比，组内相关系数(ICC)为0.9。评分员内部可靠性 将通过重测方法进行评估，我们假设r&gt；0.8。这项研究的结果将为 评估纵向评估和机器学习算法的临床实用性的第二阶段项目 认知衰退轨迹的预测者。 1

PROJECT SUMMARY: Mild Cognitive Impairment (MCI), as well as Alzheimer’s Dementia and Related Dementias (ADRD) remain woefully under diagnosed. This is despite the availability of numerous scientifically validated screening instruments and recent financial incentives to physicians. This “evidence-practice” gap is at least in large part because available screening techniques are cumbersome to administer, expensive or too time consuming for regular use in daily clinical care. Additionally, current instruments often require trained docent administration, and/or caregiver confirmation, and typically are administered in the artificial clinical setting rather than monitored in the setting where impairment affects daily living, introducing potential recall bias. Conversely, attempts to digitize assessments typically are not form and function optimized for the target demographic, requiring additional computer literacy and skills or extensive user technology training, and limiting user acceptance. We have developed and demonstrated the potential utility of conversational agent technology (CAT) delivered on smart speakers and smart displays for serially monitoring the cognitive and daily living status of older adults in the home. Preliminary data on 760 older adults (age 60-95) suggests the ability to deploy cognitive screening tools enabled by voice and screen administration, without the potential limitations of unfamiliar technology interfaces or dependency on a clinical administrator, all in the comfort of the subject’s living environment. A companion smart phone app allows caregiver assessment where needed. Following literature review and expert panel input, we have determined that cognitive assessment tests from the well- validated NIH Toolbox Cognitive Battery (NIHTCB) are the best target for adaptation and potential broad scale clinical and commercial use. The present study has two objectives: 1) Create an in-home conversational agent administered comprehensive cognitive assessment platform utilizing assessment tests from the NIHTCB; and 2) Define the psychometric properties of CAT adapted cognitive assessments and compare them to standard assessments. In our Stage 1A study, the specific aim is proving feasibility and usability in a cohort of 30 patients recruited from our current commercial user base. Upon satisfactory evidence of technology viability, we will undertake a Stage 1B study with the specific aim of defining the psychometric properties of the adapted cognitive assessment instruments in 100 patient-caregiver dyads. CAT administered assessments will be evaluated for concurrent criterion validity compared to current standard assessments. We hypothesize Intraclass Correlation Coefficient (ICC) >0.9 compared to current standard assessments. Intra-rater reliability will be assessed with test-retest methods and we hypothesize r >0.8. Results from this study will inform a Phase II project assessing the clinical utility of longitudinal assessments and machine learning algorithms as predictors of the trajectory of cognitive decline. 1