Interactive Software for Assessing Meaningful Cognitive Function

项目总结 阿尔茨海默病(AD)和AD相关痴呆(ADRD)与严重残疾有关 以及丧失独立性，对受影响的个人、家庭照顾者和医疗保健造成负担和费用 系统。尽管人口老龄化迅速增长，AD和轻度AD的成本和后果也很高 认知障碍(MCI)、阿尔茨海默病(AD)和MCI的临床试验大多不成功，成本高昂，而且还没有 能够表现出临床上有意义的效果，如延缓临床症状的发生或进展 症状。成功预防痴呆症的一个重要障碍是目前可用的结果 在MCI临床试验中使用的方法无法检测到认知和功能的有意义的变化 发生在MCI中，导致小效应大小，临床试验需要大样本大小。发展中的 对临床上有意义的认知和功能的最早变化敏感的新评估工具 是改变MCI临床试验并成功预防痴呆症的迫切需要。 解决改善MCI临床试验结果衡量标准的迫切需要，这些临床试验包括 食品和药物管理局(FDA)的建议和国家老龄研究所(NIA)的战略 优先，我们将设计、开发和测试一种新的计算机化评估工具，该工具结合了临床 在MCI临床试验中改善结果测量的有意义的认识和作用。《认知与 IADL任务中的功能“(C-FIT)评估工具将通过语音或触摸屏轻松自我管理 输入；可靠和可重复；有效地检测早期和有意义的认知和功能变化 在患有MCI的老年人中。C-Fit评估工具的目标是提高早期发现有意义的 MCI临床试验中认知和功能的变化。在这个SBIR第一阶段项目中，我们将实现三个目标： 目的1：设计和开发C-Fit测评工具。目标2：C-Fit评估工具的初步测试。目标3： 展示C-Fit评估工具的初步可靠性和有效性。 为了实现这些目标，我们将设计一种新的计算机化评估，以评估关键认知 以及对MCI最敏感的功能结构域。我们将在10名成年人中测试C-Fit评估工具 年龄65岁或以上：5名认知健康，5名临床诊断为MCI。我们将利用一个 以用户为中心的设计流程，并使用参与者反馈反复改进C-Fit评估。我们 将对30名65岁或65岁以上的成年人进行心理测试：15名认知能力强的人 健康的15人，临床诊断为MCI的15人。为了评估重测的可靠性，参与者需要完成 两次C-Fit评估，间隔两周。每个时间点的分数将使用INTERLASS进行比较 相关系数。为了评估同时效度，参与者将完成验证性认知和 MCI临床试验中使用的功能测试。分数将使用皮尔逊相关系数进行比较。

PROJECT SUMMARY Alzheimer’s disease (AD) and AD-related dementias (ADRD) are associated with significant disability and loss of independence, burden, and costs to affected individuals, family caregivers, and health care systems. Despite the rapidly growing aging population and the high cost and consequences of AD and mild cognitive impairment (MCI), AD and MCI clinical trials have been largely unsuccessful, costly, and have not been able to demonstrate clinically meaningful effects such as delaying the onset or progression of clinical symptoms. One significant barrier to successful dementia prevention is that currently available outcome measures used in MCI clinical trials are unable to detect meaningful changes in cognition and function that occur in MCI, resulting in small effect sizes that require large sample sizes for clinical trials. Development of new assessment tools that are sensitive to the earliest changes in clinically meaningful cognition and function are desperately needed to transform MCI clinical trials and lead to successful dementia prevention. To address the critical need for improved outcome measures for MCI clinical trials that incorporate Food and Drug Administration (FDA) recommendations and National Institute on Aging (NIA) strategic priorities, we will design, develop, and test a novel computerized assessment tool that combines clinically meaningful cognition and function to improve outcome measures in MCI clinical trials. The “Cognition and Function in an IADL Task” (C-FIT) Assessment Tool will be easy to self-administer via speech or touchscreen input; reliable and repeatable; and effective in detecting early and meaningful cognitive and functional changes in older adults with MCI. The goal of the C-FIT assessment tool is to improve early detection of meaningful change in cognition and function in MCI clinical trials. In this SBIR Phase I project we will execute three aims: Aim 1: Design and develop the C-FIT assessment tool. Aim 2: Initial test of the C-FIT assessment tool. Aim 3: Demonstrate initial reliability and validity of the C-FIT assessment tool. To achieve these aims, we will design a novel computerized assessment that evaluates key cognitive and functional domains that are most sensitive to MCI. We will test the C-FIT assessment tool with 10 adults ages 65 or older: 5 who are cognitively healthy and 5 who have a clinical diagnosis of MCI. We will utilize a user-centered design process and iteratively improve the C-FIT assessment using participant feedback. We will conduct psychometric testing with a new sample of 30 adults ages 65 or older: 15 who are cognitively healthy and 15 who have a clinical diagnosis of MCI. To assess test-retest reliability, participants will complete the C-FIT assessment twice, two weeks apart. Scores at each timepoint will be compared using intraclass correlation coefficients. To assess concurrent validity, participants will complete validated cognitive and functional tests used in MCI clinical trials. Scores will be compared using Pearson correlation coefficients.