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Validation of the Remote Cognitive Aging and Alzheimer’s Disease REsearch (R-CARE) Toolbox for Diverse Populations

Validation of the Remote Cognitive Aging and Alzheimer’s Disease REsearch (R-CARE) Toolbox for Diverse Populations
针对不同人群的远程认知衰老和阿尔茨海默病研究 (R-CARE) 工具箱的验证
批准号:
10737723
负责人:
Ali Ezzati
金额:
$210.19万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-15 至 2028-05-31

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中文摘要
翻译
摘要 面对面管理是目前评估认知和功能的“黄金标准” 在阿尔茨海默病和其他痴呆症(ADRD)的研究中。远程神经心理学 已倡导进行评估,以克服各种访问障碍并降低 神经心理服务。此外,由于紧迫的挑战和安全问题 由新冠肺炎大流行强加的,迫切需要强有力的方法来评估 以及通过远程评估监控认知和功能状态。的总目标是 目前的项目是验证、改进和校准远程认知老化和阿尔茨海默氏症 疾病研究(R-CARE)认知和监测评估工具箱 在最初没有痴呆症的老年人的不同样本中发挥作用。该工具箱将包括 来自统一数据集的测量,神经心理成套测验(UDSNB-3.0),补充 通过临床前AD试验中经常使用的测试。为远程用户带来挑战的面对面测试 管理将被基于平板电脑的计算机化任务所取代。我们将招聘600人 无痴呆症,种族/民族多样化(约1/3非西班牙裔黑人,1/3西班牙裔,1/3非西班牙裔 西班牙裔白人),居住在社区的参与者,年龄65岁,≥。一个随机的, 平衡设计将用于管理全面的临床和认知电池 跨两种评估模式(面对面与远程),基线间隔2-6周, 随访时间分别为18个月和36个月。数字生物标志物(计算机化测试和基于语音的测试) 并将收集基于ADRD血液的生物标记物。目的1:评估心理测量学 远程认知测试的特性,并对照标准的面对面测试评估其有效性。 目标2:评估全球认知和认知内部纵向变化的可比性 跨整个样本的评估模式(面对面与远程)以及 按性别和种族/族裔划分的小组。目标3(探索性):识别新的数字 为区分临床和病理疾病提供递增有效性的生物标志物 用于测量和预测认知和功能衰退。总而言之,这项研究 将验证跨不同认知域的远程管理测试,为 横断面效度和纵向效度,并为不同领域的远程测试提供标准化数据 人口。我们的长期目标是开发一个开源、可靠和有效的工具箱, 将使AD研究人员和临床医生能够评估来自不同背景的老年人 无论是否有能力参加面对面的访问。 1
英文摘要
ABSTRACT In-person administration is the current “gold-standard” for assessment of cognition and function in studies of Alzheimer’s disease and other dementia (ADRD). Remote neuropsychological assessment has been advocated to overcome various access barriers and decrease costs of neuropsychological services. Moreover, due to the pressing challenges and safety concerns imposed by the COVID-19 pandemic, there is an urgent need for robust methods for assessing and monitoring cognitive and functional status through remote assessments. The overall goal of the current project is to validate, refine, and calibrate the Remote Cognitive Aging and Alzheimer’s Disease REsearch (R-CARE) Toolbox for the assessment and monitoring of cognition and function in a diverse sample of initially dementia-free older adults. The toolbox will include measures from the Uniform Data Set, Neuropsychological Battery (UDSNB-3.0), complemented by frequently used tests in preclinical AD trials. In-person tests that pose challenges for remote administration will be replaced with tablet-based computerized tasks. We will recruit 600 dementia-free, racially/ethnically diverse (~ 1/3 non-Hispanic Black, 1/3 Hispanic, and 1/3 non- Hispanic White), community-residing participants aged ≥65 years. A randomized, counterbalanced design will be used to administer a comprehensive clinical and cognitive battery across both assessment modes (in-person vs. remote) separated by 2-6 weeks at baseline, with follow up visits at 18 and 36 months. Digital biomarkers (computerized tests and speech-based) and ADRD blood-based biomarkers will be collected. AIM 1: To evaluate the psychometric properties of remote cognitive tests and assess their validity against standard in-person tests. AIM 2: To evaluate comparability of longitudinal change in global cognition and within cognitive domains across modes of assessment (in-person vs remote) for the whole sample as well as subgroups defined by sex and race/ethnicity. AIM 3 (exploratory): To identify novel digital biomarkers that provide incremental validity for differentiating clinical and pathological disease stages, and for measuring and predicting cognitive and functional decline. Collectively, this study will validate remotely administered tests across different cognitive domains, provide evidence for cross-sectional and longitudinal validity, and provide normative data for remote tests in diverse populations. Our long-term goal is to develop an open-source, reliable and valid toolbox that would enable AD-researchers and clinicians to evaluate older adults from diverse backgrounds regardless of ability to attend in-person visits. 1
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Predictive analytics for cognitive decline and Alzheimer’s disease
Predictive analytics for cognitive decline and Alzheimer’s disease
Predictive analytics for cognitive decline and Alzheimer’s disease
Predictive analytics for cognitive decline and Alzheimer’s disease
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