Stricker Learning Span: A Computer Adaptive Word List Memory Test Optimized for Remote Assessment

项目摘要/摘要 由于新冠肺炎的存在，远程认知评估已经从一个重要的研究目标转变为一种 迫切的研究和临床需要。这种需求突显了缺乏经过充分验证的、 可用于远程评估的敏感、可靠和规范的测试。词表存储性能 阿尔茨海默病(AD)临床前阶段的早期变化，直到轻度阿尔茨海默病发病前20年 认知障碍(MCI)。尽管延迟的单词列表回忆被视为最敏感的记忆措施 在MCI和AD痴呆的研究中，传统的词表记忆测试范式需要交互 行政部门与审查员。此外，新近效应可能会影响延迟词表的敏感性 回忆：在即刻回忆试验中，对列表末尾单词的正确回忆可能代表注意力集中 Span/某些人的工作记忆。当这些词后来没有被延迟回忆时，它们是 被认为被遗忘了，但另一种观点认为，在重复学习的过程中，这些单词从未被完全编码 审判。与此相一致，AD的中心缺陷可能是学习缺陷之一。以帮助解决对 我们开发了一种新的词表学习范式，这是一种敏感而简短的远程学习措施。这个 Stricker学习广度(SLS)的目标是实现单词列表记忆测试对最早的已知敏感性 临床前AD更改，同时允许在智能手机或替代设备上进行可靠的远程自我管理 (例如，PC、平板电脑)。为了接触到最多的用户，这种灵活的方法是必要的 并消除潜在的健康差距。我们已经改变了传统的治字方式 以几种重要的方式列出内存测试，从而产生了一种充分利用计算机的新范式- 通过使用计算机自适应测试程序进行基础管理，侧重于学习、视觉 提供单词列表、4个选项识别以评估学习的单词，以及完整的数据捕获 使今后的研究能够侧重于基于过程的措施和机器学习的应用 技巧。这项研究将利用正在进行的基于人群的梅奥诊所研究的资源 老龄化，它已经纳入了诊所内和远程计算机化的认知评估。具体目标 包括(1)确定无监督SLS的可接受性和效率，(2)展示 通过检查与传统词表记忆测量的关联性来构建SLS的效度，(3) 通过对临床前AD的敏感性(横断面效应大小)确定SLS的标准效度 认知未受损的淀粉样蛋白PET阳性组和阴性组之间)以及与 所有参与者的AD神经成像生物标记物(tau PET，海马体体积)具有神经成像数据，以及 (4)确定SLS的7.5个月重测信度。灵敏、远程管理的计算机化 这些措施将有助于识别临床前阿尔茨海默病患者，以进行抗淀粉样蛋白和抗tau临床试验 作为符合治疗标准的个体，一旦抗淀粉样蛋白或抗tau疗法获得批准。

PROJECT SUMMARY/ABSTRACT Due to COVID-19, remote cognitive assessment has transitioned from an important research aim to an immediate and urgent research and clinical need. This need has underscored the lack of well-validated, sensitive, reliable, and well-normed tests available for remote assessment. Word list memory performance changes early in the preclinical phase of Alzheimer’s disease (AD), up to 20 years prior to onset of mild cognitive impairment (MCI). Although delayed word list recall is viewed as the most sensitive memory measure in studies of MCI and AD dementia, traditional word list memory test paradigms require interactive administration with an examiner. Further, recency effects may influence the sensitivity of delayed word list recall: correct recall of words at the end of the list during immediate recall trials may represent attentional span/working memory for some individuals. When these words are subsequently not recalled at delay, they are considered forgotten, but an alternate view is that the words were never fully encoded during repeated learning trials. In line with this, the central deficit in AD may be one of learning. To help address the critical need for a sensitive and brief remote learning measure, we have developed a new word list learning paradigm. The Stricker Learning Span (SLS) aims to achieve the known sensitivity of word list memory tests to the earliest preclinical AD changes while allowing reliable remote self-administration on a smartphone or alternative device (e.g., PC, tablet) through a web-based platform. This flexible approach is necessary to reach the most users and eliminate potential health disparities. We have transformed the traditional manner of administering a word list memory test in several important ways, resulting in a novel paradigm that takes full advantage of computer- based administration through use of a computer adaptive testing procedure focused on learning, visual presentation of the word list, 4-choice recognition to assess words learned, and a complete data capture approach to allow future studies focused on process-based measures and application of machine learning techniques. This study will leverage the resources of the ongoing population-based Mayo Clinic Study of Aging, which already incorporates in-clinic and remote computerized cognitive assessment. Specifics aims include to (1) determine the acceptability and efficiency of the unsupervised SLS, (2) demonstrate the construct validity of the SLS by examining association with a traditional word list memory measure, (3) determine the criterion validity of the SLS through sensitivity to preclinical AD (cross-sectional effect sizes between cognitively unimpaired amyloid PET positive and negative groups) and associations with continuous AD neuroimaging biomarkers (tau PET, hippocampal volume) in all participants with neuroimaging data, and (4) determine 7.5 month test-retest reliability for the SLS. Sensitive, remotely administered computerized measures will help to identify individuals with preclinical AD for anti-amyloid and anti-tau clinical trials, as well as individuals meeting criteria for treatment once anti-amyloid or anti-tau therapies are approved.