Development of a Mobile Cognitive App for Detecting and Monitoring Change in Preclinical Alzheimer's Disease

开发用于检测和监测临床前阿尔茨海默病变化的移动认知应用程序

• 批准号: 10252922
• 负责人: DAWN J MECHANIC-HAMILTON
• 金额: $ 17.91万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10252922
• 关键词: Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Amyloid; Area; Assessment tool; Atrophic; Biological Markers; Caring; Clinic; Code; Cognition; Cognitive; Cognitive deficits; Data; Data Collection; Data Set; Detection; Development; Diagnosis; Early Diagnosis; Elderly; Enrollment; Environment; Floor; Funding; Health; Hippocampus (Brain); Home; Impaired cognition; Individual; Intervention; Intervention Studies; Intervention Trial; Knowledge; Lead; Length; Magnetic Resonance Imaging; Measures; Medial; Memory; Molecular; Monitor; Motivation; Nerve Degeneration; Neurobehavioral Manifestations; Neuropsychology; Pattern; Performance; Phosphorus 32; Positron-Emission Tomography; Preventive treatment; Principal Investigator; Property; Psychometrics; Reporting; Research; Resolution; Resources; Risk; Structure; Technology; Temporal Lobe; Testing; Time; United States National Institutes of Health; Update; Validation; aging brain; base; career; career development; cognitive change; cognitive neuroscience; cognitive performance; cognitive testing; cohort; computerized; design; digital; executive function; experience; flexibility; mobile computing; molecular pathology; neuroimaging marker; pre-clinical; processing speed; ranpirnase; response; screening; targeted imaging; theories; tool; treatment trial; user-friendly

PROJECT SUMMARY/ABSTRACT The diagnosis and monitoring of cognitive symptoms in Alzheimer's disease and related dementias (ADRD) requires valid and reliable assessment of cognitive change. Current limitations in traditionally used testing measures include the length of testing, low engagement, day-to day variability, few data points, and limits in the ability to identify subtle differences in normal individuals at risk for cognitive decline. Many of the currently available computerized assessment tools either (1) have not been updated to incorporate current cognitive neuroscience theory (2) do not target measures associated with the earliest neuroanatomical changes in brain aging and neurodegeneration and/or (3) do not capitalize on the benefits of digital technology to maximize valid and reliable cognitive data collection. Mobile and sensitive measures for detection of subtle cognitive change in preclinical Alzheimer's Disease (AD) along with biomarker measures of molecular pathology and neurodegeneration will combine to identify individuals who will benefit most from interventions. This project will include development and validation of two, engaging, user-friendly and empirically based cognitive assessment tasks and create the Mobile Cognitive App Performance Platform (mCAPP). The two cognitive tests will comprise: (1) a memory card game (already designed and pilot-tested) that includes similar pairs of objects and increasing memory load and (2) an executive functioning and processing speed test using a code completion game. The cognitive tests will include a high-ceiling and low floor to capture a range of ability, burst testing to increase reliability of the data, gamified design to increase engagement and motivation, and mobile- based design for capturing data in all environments. The mCAPP will be validated in a well-characterized cohort of older adults with normal cognition in the Penn Alzheimer's Disease Center. The cohort will concurrently complete a neuropsychological battery, high resolution structural MRI and amyloid PET imaging through funded P32 and R01 projects, which will be leveraged in this study to understand the relationships between cognitive performance and neuroimaging biomarker status. Structural imaging targets will include areas of the medial temporal lobe implicated in the earliest stages of preclinical Alzheimer's disease. The purpose of the mCAPP is to collect reliable and valid cognitive data to detect very early signs of AD-related cognitive change and remotely track response to interventions. Remotely administered, engaging cognitive tests that are sensitive to the earliest changes in individuals at risk for AD have the potential to expand our knowledge of cognition in aging, lead to early detection of cognitive variability, monitor change over time and change as a result of intervention. This project will facilitate collection of data to support larger studies with the mCAPP and career development opportunities in ADRD research for the early career principal investigator.

项目概要/摘要 阿尔茨海默病和相关痴呆症 (ADRD) 认知症状的诊断和监测 需要对认知变化进行有效且可靠的评估。传统测试的当前局限性 衡量标准包括测试时间长、参与度低、日常变化、数据点少以及限制 识别有认知能力下降风险的正常个体的细微差异的能力。目前许多 可用的计算机化评估工具 (1) 尚未更新以纳入当前的认知能力 神经科学理论 (2) 并不针对与大脑最早的神经解剖学变化相关的测量 衰老和神经退行性疾病和/或 (3) 没有利用数字技术的优势来最大化有效 和可靠的认知数据收集。用于检测细微认知变化的移动和敏感措施 临床前阿尔茨海默病 (AD) 以及分子病理学和生物标志物测量 神经退行性疾病将结合起来确定哪些人将从干预措施中受益最大。该项目将 包括开发和验证两种引人入胜、用户友好且基于经验的认知评估 任务并创建移动认知应用性能平台 (mCAPP)。这两项认知测试将 包括：（1）一个记忆卡游戏（已经设计并经过试点测试），其中包括相似的物体对 增加内存负载，以及 (2) 使用代码进行执行功能和处理速度测试 完成游戏。认知测试将包括高上限和低下限，以捕捉一系列能力、爆发力 测试以提高数据的可靠性，游戏化设计以提高参与度和积极性，以及移动设备 基于在所有环境中捕获数据的设计。 mCAPP 将在充分表征的环境中进行验证 宾夕法尼亚州阿尔茨海默病中心的一组认知能力正常的老年人。该队列将 同时完成神经心理学电池、高分辨率结构 MRI 和淀粉样蛋白 PET 成像 通过资助的 P32 和 R01 项目，本研究将利用这些项目来了解这些关系 认知表现和神经影像生物标志物状态之间的关系。结构成像目标将包括 与临床前阿尔茨海默病最早阶段有关的内侧颞叶区域。这 mCAPP 的目的是收集可靠且有效的认知数据，以检测 AD 相关的早期迹象 认知变化并远程跟踪对干预措施的反应。远程管理，参与认知 对 AD 风险个体的最早变化敏感的测试有可能扩大我们的研究范围。 了解衰老过程中的认知知识，从而及早发现认知变异，监测随时间的变化 因干预而改变。该项目将促进数据收集，以支持更大规模的研究 早期职业首席研究员在 ADRD 研究中的 mCAPP 和职业发展机会。