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: 10441502
• 负责人: DAWN J MECHANIC-HAMILTON
• 金额: $ 17.93万
• 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/10441502
• 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; 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; pilot test; 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.

项目总结/摘要 阿尔茨海默病及相关痴呆（ADRD）认知症状的诊断和监测 需要有效和可靠的认知变化评估。传统测试的局限性 衡量标准包括测试的时间长度、低参与度、日常变化性、数据点少以及 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 现有的计算机化评估工具（1）尚未更新，以纳入当前的认知 神经科学理论（2）不针对与大脑中最早的神经解剖学变化相关的测量 老化和神经退化和/或（3）不利用数字技术的好处，以最大限度地提高有效 和可靠的认知数据收集。移动的和敏感的措施，用于检测细微的认知变化， 临床前阿尔茨海默病（AD）沿着分子病理学的生物标志物测量， 神经变性将联合收割机结合起来，以确定将从干预中获益最多的个体。该项目将 包括开发和验证两种吸引人、用户友好和基于经验认知评估 任务并创建移动的认知应用性能平台（mCAPP）。这两项认知测试将 包括：（1）一种记忆卡游戏（已经设计和试点测试）， 以及（2）使用代码的执行功能和处理速度测试 完成游戏认知测试将包括一个高天花板和低地板，以捕捉一系列的能力，爆发 测试以提高数据的可靠性，游戏化设计以提高参与度和动力，以及移动的- 基于的设计，可在所有环境中捕获数据。mCAPP将在充分表征的 宾夕法尼亚州阿尔茨海默病中心认知正常的老年人队列。则将队列 同时完成神经心理电池、高分辨率结构MRI和淀粉样蛋白PET成像 通过资助的P32和R 01项目，将在本研究中利用这些项目来了解 认知能力和神经影像学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早期阶段有关的内侧颞叶区域。的 mCAPP的目的是收集可靠和有效的认知数据，以检测AD相关的非常早期的体征。 认知变化和远程跟踪对干预措施的反应。远程管理，参与认知 对AD风险个体的早期变化敏感的测试有可能扩大我们的研究范围。 认知老化知识，导致认知变异的早期检测，监测随时间的变化， 因干预而改变。该项目将促进数据收集，以支持与 mCAPP和ADRD研究的早期职业首席研究员的职业发展机会。