Using an Online Video Game to Predict Functional and Cognitive Decline within the MindCrowd Electronic Cohort

使用在线视频游戏来预测 MindCrowd 电子队列中的功能和认知衰退

• 批准号: 10314477
• 负责人: Andrew Hooyman
• 金额: $ 6.64万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10314477
• 关键词: Using; Online; Video; Game; Predict

ABSTRACT Despite the best scientific efforts, no new treatment for Alzheimer’s Disease (AD) has been approved by the FDA since 2003. However, this may not be due to the treatment under investigation but rather clinical heterogeneity within the study sample, as a subset of participants may not have AD or experience little to no decline in AD symptoms. Thus, to improve the likelihood of success, AD clinical trials must homogenize or enrich their study sample with individuals who will experience more rapid decline and are biologically- confirmed with AD. One enrichment solution is related to the degree of performance change a person experiences due to repeated exposure of a screening assessment, known as a practice effect. Practice effects can be used to inform prognosis, diagnosis and treatment response in AD. We have recently designed an online video game, called SuperG, that uses finger coordination to assess individual practice effects without supervision in less than 7 minutes. We intend to deploy our online video game into MindCrowd, an electronic cohort of >100,000 participants worldwide designed with the infrastructure for remote, large-scale, and widely- distributed research to discover and study early biomarkers of AD. The long-term goal of this project is to merge the PI’s experience in learning and video game development with his interest in AD-focused research to enhance his career in creating next-generation, ‘crowd-sourced’ screening procedures to enrich the AD clinical trial enterprise. The overall objective of this application is to utilize the MindCrowd electronic cohort to determine how learning capacity, assayed with SuperG, relates to changes in cognition and daily function over time, while providing valuable mentorship for the PI in motor-cognitive interactions, electronic cohorts and practice effects in the context of aging. Based on extensive published and pilot work from the mentorship team, the central hypothesis is that practice effects on SuperG will predict one-year changes in cognition and daily function among MindCrowd older adults. Since SuperG game play is easily collected online, the rationale for this proposed research in a distributed electronic cohort offers an affordable and efficient means to enrich clinical trials in AD. There are two independent aims within this proposal. First, we will determine the extent that SuperG practice effects predict one-year cognitive change in older adults. Second, we will determine the extent that SuperG practice effects predict one-year functional change in older adults. If successful, this project will provide cognitive aging research with a novel online screening tool that has the potential to enrich future Alzheimer’s Disease and Related Dementia clinical trials. This project also incorporates the PI’s career goals and training activities concerning: motor-cognitive interactions, electronic cohorts, and practice effects, together providing for independence in the establishment of a “virtual” lab with modern capabilities. Further, the ‘socially-distanced’ nature of this project is particularly relevant in the context of COVID-19 and will allow for the safe inclusion of participants remotely.

抽象的 尽管做出了最大的科学努力，但阿尔茨海默病（AD）的新疗法尚未获得批准 自 2003 年起获得 FDA 批准。然而，这可能不是由于正在研究的治疗方法，而是由于临床 研究样本内的异质性，因为一部分参与者可能没有 AD 或几乎没有经历 因此，为了提高成功的可能性，AD 临床试验必须同质化或 丰富他们的研究样本，选择那些会经历更快速衰退并且在生物学上处于劣势的个体。 AD 证实了一种浓缩解决方案与一个人的绩效改变程度有关。 由于反复接触筛选评估而产生的体验，称为练习效果。 我们最近设计了一种可用于告知 AD 的预后、诊断和治疗反应的方法。 名为 SuperG 的在线视频游戏，使用手指协调来评估个人练习效果，而无需 我们打算将我们的在线视频游戏部署到电子 MindCrowd 中。 全球超过 100,000 名参与者的队列设计了用于远程、大规模和广泛的基础设施 发现和研究 AD 早期生物标志物的分布式研究 该项目的长期目标是 将 PI 在学习和视频游戏开发方面的经验与他对以 AD 为中心的研究的兴趣结合起来 加强他在创建下一代“众包”筛查程序以丰富 AD 临床方面的职业生涯 该应用程序的总体目标是利用 MindCrowd 电子队列来 确定使用 SuperG 测定的学习能力与认知和日常功能变化之间的关系 时间，同时为 PI 在运动认知交互、电子队列和 基于指导团队广泛发表的和试点工作， 中心假设是 SuperG 的练习效果将预测认知和日常认知的一年变化 MindCrowd 老年人之间的功能 由于 SuperG 游戏很容易在线收集，因此其基本原理是 这项在分布式电子队列中提出的研究提供了一种负担得起且有效的手段来丰富 该提案有两个独立的目标：首先，我们将确定范围。 SuperG 练习效果可以预测老年人一年的认知变化。 确定 SuperG 练习效果预测老年人一年功能变化的程度。 成功后，该项目将为认知衰老研究提供一种新颖的在线筛查工具，该工具具有 该项目还有丰富未来阿尔茨海默病和相关痴呆症临床试验的潜力。 包含 PI 的职业目标和培训活动，涉及：运动认知交互、电子 队列和实践效果，共同提供建立“虚拟”实验室的独立性 此外，该项目的“社交距离”性质在背景下尤其相关。 的COVID-19，并将允许参与者远程安全参与。