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.

摘要 尽管最好的科学努力，没有新的治疗阿尔茨海默病（AD）已被批准的 FDA自2003年以来然而，这可能不是由于正在研究的治疗，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患有AD或几乎没有经历过AD 减少AD症状。因此，为了提高成功的可能性，AD临床试验必须同质化或 用那些会经历更快衰退的人来丰富他们的研究样本， 与AD确认。一个丰富的解决方案是有关的程度的表现改变一个人 经验，由于反复暴露的筛选评估，被称为实践效果。实践效果 可用于告知AD的预后、诊断和治疗反应。我们最近设计了一个 一款名为SuperG的在线视频游戏，它使用手指协调来评估个人练习效果， 不到7分钟的监督。我们打算将我们的在线视频游戏部署到MindCrowd中， 全球超过100，000名参与者的队列，其基础设施设计用于远程、大规模和广泛的 分布式研究发现和研究AD的早期生物标志物。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广告为重点的研究的兴趣相结合， 加强他的职业生涯，创造下一代，“众包”筛选程序，以丰富AD临床 试验企业。该应用程序的总体目标是利用MindCrowd电子队列， 确定如何学习能力，与SuperG测定，涉及认知和日常功能的变化， 时间，同时为PI在运动认知互动，电子队列和 在老龄化背景下的实践效果。基于导师团队的广泛出版和试点工作， 核心假设是，对SuperG的练习效果将预测认知和日常生活的一年变化。 在MindCrowd老年人中发挥作用。由于SuperG游戏很容易在线收集， 这项在分布式电子队列中进行的拟议研究提供了一种负担得起的有效手段， AD的临床试验这项建议有两个独立的目标。首先，我们将确定 SuperG练习效果预测老年人一年的认知变化。二是 确定SuperG练习效果预测老年人一年功能变化的程度。如果 该项目取得成功后，将为认知衰老研究提供一种新型在线筛查工具，该工具具有 有可能丰富未来的阿尔茨海默病和相关痴呆症临床试验。该项目还 包括PI的职业目标和培训活动，涉及：运动认知互动，电子 队列，实践效果，共同提供了建立一个“虚拟”实验室的独立性， 现代能力。此外，该项目的“社会距离”性质在这一背景下特别重要。 COVID-19的影响，并将允许远程安全参与者。