在会话内获得数字运动技能和阿尔茨海默病风险因素之间的关系MindCrowd队列:跨部分描述性研究
Andrew Hooyman1, Matt J Huentelman2, Matt De Both2
1Department of Physical Therapy, Chapman University, 9401 Jeronimo Rd, Irvine, CA, 92618, United States, 1 7146287208.
JMIR aging
|April 24, 2025
概括
基于网络的运动技巧游戏Super G显示出作为阿尔茨海默病 (AD) 风险查数字生物标志物的潜力. 超级G的性能对阿尔茨海默病风险因素如年龄,性别和APOE ε4载体状况敏感,从而能够进行大规模的远程评估.
科学领域:
- 神经科学是一个神经科学.
- 数字健康数字健康
- 生物标志物发现发现
背景情况:
- 在实验室内获得运动技能对阿尔茨海默病 (AD) 生物标志物敏感.
- 远程,无监督,基于网络的技能任务提供了可扩展的AD风险查潜力.
研究的目的:
- 为了评估一个基于网络的运动技巧游戏",超级G",对AD风险因素的敏感性.
- 评估游戏在识别患有AD风险的个体中的实用性.
主要方法:
- 662名MindCrowd参与者 (≥45岁,记录APOE ε4状态) 被邀请参加远程,无监督的"超级G"游戏.
- 线性回归和逻辑回归分析了AD风险因素 (年龄,性别,APOE ε4,口头学习) 和Super G性能指标之间的关系.
主要成果:
- 在54名参与者中,Super G的表现与AD风险因素有显著的相关性.
- 与APOE ε4载体身份相关的反应时间较慢;与雄性相关的目标时间 (TinT) 更长.
- 与年龄较大相关的较低的TinT;观察到与口头学习的性别特异性相互作用.
结论:
- 基于网络的"超级G"游戏证明了其作为一种基于技能的数字生物标志物的潜力,用于大规模的AD风险查.
- 这种方法提供了一种资源高效的方法来识别患有阿尔茨海默病风险的个体.
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