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技术与痴呆症 会议前会议

Emilie V Brotherhood1,2, Coty Chen1, Claire J Cadwallader1

  • 1Memory and Aging Center, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.

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概括

可穿戴的FitbitTM数据显示了前叶退行症 (FTLD) 患者的独特活动模式. 这些休息活动变化与轻度认知障碍或健康个体不同,有助于痴呆症研究.

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科学领域:

  • 神经科学是一个神经科学.
  • 老年学是指老年学的学科.
  • 生物医学工程 生物医学工程

背景情况:

  • 阿尔茨海默病和相关痴呆症 (ADRD) 显著影响睡眠-清醒周期.
  • 商业可穿戴设备提供了一种可扩展的方法,可以通过被动行为摄影来监测这些变化.
  • 描述休息活动模式可以提供对认知衰退的见解.

研究的目的:

  • 使用FitbitTM数据来描述连续休息活动模式.
  • 检查健康成年人和ADRD患者之间这些模式的差异.
  • 为了确定潜在的生物标志物对认知和功能衰退.

主要方法:

  • 收集了三轴动图数据 (步数),来自健康成年人的临床,认知,功能和情绪信息,轻度认知障碍 (MCI),阿尔茨海默病痴呆症 (AD) 和前叶退化 (FTLD) 队列.
  • 使用休息活动总和分钟级特征的量化活动模式.
  • 应用主要组件分析 (PCA) 来减少数据,并确定了五个关键组件,解释了超过85%的差异.

主要成果:

  • 主要组件1 (PC1),代表活动变化和振幅,与认知和功能下降得分 (CDR®+FTLD-NACC) 有负相关性.
  • PC1也显示了诊断组之间的显著差异,与健康和MCI队伍相比,FTLD患者的PC1得分下降.
  • 与休息活动开始时间相关的PC3与全球认知评分有负相关性.

结论:

  • 来自FitbitTM数据的活动变化和幅度可以区分FTLD患者.
  • 这些独特的活动概况与在轻度认知障碍或功能完整的人群中观察到的不同.
  • 穿戴式动图学显示出在神经退行性疾病中确定综合征特异性休息活动模式的前景.