基于传感器的数字生物标志物用于早期识别认知脆弱性:系统性审查
IEEE journal of biomedical and health informatics
|December 15, 2025
概括
基于传感器的数字生物标志物显示出检测认知脆弱性 (CF) 的前景,这种疾病结合了身体脆弱性和轻微的认知障碍. 步行和身体活动指标是关键指标,模型显示诊断性能中等至良好.
科学领域:
- 老年学是一门学科.
- 生物医学工程 生物医学工程
- 数字健康数字健康
背景情况:
- 认知虚弱 (CF),身体虚弱 (PF) 和轻度认知障碍 (MCI) 的组合,是老龄化人口的重大健康风险.
- 由于各种标准和复杂的评估,早期CF检测具有挑战性.
- 基于传感器的数字生物标志物为客观和可访问的CF评估提供了潜在的解决方案.
研究的目的:
- 系统地审查和综合基于传感器的认知虚弱 (CF) 检测数字生物标志物的证据.
- 确定与CF相关的最有效的传感器和数字生物标志物.
- 评估用于CF预测开发的机器学习模型的性能.
主要方法:
- 按照PRISMA 2020指南,对八个数据库进行系统的文献审查.
- 来自20项资格研究的数据的定性和定量综合.
- 分析传感器类型 (例如IMU),数字生物标志物 (例如步态,体力活动,身体组成,心血管指数) 和预测模型.
主要成果:
- 惯性测量单位 (IMU) 是最常见的传感器,主要测量步态和运动功能.
- 步行特征 (脚步长度,速度) 和身体活动 (MVPA) 显示出与CF有显著的关联.
- 用于CF预测的模型报告了高达0.911的AUC和0.81/0.80的聚合灵敏度/特异性,这表明分辨能力中等至良好.
结论:
- 基于传感器的数字生物标志物,特别是步态和身体活动指标,显示出早期认知虚弱 (CF) 检测的巨大潜力.
- 研究设计和定义的异质性需要标准化的协议和严格的临床转换验证.
- 需要进一步的研究来完善传感器技术和建模方法,以便可靠地评估CF.
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