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用消费级智能手表和机器学习方法在糖尿病患者中检测夜间低血糖症

Camilo Mendez^1,2,3, Ceren Asli Kaykayoglu^1,2,3, Thiemo Bähler¹

¹Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Journal of diabetes science and technology

|February 25, 2025

概括

消费者智能手表显示出在胰岛素治疗糖尿病患者中检测夜间低血糖的前景. 使用智能手表数据的机器学习模型可以识别低血糖事件,为持续血糖监测 (CGM) 提供潜在的低成本替代方案.

科学领域:

生物医学工程生物医学工程
数字健康数字健康
糖尿病管理糖尿病管理

背景情况:

夜间低血糖是胰岛素治疗糖尿病患者的重大风险.
持续血糖监测 (CGM) 系统有其局限性,包括准确性,成本和可访问性.
现有的检测夜间低血糖的方法是不够的.

研究的目的:

为了确定与夜间低血糖相关的生理生物标志物.
用消费级智能手表数据评估机器学习 (ML) 在检测夜间低血糖症方面的有效性.
探索智能手表作为一种低成本,可访问的糖尿病管理工具.

主要方法:

分析了36名胰岛素治疗糖尿病的成年人的351个晚上的数据.
同时使用两个智能手表和CGM系统.
开发和训练使用智能手表衍生的生生理学数据的ML模型.

主要成果:

低血糖夜晚的特点是觉醒,心率,压力和活动增加,特别是在早期睡眠期间.
ML模型实现了0.78 (一级) 和0.83 (二级) 的接收机操作员曲线下的面积,用于在早期睡眠期间检测低血糖症.
该模型表现出高灵敏度 (高达0.89) 和负预测值 (高达0.99).

结论:

消费级智能手表显示出检测夜间低血糖症的潜力,特别是在早期睡眠期间.
利用智能手表数据的ML模型可以作为CGM的补充工具.
需要进一步完善ML模型,以减少虚假报警并提高临床实用性.

关键词:

糖尿病糖尿病患者糖尿病患者数字生物标志物数字生物标志物机器学习是机器学习.夜间的睡眠,就是睡眠.夜间低血糖症夜间低血糖症智能手表的智能手表

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