使用可穿戴设备数据构建错过药物剂量的个性化预测模型:前性观察研究
Haru Iino1, Hayato Kizaki1, Shungo Imai1
1Division of Drug Informatics, Faculty of Pharmacy and Graduate School of Pharmaceutical Sciences, Keio University, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan, 81 3-5400-2650.
JMIR formative research
|June 24, 2025
概括
可穿戴设备 (WDs) 可以客观地跟踪生理和活动数据,以预测错过的药物剂量. 个性化的模型显示出高精度,特别是在早上和晚上方案中,突出显示了WDs.
科学领域:
- 生物医学工程 生物医学工程
- 数字健康数字健康
- 医疗信息学 医疗信息学
背景情况:
- 药物不服药是一个重大的医疗保健挑战,通常通过自我报告来衡量不可靠的情况.
- 可穿戴设备 (WDs) 通过收集持续的生理和活动数据,为客观的坚持监测提供了一个潜在的解决方案.
研究的目的:
- 开发和验证个性化的预测模型,用于识别错过的药物剂量.
- 利用来自WD的客观生理和活动数据来预测不坚持.
主要方法:
- 这是一项为期30天的观察性研究,有8名参与者使用果手表和专门的应用程序.
- 收集了人口统计数据,药物详细信息,心理因素,饮食时间和遗漏剂量.
- 从WDs (活动,心率,睡眠) 生成时间序列特征,并使用光梯度增强机模型.
- 组交叉验证 (CV) 和非滚动特征模型的比较.
主要成果:
- 个性化模型实现了高精度 (高达1000) 在预测遗漏剂量>20%不遵守的个体.
- 非滚动特征模型的表现普遍优于集团CV模型.
- 早晨剂量方案显示出更高的预测性能.
- 反映6小时,12小时和24小时模式的时间序列特征是关键预测因素.
结论:
- 来自WD的数据可以为高精度的个性化模型提供动力,用于检测遗漏的药物剂量.
- 这种方法证明了使用连续,客观数据从WDs预测不遵守的可行性.
- 未来的研究需要更大的群体进行验证,以及改善回忆的策略,特别是对于关键药物.
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