小数据方法将更快的时间尺度参与动态与生物行为干预中的更慢的时间尺度结果联系起来
Jingchuan Wu1, Nilam Ram2, James Marks3
1Department of Kinesiology, The Pennsylvania State University, University Park, PA 16802 United States of America.
概括
在数字健康干预中手动自我监测显著增加了结石患者的尿量. 这突显了积极参与的作用.
科学领域:
- 数字健康干预措施 数字健康干预措施
- 生物行为科学 生物行为科学
- 医疗信息学 医疗信息学
背景情况:
- 数字健康干预 (DHI) 为慢性疾病管理提供了可扩展的解决方案.
- 了解患者参与模式对于优化DHI有效性至关重要.
- 将快速时间尺度的参与数据与缓慢时间尺度的健康结果相结合仍然是一个挑战.
研究的目的:
- 将时间序列聚类和特征工程应用于小型,快速时间尺度的生物行为数据.
- 在数字流体摄入干预中识别不同的患者参与模式.
- 将这些参与模式与较慢时间尺度的健康结果联系起来,特别是尿液量.
主要方法:
- 利用了26名成年结石患者的数据,使用了迷你-sip (IT) 数字健康干预.
- 雇佣时间序列聚类和功能工程从手动应用程序跟踪和自动化智能水瓶的参与数据.
- 分析了已识别的参与模式与随后的24小时尿量变化之间的关联.
主要成果:
- 手动应用程序跟踪的参与模式与尿量增加有显著的关联.
- 自动化智能水瓶接触模式与尿量没有显著的关系.
- 通过手动跟踪来积极自我监测似乎更有效地促进所需的健康行为.
结论:
- 小数据方法,包括时间序列聚类和特征工程,可以有效地将快速时间尺度的参与与慢时间尺度的健康结果联系起来.
- 在这种情况下,手动参与方法可能优于自动化方法来促进行为变化.
- 这些技术为分析生物行为干预数据提供了有价值的工具,当深度学习的大数据集不可用时.
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