Wu Jingchuan, Ram Nilam, Marks James, Streeper Necole M, Conroy David E
Department of Kinesiology, The Pennsylvania State University, University Park, PA 16802 United States of America.
Departments of Communication and Psychology, Stanford University, Palo Alto, CA 94305 United States of America.
Chin Polit Sci Rev. 2025 Jun;10(2):254-267. doi: 10.1007/s41111-024-00255-1. Epub 2024 Jun 24.
This study illustrates the application of time series clustering and feature engineering techniques to small data obtained at a fast time-scale from biobehavioral interventions to identify slower time-scale health outcomes.
Using data from 26 adult kidney stone patients engaged with mini-sip, a month-long digital health intervention targeting increased fluid intake, we identified distinct patterns of engagement with both manual app tracking and automated smart water bottles and examined how those patterns were related to subsequent urine volume.
Time-series based analysis of engagement revealed that manual tracking was significantly associated with increased urine volume, highlighting the potential for active self-monitoring to improve health behaviors. In contrast, differential patterns of engagement with automated tracking were not related to differences in urine volume.
These findings suggest that small data approaches can effectively bridge time scales in behavioral interventions, and that manual engagement methods may be more beneficial than automated ones in fostering behavior change. Absent large datasets to support identification of engagement patterns via deep learning, time series clustering and feature engineering provide valuable tools for linking fast time-scale engagement processes with slow time-scale health outcome processes.
This study was conducted with the approval of the Institutional Review Board (STUDY00015017), granted on 9/22/2021.
本研究阐述了时间序列聚类和特征工程技术在从生物行为干预快速时间尺度获取的小数据中的应用，以识别较慢时间尺度的健康结果。
利用26名成年肾结石患者参与mini-sip（一项为期一个月旨在增加液体摄入量的数字健康干预）的数据，我们识别出手动应用程序追踪和自动智能水瓶的不同参与模式，并研究这些模式与后续尿量的关系。
基于时间序列的参与度分析表明，手动追踪与尿量增加显著相关，突出了主动自我监测改善健康行为的潜力。相比之下，自动追踪的不同参与模式与尿量差异无关。
这些发现表明，小数据方法可以有效弥合行为干预中的时间尺度，并且在促进行为改变方面，手动参与方法可能比自动方法更有益。由于缺乏大型数据集来支持通过深度学习识别参与模式，时间序列聚类和特征工程为将快速时间尺度的参与过程与较慢时间尺度的健康结果过程联系起来提供了有价值的工具。
本研究在机构审查委员会（STUDY00015017）批准下进行，批准日期为2021年9月22日。
