动态贝叶斯网络方法来建模参与和行走行为:长达一年的微型随机试验 (Heartsteps II) 的见解
Steven A De La Torre1, Mohamed El Mistiri2, Karine Tung3
1The Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA, United States.
Health psychology and behavioral medicine
|September 22, 2025
概括
动态贝叶斯网络有效地模拟了移动健康干预措施的参与,显示消息增加了应用程序的使用量,西班牙裔/拉丁裔参与者将应用程序的使用与步行联系起来. 这有助于个性化的数字健康策略.
科学领域:
- 数字健康数字健康
- 行为科学 行为科学
- 计算建模 计算建模
背景情况:
- 像可穿戴设备和应用程序这样的移动健康 (mHealth) 技术可以促进行为改变.
- 参与者参与数字干预对于有效性至关重要.
- 行为变化的动态理论需要强大的建模方法来进行参与分析.
研究的目的:
- 模拟参与移动健康干预的个人 (图形) 每日动态.
- 调查应用参与度,体力活动和干预组件之间的关系.
- 探索这些动态中的种族差异.
主要方法:
- 动态贝叶斯网络 (DBN) 被用来建模日常关系.
- 变量包括应用程序参与度 (页面浏览量),步行行为,消息,上下文 (温度) 和心理因素 (休息,忙碌).
- 在西班牙裔/拉丁裔和非西班牙裔/拉丁裔白人参与者之间比较了DBN模型.
主要成果:
- 消息对所有参与者的每日应用程序页面浏览量产生了积极的影响.
- 西班牙裔/拉丁裔参与者显示,应用程序页面浏览量和步行行为之间存在积极联系.
- 两组人都在背景和心理状态如何影响行走方面表现出个体差异.
结论:
- 在数字干预研究中,DBN对于建模表观行为动态是有益的.
- 这种方法可以完善行为变化的动态理论.
- 它支持个性化移动健康干预措施的开发.
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