模拟参与数字行为改变干预 (HeartSteps II):一个探索性的系统识别方法
Steven A De La Torre1, Mohamed El Mistiri2, Eric Hekler3
1The Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, CA, United States.
Journal of biomedical informatics
|September 12, 2024
概括
了解对数字行为改变干预措施 (DBCI) 的长期参与对于持续的身体活动至关重要. 这项研究使用了微随机试验来建模动态参与因素,揭示了个性化干预措施的关键预测因素.
科学领域:
- 数字健康数字健康
- 行为科学 行为科学
- 个性化医疗是个性化的医疗.
背景情况:
- 数字行为改变干预措施 (DBCI) 显示出改善身体活动的前景.
- 参与者对DBCI的长期参与尚未得到充分理解.
- 参与是一个动态的过程,受个人环境的影响.
研究的目的:
- 调查DBCI的长期参与动态.
- 用系统识别方法建模个人参与.
- 探索自决理论构造和参与之间的关系.
主要方法:
- 这是一项为期一年的微型随机试验 (HeartSteps II),共有11名参与者.
- 来自可穿戴传感器,应用程序使用和生态瞬间评估的综合数据.
- 应用自回归与外源输入 (ARX) 分析,灵感来自自决理论 (SDT).
主要成果:
- 灵感来自SDT的ARX模型预测了应用程序参与度,加权RMSEA为31.75%.
- 在西班牙裔/拉丁裔 (34.22%) 和非西班牙裔/拉丁裔白人 (22.39%) 参与者之间,模型适合程度有所不同.
- 每日通知提示,周末/周日状态,以及感知到的忙碌度预测了应用程序的使用情况.
结论:
- 开发了一种新的方法来模拟动态DBCI参与.
- 鉴定的因素可以个性化和调整干预措施,以持续改变行为.
- 根据个人背景定制干预措施可以随着时间的推移提高参与度.
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