对电子卫生技术个性化方法进行系统审查.
Iris Ten Klooster1, Hanneke Kip1,2, Lisette van Gemert-Pijnen1
1Centre for eHealth and Wellbeing Research, Department of Psychology, Health, and Technology, University of Twente, Enschede, The Netherlands.
iScience
|September 18, 2024
概括
本综述对电子健康个性化策略进行了分类,揭示了13个集群和10个计算方法. 未来的研究应该专注于技术特征,以便在数字健康工具中更好地对用户进行细分.
科学领域:
- 数字健康数字健康
- 人与计算机的交互
- 计算方法 计算方法
背景情况:
- 电子健康技术的个性化是常见的,但并未得到充分理解.
- 缺乏对个性化方法及其计算基础的全面理解.
研究的目的:
- 系统地审查和集群电子卫生中的个性化方法.
- 识别用于用户细分和技术适应的计算方法.
- 揭示当前个性化研究中的差距.
主要方法:
- 对412份关于电子健康个性化报告的系统文献综述.
- 基于用户细分变量和电子健康适应的个性化方法的集群.
- 使用的计算方法的识别和分类.
主要成果:
- 确定了13个不同的个性化方法集群 (例如,行为 + 道,环境 + 建议).
- 记录了10种用于将用户部分与技术适应相匹配的计算方法 (例如分类,强化学习).
- 发现技术相关变量和用户交互提醒的探索有限.
结论:
- 目前的电子健康个性化严重依赖单个变量类型,表明需要更复杂的细分.
- 未来的研究应该整合技术特征,以开发更个性化的个性化策略.
- 建议对用户交互提醒和各种变量类型进行进一步调查,以加强电子健康个性化.
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