支持性问责模型在数字健康干预中的应用:范围审查
Gary Kwok1, Shannon Pui Ying Cheung1, Jennifer Duffecy2
1Cancer Prevention Precision Control Institute, Center for Discovery and Innovation, Nutley, NJ, United States.
Journal of medical Internet research
|September 26, 2025
概括
在数字健康干预 (DHI) 中的人类支持可以改善用户参与和坚持. 然而,Supportive Accountability Model (SAM) 的应用不一致,需要专注于其所有组成部分,以获得最大的DHI影响.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 行为科学 行为科学
背景情况:
- 数字健康干预 (DHIs) 面临着用户参与和遵守的挑战.
- 支持性问责制模型 (SAM) 提供了一个框架,通过结构化的人力支持来加强遵守.
研究的目的:
- 审查影响DHI参与和坚持的人类支持因素的研究程度.
- 评估SAM构造 (问责制,债券,合法性) 在DHIs中的应用.
主要方法:
- 根据PRISMA-ScR指南进行范围审查.
- 在6个数据库中进行了搜索 (MEDLINE,PsycINFO,Embase,CINAHL,Scopus,ClinicalTrials.gov).
- 包括36篇经验性,同行评审的关于人类支持的DHI的文章.
主要成果:
- 人类支持的来源,交付,频率和持续时间各不相同.
- 不一致的SAM应用,有限的重点是债券和合法性超越问责制.
- 人类支持有时是主要干预,而不是辅助.
结论:
- 在将SAM应用于人类支持的DHI方面存在差距.
- 未来的研究应该整合所有SAM组件,并使用人类支持作为辅助.
- 优化SAM应用可以提高DHI的有效性和健康结果.
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