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支持性问责模型在数字健康干预中的应用:范围审查

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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.

关键词:
数字健康干预是数字健康干预.人类支持 人类支持干预的坚持 干预的坚持干预参与干预参与干预参与移动健康 移动健康 移动健康 移动健康支持性的问责制支持性的问责制

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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.