提高预防性数字卫生干预措施的可预测性和有效性:范围审查
Keld Pedersen1, Bjarne Rerup Schlichter1
1Information Systems, Department of Management, Aarhus University, Aarhus C, Denmark.
预防性数字健康干预 (P-DHIs) 的结果不一. 这项研究开发了一种投资模型,以提高P-DHI结果的可预测性和生活方式相关疾病的大规模实施效率.
科学领域:
- 数字健康数字健康
- 公共卫生干预 公共卫生干预
- 健康 行为 改变 改变
背景情况:
- 生活方式疾病带来了重大的公共卫生挑战,增加了死亡率和医疗保健成本.
- 预防性数字健康干预 (P-DHIs) 显示出潜力,但结果和实施障碍各不相同.
- 由于采用和实施因素,为可预测的结果扩大P-DHIs仍然是一个挑战.
研究的目的:
- 增强对提高P-DHIs结果可预测性的理解.
- 提高大规模P-DHI实施的有效性.
- 专注于P-DHIs中的身体活动和饮食行为.
主要方法:
- 基于PRISMA-ScR方法的多学科范围审查.
- 在Web of Science,PubMed和Google Scholar搜索相关的英语文章.
- 利用信息系统理论,公共卫生和mHealth文献来开发P-DHI投资模式.
主要成果:
- 203篇文章符合资格标准,采用不同的研究方法.
- 开发的P-DHI投资模型确定了提高结果可预测性的关键构造.
- 该模型突出了提高大规模P-DHI实施效率的关键因素.
结论:
- 报告与P-DHI投资模型构造的经验研究可以提高结果的可预测性.
- P-DHI投资模式可以指导评估和设计,以实现更有效的大规模实施.
- 由于复杂性和缺乏协调控制,大规模P-DHI实施的成本效益是不确定的.
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