Wang Minmin, Huang Kepei, Li Xiangning, Zhao Xuetong, Downey Laura, Hassounah Sondus, Liu Xiaoyun, Jin Yinzi, Ren Minghui
Department of Global Health, School of Public Health, Peking University, 38 Xue Yuan Road, Haidian District, Beijing100191, China.
School of Public Health, Imperial College London, London, England.
Bull World Health Organ. 2025 Feb 1;103(2):126-135F. doi: 10.2471/BLT.24.292157. Epub 2024 Dec 3.
To conduct a systematic review and meta-analysis of the facilitators of and barriers to the acceptance and use of digital health technology by health workers in low- and middle-income countries.
We searched several databases for relevant articles published until 25 April 2024. We extracted data on four unified theories of acceptance and use of technology factors (performance expectancy, effort expectancy, social influence and facilitating conditions) and six additional factors (attitude, habit, incentive, risk, trust and self-efficacy); how these affected the outcomes of behavioural intention and actual use; and the strength of association if reported. We conducted a meta-analysis of the quantitative studies.
We reviewed 36 publications, 20 of which were included in our meta-analysis. We observed that performance expectancy was the most frequently reported facilitator (in 21 studies; 58.3%) and that lack of facilitating conditions was the most cited barrier (10; 27.8%). From our meta-analysis, trust ( = 0.53; 95% confidence interval, CI: 0.18 to 0.76) and facilitating conditions ( = 0.42; 95% CI: 0.27 to 0.55) were the leading facilitators of behavioural intention and actual use, respectively. We identified concerns with performance expectancy ( = -0.14, 95% CI: -0.29 to 0.01) as the primary barrier to both outcomes.
Our approach of clustering the facilitators of and barriers to the acceptance and use of digital health technology from the perspective of health workers highlighted the importance of creating an enabling ecosystem. Supportive infrastructure, tailored training programmes and incentive policies should be incorporated in the implementation of digital health programmes in low- and middle-income countries.
对低收入和中等收入国家卫生工作者接受和使用数字健康技术的促进因素和障碍进行系统评价和荟萃分析。
我们在多个数据库中检索了截至2024年4月25日发表的相关文章。我们提取了关于技术接受和使用的四个统一理论因素（绩效期望、努力期望、社会影响和促进条件）以及六个额外因素（态度、习惯、激励、风险、信任和自我效能）的数据；这些因素如何影响行为意向和实际使用的结果；以及如果有报告，关联强度如何。我们对定量研究进行了荟萃分析。
我们审查了36篇出版物，其中20篇纳入了我们的荟萃分析。我们观察到，绩效期望是最常被报告的促进因素（21项研究；58.3%），而缺乏促进条件是最常被提及的障碍（10项；27.8%）。从我们的荟萃分析来看，信任（=0.53；95%置信区间，CI：0.18至0.76）和促进条件（=0.42；95%CI：0.27至0.55）分别是行为意向和实际使用的主要促进因素。我们确定对绩效期望的担忧（=-0.14，95%CI：-0.29至0.01）是这两个结果的主要障碍。
我们从卫生工作者的角度对数字健康技术接受和使用的促进因素和障碍进行聚类分析的方法，突出了创建有利生态系统的重要性。支持性基础设施、量身定制的培训计划和激励政策应纳入低收入和中等收入国家数字健康计划的实施中。
