Table 1_Mobile health (mHealth) technologies for fall prevention among older adults in low-middle income countries: bibliometrics, network analysis and integrative review.docx

IntroductionmHealth technologies offer promising solutions to reduce the incidence of falls among older adults. Unfortunately, publications on their application to Low-Middle Income Countries (LMIC) settings have not been collectively examined.
MethodsA triadic research design involving bibliometrics, network analysis, and model-based integrative review was conducted to process articles (n = 22) from 629 publications extracted from major databases using keywords related to mHealth, falls prevention, and LMIC. The web-based application Covidence and stand-alone VosViewer software were used to process data following previously published review standards.
ResultsPublished articles in the field feature multidisciplinary authorships from multiple scholars in the domains of health and technology. Network analysis revealed the most prominent stakeholders and keyword clusters related to mHealth technology features and applications in healthcare. The papers predominantly focused on the development of mHealth technology, usability, and affordances and less on the physiologic and sociologic attributes of technology use. mHealth technologies in low and middle-income countries are mostly smartphone-based, static, and include features for home care settings with fall detection accuracy of 86%–99.62%. Mixed reality-based mobile applications have not yet been explored.
ConclusionOverall, key findings and information from the articles highlight a gradually advancing research domain. Outcomes reinforce the need to expand the focus of mHealth investigations to include emerging technologies, update current technology models, create a more human-centered technology design, test mHealth technologies in the clinical setting, and encourage continued cooperation between and among researchers from various fields and environments.

引言：移动健康（mHealth）技术为降低老年人跌倒发生率提供了极具前景的解决方案。然而，目前尚未有研究对其在中低收入国家（LMIC）场景中的应用相关文献进行系统性梳理。
方法：本研究采用文献计量学、网络分析与基于模型的整合性综述相结合的三元研究设计，对从主流数据库中检索得到的629篇文献进行筛选，最终纳入22篇与移动健康、跌倒预防及中低收入国家相关的文献。本研究遵循已发表的综述规范，使用基于网页的Covidence文献管理工具与独立的VosViewer可视化软件进行数据处理。
结果：该领域已发表文献的作者团队涵盖健康与技术等多个学科领域。网络分析揭示了该领域最具影响力的利益相关方，以及与移动健康技术特性及医疗应用相关的关键词聚类。现有研究主要聚焦于移动健康技术的开发、可用性与可供性，对技术使用的生理学与社会学属性关注较少。中低收入国家的移动健康技术多基于智能手机平台，功能相对固定，且多用于家庭护理场景，跌倒检测准确率介于86%至99.62%之间。目前尚未有针对基于混合现实（Mixed Reality）的移动应用的相关研究。
结论：综上，纳入文献的关键发现与相关信息表明，该研究领域正逐步发展。研究结果凸显了拓展移动健康研究方向的必要性：应将新兴技术纳入研究范畴，更新现有技术模型，构建更加以人为本的技术设计方案，在临床场景中开展移动健康技术测试，并鼓励不同领域与环境的研究者之间持续开展合作。