Dino Michael Joseph, Thiamwong Ladda, Xie Rui, Malacas Ma Kristina, Hernandez Rommel, Balbin Patrick Tracy, Vital Joseph Carlo, Rivero Jenica Ana, Xi Vivien Wu
College of Nursing, University of Central Florida, Orlando, FL, United States.
Research Development and Innovation Center, Our Lady of Fatima University, Valenzuela, Philippines.
Front Digit Health. 2025 Mar 28;7:1559570. doi: 10.3389/fdgth.2025.1559570. eCollection 2025.
mHealth 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.
A triadic research design involving bibliometrics, network analysis, and model-based integrative review was conducted to process articles ( = 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.
Published 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.
Overall, 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.
移动健康技术为降低老年人跌倒发生率提供了有前景的解决方案。遗憾的是，关于其在低收入和中等收入国家（LMIC）环境中的应用的出版物尚未得到综合审视。
采用文献计量学、网络分析和基于模型的综合综述的三元研究设计，对从主要数据库中提取的629篇出版物中的22篇文章进行处理，这些文章使用了与移动健康、跌倒预防和低收入和中等收入国家相关的关键词。基于网络的应用程序Covidence和独立的VosViewer软件用于按照先前发表的综述标准处理数据。
该领域已发表的文章具有来自健康和技术领域的多位学者的多学科作者身份。网络分析揭示了与移动健康技术在医疗保健中的特征和应用相关的最突出的利益相关者和关键词簇。这些论文主要关注移动健康技术的开发、可用性和可承受性，而较少关注技术使用的生理和社会学属性。低收入和中等收入国家的移动健康技术大多基于智能手机，是静态的，包括家庭护理设置的功能，跌倒检测准确率为86%-99.62%。基于混合现实的移动应用尚未得到探索。
总体而言，文章中的关键发现和信息突出了一个逐渐发展的研究领域。研究结果强化了扩大移动健康研究重点的必要性，以包括新兴技术、更新当前技术模型、创建更以人为本的技术设计、在临床环境中测试移动健康技术，并鼓励来自不同领域和环境的研究人员之间持续合作。
