Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Department of Physical Therapy, Rehabilitation Science, and Athletic Training, University of Kansas Medical Center, Kansas City, Kansas, USA.
J Gerontol A Biol Sci Med Sci. 2023 May 11;78(5):861-868. doi: 10.1093/gerona/glac116.
Falls are the leading cause of accidental death in older adults that result from a complex interplay of risk factors. Recently, the need for person-centered approach utilizing personalization, prediction, prevention, and participation, known as the P4 model, in fall prevention has been highlighted. Features of mobile technology make it a suitable technological infrastructure to employ such an approach. This narrative review aims to review the evidence for using mobile technology for personalized fall risk assessment and prevention since 2017 in older adults. We aim to identify lessons learned and future directions for using mobile technology as a fall risk assessment and prevention tool. Articles were searched in PubMed and Web of Science with search terms related to older adults, mobile technology, and falls prevention. A total of 23 articles were included. Articles were identified as those examining aspects of the P4 model including prediction (measurement of fall risk), personalization (usability), prevention, and participation. Mobile technology appears to be comparable to gold-standard technology in measuring well-known fall risk factors including static and dynamic balance. Seven applications were developed to measure different fall risk factors and tested for personalization, and/or participation aspects, and 4 were integrated into a falls prevention program. Mobile health technology offers an innovative solution to provide tailored fall risk screening, prediction, and participation. Future studies should incorporate multiple, objective fall risk measures and implement them in community settings to determine if mobile technology can offer tailored and scalable interventions.
跌倒已成为导致老年人意外死亡的主要原因，这是多种风险因素相互作用的结果。最近，人们强调需要采用以人为本的方法，利用个性化、预测、预防和参与，即 P4 模式，来预防跌倒。移动技术的特点使其成为采用这种方法的合适技术基础架构。本叙述性综述旨在回顾 2017 年以来利用移动技术对老年人进行个性化跌倒风险评估和预防的证据。我们旨在确定使用移动技术作为跌倒风险评估和预防工具的经验教训和未来方向。在 PubMed 和 Web of Science 中使用与老年人、移动技术和跌倒预防相关的搜索词搜索了文章。共纳入了 23 篇文章。这些文章被确定为研究 P4 模型各个方面的文章，包括预测（跌倒风险测量）、个性化（可用性）、预防和参与。移动技术在测量众所周知的跌倒风险因素（包括静态和动态平衡）方面似乎与黄金标准技术相当。开发了七种应用程序来测量不同的跌倒风险因素，并对个性化和/或参与方面进行了测试，其中 4 种应用程序已整合到跌倒预防计划中。移动健康技术为提供量身定制的跌倒风险筛查、预测和参与提供了创新的解决方案。未来的研究应纳入多种客观的跌倒风险测量方法，并在社区环境中实施这些方法，以确定移动技术是否可以提供量身定制和可扩展的干预措施。
