老年人跌倒风险评估工具与干预策略的研究进展

Research Progress on Fall Risk Assessment Tools and Intervention Strategies for Older Adults

摘要: 目的：系统梳理老年人跌倒风险评估工具及预防策略的研究进展，为临床护理实践提供循证依据。方法：检索PubMed、Web of Science、Cochrane Library及中国知网等数据库，纳入近年来关于老年人跌倒风险评估与预防的相关文献，对风险因素、评估工具及干预策略进行归纳分析。结果：老年人跌倒是多因素共同作用的结果，主要包括个体因素、环境因素及行为因素。现有评估工具如Morse跌倒风险评估量表、Berg平衡量表、Hendrich II模型等各具特色，但在预测效度和适用性方面存在差异。预防策略以运动干预、环境改造、药物管理及辅助设备应用为主，其中运动干预证据最为充分。近年来，人工智能、可穿戴设备及虚拟现实技术在跌倒风险监测与干预中展现出良好的应用前景。结论：现有跌倒风险评估工具尚难全面覆盖多维风险因素，联合评估及个体化干预是发展趋势。未来应加强多源数据整合与智能预测模型构建，推动技术转化与临床应用，以提升老年人跌倒预防效果。

Abstract: Objective: To systematically review the research progress on fall risk assessment tools and prevention strategies in older adults, and to provide evidence-based references for clinical nursing practice. Methods: Relevant literature on fall risk assessment and prevention in older adults was retrieved from PubMed, Web of Science, Cochrane Library, and China National Knowledge Infrastructure (CNKI). The included studies were analyzed to summarize risk factors, assessment tools, and intervention strategies. Results: Falls in older adults result from multiple interacting factors, mainly including individual, environmental, and behavioral factors. Commonly used assessment tools, such as the Morse Fall Risk Scale, Berg Balance Scale, and Hendrich II Fall Risk Model, have their own characteristics but differ in predictive validity and applicability. Prevention strategies primarily include exercise interventions, environmental modification, medication management, and the use of assistive devices, among which exercise interventions have the strongest evidence. In recent years, artificial intelligence, wearable devices, and virtual reality technologies have shown promising applications in fall risk monitoring and intervention. Conclusion: Existing fall risk assessment tools are insufficient to comprehensively cover multidimensional risk factors. Combined assessment and individualized interventions represent future trends. Further efforts are needed to integrate multi-source data and develop intelligent predictive models, as well as to promote the translation of technologies into clinical practice to improve fall prevention outcomes in older adults.