International Journal of Clinical Case Reports, 2025, Vol.15, No.5, 200-208 http://medscipublisher.com/index.php/ijccr 203 to continuously optimize and validate these tools in different community groups to ensure their accurate judgment and applicability (Meekes et al., 2021; Ong et al., 2022). 4.2 Sensitivity and specificity: the effect of identifying high-risk groups The commonly used fall risk assessment tools in the community show significant differences in sensitivity and specificity. Take BBS and TUG as examples. Their sensitivity in distinguishing whether there is a history of falls is approximately 61% to 67.5%, and their specificity is between 53% and 56.3%. Some more complex tools or those that rely on sensors can achieve higher sensitivity (such as up to 93.8%), but sometimes their specificity will decline accordingly (Table 1) (Wang et al., 2022; Alharbi, 2023). For example, the sensitivity and specificity of the EASY-Care criterion in predicting falls within 6 months were 76.6% and 87.5%, respectively (Shahrestanaki et al., 2022). These results indicate that certain tools are effective in identifying high-risk individuals, but currently no method can maintain both high sensitivity and high specificity in all community Settings simultaneously. Table 1 Comparative analysis of fall assessment score between participants with and without a history of fall (Adopted from Alharbi, 2023) - With a history of fall Without a history of fall P-value¹ Berg Balance Scale score <0.0001 Mean/standard deviation 34.44/16.04 42.78/11.58 - Five Times Sit-to-Stand test score 0.56 Mean/standard deviation 36.77/1103 29.38/10.32 - Timed Up and Go test score 0.07 Mean/standard deviation 24.46/8.82 18.66/6.97 - Table caption: 1 Post-test accuracy (%) was calculated using the selected cut-off score Furthermore, the predictive power relying solely on questionnaires is generally lower than that of methods based on sports tests or sensor data. If self-reported risk factors are combined with physical examination or sensor information, high-risk groups can be identified more accurately. However, due to the relatively complex operation and high resource requirements, such methods may be difficult to be widely implemented in some communities (Kulkarni, 2022; Mourad-Chehade et al., 2023). 4.3 Feasibility and potential for community use When promoting in the community, operability is an important consideration. Most of the assessment tools for the elderly in the community are relatively simple, requiring only a small amount of equipment. Even non-professionals and the elderly themselves can complete them. Therefore, these tools are very practical in both large-scale screening and daily use (Vilpunaho et al., 2023). "Remain Independent", FRSAS and some mobile application-based tools (such as FallSA) have good acceptance among the elderly, are easy to operate and have a high completion rate (Ong et al., 2022). However, some action-based tools (such as BBS and TUG) require more time, space or professional support, which may be limited in some communities (Meekes et al., 2021). Sensors and application tools have advantages in terms of accuracy and scalability, but their popularization is limited by technical conditions and usage habits. Overall, the most suitable tool should strike a balance among simplicity, operability and predictive effect, and be selected based on the actual resources and needs of the community (Singh et al., 2021; Wang et al., 2022). 5 Comparison of Research Progress at Home and Abroad 5.1 Main characteristics of tool application in international research International research on the risk assessment of falls among the elderly generally employs validated standardized tools, such as the Morse Fall Scale, Tinetti Balance Assessment, and the Hendrich II Fall Risk Model, etc. These tools are commonly found in large-scale, multi-center studies, and their reliability, validity, sensitivity and specificity have been tested in different populations and scenarios (Figure 1) (Liang et al., 2025). The current research trend focuses on introducing new technologies, such as wearable sensors and machine learning, to
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