International Journal of Clinical Case Reports, 2025, Vol.15, No.5, 200-208 http://medscipublisher.com/index.php/ijccr 204 enhance the accuracy and objectivity of predictions. Some models have good prediction effects (AUC values can reach 0.84), and can simultaneously incorporate multiple risk factors such as physiology, psychology, and environment (Yu et al., 2025). In addition, international research also focuses on developing methods that comprehensively consider multiple factors, integrating questionnaires, self-reports, physical fitness tests and sensor data. This approach helps to more accurately identify high-risk populations, promote targeted interventions, and emphasize cross-cultural applicability and external validation, thereby enhancing the universal applicability of research results (Chen et al., 2022; Chen et al., 2023). 5.2 Limitations and challenges of domestic research In China, research on fall risk assessment has also made certain progress, especially in developing tools that are more suitable for the local population, such as the Self-Assessment Form for Fall Risk (FRSAS) and predictive models based on national data (such as CHARLS). These tools have demonstrated excellent reliability and practicality in community applications. In recent years, there have also been studies attempting to enhance the predictive ability by leveraging machine learning (Chen et al., 2023; Liang et al., 2025). However, many studies still have problems such as limited sample size, concentration in a single region, and lack of external validation, which affect the generalization and practical application value of the results (Wang et al., 2022). On the other hand, the deficiency is that compared with foreign countries, the application of advanced technologies and multi-factor comprehensive analysis in China is still not extensive enough. Although there have been attempts in sensors and machine learning, the overall application level still needs to be improved. In the future, more complete models need to be constructed to incorporate risk factors such as the environment and social psychology. Furthermore, the lack of unified intervention standards in various regions has also restricted the promotion of research results in practice (Xia et al., 2022). 5.3 Similarities and differences between chinese and western research achievements and their implications Both Western and Chinese research hold that the risk of falls is the result of multiple factors working together and emphasize the importance of comprehensive assessment tools. Both attach importance to key risk points such as physical function, cognitive level, depression and environmental factors, and both have proved that combining self-reporting with objective measurement can improve the accuracy of prediction (Chen et al., 2023; Yu et al., 2025). There are still differences between domestic and foreign countries in terms of technology application, research scope and standardization. International research began to use wearable sensors, artificial intelligence technology and conduct large-scale validation earlier, while China has made progress in developing tools suitable for local needs and building models using national databases (Liang et al., 2025). These differences indicate that it is necessary to enhance cross-cultural collaboration, promote the validation of local tools among a broader population, and introduce advanced technologies more widely within the country, thereby improving the accuracy and applicability of fall risk assessment for elderly people in the community. 6 Existing Problems and Challenges 6.1 Insufficient adaptability of tools: cultural and demographic differences Many fall risk assessment methods were originally designed and validated among Western populations, and thus have limited adaptability when applied to different cultures or groups. These tools often have difficulty reflecting the risks brought about by differences in lifestyle, environmental conditions and health concepts among different populations, especially among the elderly in communities in non-Western countries (Strini et al., 2021; Ong et al., 2022). Therefore, when these tools are used outside the original environment, their predictive accuracy and relevance may decline, and they are also prone to overlook important risk factors in specific communities. Most of the existing assessment tools focus more on physical functions and structural aspects, while giving relatively insufficient consideration to factors such as the environment, personal habits and cultural background.
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