Research Progress on Risk Prediction Models of Physical Restraint in the Elderly: a Narrative Review

Jian Huang¹Shaoyi Tao²Hui Li³Juan Li^1*

• ¹University of Science and Technology of China, Hefei, China
• ²Second People's Hospital of Hefei, Hefei, China
• ³People's Hospital of Zongyang County, Tongling, China

This review presents the advancements in research on risk prediction models for physical restraint among the elderly. As the global population ages, the issue of physical restraint in older adults has become increasingly prominent, making accurate risk prediction essential for enhancing their quality of life. Current Status: Physical restraint rates exhibit marked regional disparities (e.g., 84.9% in Spain vs. 1.9% in the US). Key risk factors include age ≥75, dementia, and agitation. Machine learning models achieve higher accuracy than traditional statistical approaches, but hybrid models better balance precision and interpretability. Future Directions: (1) Developing real-time monitoring systems via sensor technology; (2) Establishing ethical frameworks for model deployment through clinician-data scientist partnerships; (3) Implementing validated tools in clinical settings to minimize restraint use. Finally, the review emphasizing the need for improved methodologies and the integration of interdisciplinary approaches to better address this complex issue.