# Research progress on risk prediction models of physical restraint in the elderly: a narrative review

## TL;DR

This paper reviews progress in predicting physical restraint risks in elderly patients, highlighting regional differences and the potential of machine learning.

## Contribution

The paper emphasizes hybrid models and interdisciplinary approaches for better prediction and ethical deployment of restraint risk tools.

## Key findings

- Machine learning models outperform traditional methods in predicting physical restraint risks.
- Hybrid models offer a balance between precision and interpretability in risk prediction.
- Sensor technology and clinician partnerships are proposed for future real-time monitoring and ethical frameworks.

## Abstract

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.