Author/Authors :
Chen, Yalan Department of Medical Informatics - School of Medicine - Nantong University - Nantong, China , Yu, Yijun Nantong University - Nantong, China , Lin, Xin University of Technology Sydney - Sydney, Australia , Han, Zhenwei Nantong University - Nantong, China , Feng, Zhe Department of Medical Informatics - School of Medicine - Nantong University - Nantong, China , Hua, Xinyi Department of Medical Informatics - School of Medicine - Nantong University - Nantong, China , Chen, Dongliang Nantong University - Nantong, China , Xu, Xiaotao Department of Medical Informatics - School of Medicine - Nantong University - Nantong, China , Zhang, Yuanpeng Department of Medical Informatics - School of Medicine - Nantong University - Nantong, China , Wang, Guheng Department of Hand Surgery - Affiliated Hospital of Nantong University - Nantong, China
Abstract :
To systematically analyze the existing intelligent rehabilitation mobile applications (APPs) related to distal radius
fracture (DRF) and evaluate their features and characteristics, so as to help doctors and patients to make evidence-based choice
for appropriate intelligent-assisted rehabilitation. Methods. Literatures which in regard to the intelligent rehabilitation tools of
DRF were systematic retrieved from the PubMed, the Cochrane library, Wan Fang, and VIP Data. The effective APPs were
systematically screened out through the APP markets of iOS and Android mobile platform, and the functional characteristics of
different APPs were evaluated and analyzed. Results. A total of 8 literatures and 31 APPs were included, which were divided into
four categories: intelligent intervention, angle measurement, intelligent monitoring, and auxiliary rehabilitation games. These
APPs provide support for the patients’ home rehabilitation guidance and training and make up for the high cost and space
limitations of traditional rehabilitation methods. The intelligent intervention category has the largest download ratio in the APP
market. Angle measurement tools help DRF patients to measure the joint angle autonomously to judge the degree of
rehabilitation, which is the most concentrated type of literature research. Some of the APPs and tools have obtained good
clinical verification. However, due to the restrictions of cost, geographic authority, and applicable population, a large number of
APPs still lack effective evidence to support popularization. Conclusion. Patients with DRF could draw support from different
kinds of APPs in order to fulfill personal need and promote self-management. Intelligent rehabilitation APPs play a positive role
in the rehabilitation of patients, but the acceptance of the utilization for intelligent rehabilitation APPs is relatively low, which
might need follow-up research to address the conundrum.
