Application of digital health technologies in upper limb motor function rehabilitation after stroke from 2015 to 2025: a bibliometric analysis
10.3969/j.issn.1006-9771.2026.05.004
- VernacularTitle:2015年至2025年数字健康技术在脑卒中上肢运动功能康复中应用的文献计量分析
- Author:
Rui LIU
1
;
Zhenmei GAO
2
;
Xingyu ZHOU
1
;
Qi ZHANG
1
;
Jianlin WU
2
Author Information
1. College of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Ji'nanShandong 250355, China
2. College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Ji'nanShandong 250355, China
- Publication Type:Journal Article
- Keywords:
stroke;
upper limb motor function;
digital health technology;
bibliometrics
- From:
Chinese Journal of Rehabilitation Theory and Practice
2026;32(5):534-549
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo analyze the current research status, hotspots and future trends of the application of digital health technology (DHT) in the rehabilitation of upper limb motor function after stroke. MethodsRelevant literature on the application of DHT in upper limb motor rehabilitation for stroke patients published between January, 2015 and December, 2025 was retrieved from Web of Science Core Collection, CNKI and Wanfang database. CiteSpace 6.4.R1 was used for visualized bibliometric analysis. ResultsA total of 1 295 publications were included, comprising 454 in Chinese and 841 in English. The annual number of publications generally showed an upward trend. China ranked first in publication output in English literature. The institutions with the highest numbers of publications were Huashan Hospital Affiliated to Fudan University and Swiss Federal Institute of Technology in Zurich. Both Chinese and English keywords formed ten clustering groups. Chinese clusters mainly involved occupational therapy, neural mechanisms and home-based rehabilitation, whereas English clusters focused on virtual reality, brain-computer interfaces and machine learning. High-frequency keywords included virtual reality, brain-computer interface, machine learning and deep learning. Chinese keywords with a strong burst included rehabilitation training, while deep learning showed a strong burst in English keywords. Stroke was the most frequently cited journal. Highly cited journals covered multiple disciplines, including rehabilitation medicine, neuroscience and computer science, reflecting the interdisciplinary characteristics of this field. ConclusionResearches on DHT for upper limb motor function rehabilitation in stroke are increasing annually, focusing on core interaction technologies, neural mechanism and artificial intelligence. Future research trends may include inter-disciplinary integration of artificial intelligence with core rehabilitation technologies, neuroimaging-guided targeted interventions, optimisation of home-based rehabilitation systems, and development of multidimensional quantitative assessment models.
