Cisek, Katryna and Kelleher, John D. (2023) Current Topics in Technology-Enabled Stroke Rehabilitation and Reintegration: A Scoping Review and Content Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31. pp. 3341-3352. ISSN 1534-4320
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Abstract
Background. There is a worldwide health crisis stemming from the rising incidence of various debilitating chronic diseases, with stroke as a leading contributor.
Chronic stroke management encompasses rehabilitation
and reintegration, and can require decades of personalized medicine and care. Information technology (IT) tools
have the potential to support individuals managing chronic
stroke symptoms. Objectives. This scoping review identifies prevalent topics and concepts in research literature
on IT technology for stroke rehabilitation and reintegration,
utilizing content analysis, based on topic modelling techniques from natural language processing to identify gaps
in this literature. Eligibility Criteria. Our methodological
search initially identified over 14,000 publications of the last
two decades in the Web of Science and Scopus databases,
which we filter, using keywords and a qualitative review, to a
core corpus of 1062 documents. Results. We generate a 3-
topic, 4-topic and 5-topic model and interpret the resulting
topics as four distinct thematics in the literature, which
we label as Robotics, Software, Functional and Cognitive.
We analyze the prevalence and distinctiveness of each thematic and identify some areas relatively neglected by the
field. These are mainly in the Cognitive thematic, especially
for systems and devices for sensory loss rehabilitation,
tasks of daily living performance and social participation.
Conclusion. The results indicate that IT-enabled stroke literature has focused on Functional outcomes and Robotic
technologies, with lesser emphasis on Cognitive outcomes
and combined interventions. We hope this review broadens
awareness, usage and mainstream acceptance of novel
technologies in rehabilitation and reintegration among clinicians, carers and patients.
Item Type: | Article |
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Keywords: | Stroke; rehabilitation; reintegration; information technology; artificial intelligence; topic modeling; scoping review; content analysis; |
Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
Item ID: | 17462 |
Identification Number: | 10.1109/TNSRE.2023.3304758 |
Depositing User: | John Kelleher |
Date Deposited: | 24 Aug 2023 10:38 |
Journal or Publication Title: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Related URLs: | |
URI: | https://mural.maynoothuniversity.ie/id/eprint/17462 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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