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Emerging Digital Technologies for Monitoring and Rehabilitation Support in Chronic Dust-Induced Lung Diseases: A Scoping Review
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DOI：10.3390/jpm16080407.png)
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Abstract
En 中文
Background/Objectives: Chronic dust-induced lung diseases, including pneumoconiosis and asbestosis, require long-term monitoring and individualized management. Conventional rehabilitation and follow-up often depend on in-person visits and may not provide continuous assessment outside clinical settings. This scoping review mapped evidence on digital technologies used to monitor patients, assess respiratory symptoms and functional status, and support rehabilitation follow-up in chronic dust-induced lung diseases. Methods: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Joanna Briggs Institute methodology. PubMed, Scopus, and Web of Science were searched from inception to 29 June 2026 without language restrictions. Five eligible publications were included. Results: The evidence was grouped into three categories: wearable monitoring of activity and respiratory symptoms, analysis of data from a digital rehabilitation platform, and electronic medical record (EMR) analysis. Wearable sensors showed high performance in recognizing basic activity states and cough events under controlled conditions. Platform- and EMR-based approaches showed potential for using clinical and functional data to support patient stratification and monitoring. However, most studies were limited to early technical or algorithmic validation and did not assess long-term home use, patient adherence, integration into clinical workflows, or clinical and rehabilitation outcomes. Conclusions: Digital technologies may support objective monitoring and rehabilitation follow-up in chronic dust-induced lung diseases, but the evidence base remains small and clinically immature. Prospective studies in real-world settings should evaluate usability, external validity, workflow integration, and clinically meaningful outcomes.
Keywords:
chronic dust-induced lung diseases
pneumoconiosis
wearable devices
digital monitoring
machine learning
rehabilitation
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