Emerging Digital Technologies for Monitoring and Rehabilitation Support in Chronic Dust-Induced Lung Diseases: A Scoping Review

Our plain-English summary of Emerging Digital Technologies for Monitoring and Rehabilitation Support in Chronic Dust-Induced Lung Diseases: A Scoping Review, by N. Sagandykova, M. Baurzhan, A. Gulyayev, et al (Journal of Personalized Medicine, 2026).

The study

• Authors: N. Sagandykova, M. Baurzhan, A. Gulyayev, S. Kairgeldina, S. Sergazy, G. Daniyarova, K. Absattarova, L. Kovalenko, A. Izbassarova
• Journal: Journal of Personalized Medicine
• Published: 2026;16(8)
• Type of article: Review
• Read it: PubMed · Publisher · Free full text

Abstract

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.

In plain English

This scoping review mapped published research rather than collecting new patient data. They searched three databases up to 29 June 2026 for studies of digital monitoring or rehabilitation tools for chronic non-malignant dust-induced lung diseases such as silicosis, asbestosis and pneumoconiosis. Five publications were included. Three analyzed wearable chest-patch data: an activity dataset of 25 patients with pneumoconiosis and 20 healthy adults, and a cough dataset of 25 patients and 25 healthy volunteers. One study recognized activities such as sitting and walking with 94.25% accuracy in patients. A second reanalyzed that activity dataset. The cough study recognized cough with 96.1% accuracy under controlled testing. A retrospective study of 4220 pneumoconiosis patients from a Chongqing rehabilitation platform built a complication model the authors rated as moderately able to tell groups apart. One study searched hospital records of patients with pulmonary asbestosis for links between symptoms, complications and readmissions; its sample size was poorly described. The authors concluded that these technologies remain at an early stage, supported by a small, heterogeneous evidence base that did not establish improved clinical or rehabilitation outcomes.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Scoping review following PRISMA-ScR and Joanna Briggs Institute methodology, with narrative synthesis (no meta-analysis)
• Who was studied: Five publications selected from 383 records found in PubMed, Scopus and Web of Science, searched from database inception to 29 June 2026 without language restrictions. The included studies covered patients with pneumoconiosis (wearable and platform studies) and patients with pulmonary asbestosis (one electronic medical record study).
• Exposure: Digital technologies (wearable sensors, a digital rehabilitation platform, electronic medical record analysis) used for monitoring or rehabilitation in patients with chronic non-malignant dust-induced lung diseases. No asbestos fiber types or exposure doses were studied.
• Main results:
  • Ren et al. (25 patients with pneumoconiosis, 20 healthy controls): a wearable patch combining an accelerometer and ECG, analyzed with a CNN–LSTM model, classified basic activity states in patients with 94.25% accuracy under controlled conditions.
  • Mekruksavanich et al. reanalyzed the same dataset without collecting new clinical data. Their BiGRU model reached 97.14% accuracy in the pneumoconiosis patient group. The reviewers counted this as a secondary analysis, not an independent clinical validation.
  • Wang et al. (25 patients with pneumoconiosis, 25 healthy volunteers): a chest patch with an accelerometer and a Random Forest model recognized cough in the patient group with 96.1% accuracy, 95.0% precision and 97.4% recall, under controlled testing conditions.
  • Zhang et al. (retrospective, 4220 patients with occupational pneumoconiosis, 1872 with complications and 2348 without): the XGBoost complication model had moderate discriminatory ability, with AUC 0.718, accuracy 0.666, sensitivity 0.547 and specificity 0.763.
  • Song et al. analyzed 4 years of inpatient electronic medical records of patients with pulmonary asbestosis with an association-rule algorithm, finding rules linking symptoms, comorbidities and readmission intervals. The sample size was poorly described and no external or clinical validation was performed.
  • All three wearable publications represented early technical or algorithmic validation. None assessed adherence, clinical deterioration, hospitalization or rehabilitation outcomes.
• The authors' conclusion: The authors concluded that digital technologies for monitoring and rehabilitation follow-up in chronic dust-induced lung diseases remain at an early stage. They found that current studies do not establish that these technologies improve rehabilitation outcomes or clinical decision-making, and they called for prospective real-world studies.
• Limitations the authors state:
  • Only a small number of eligible publications were found.
  • The included studies were heterogeneous, which prevented quantitative synthesis.
  • Part of the evidence came from conference proceedings or secondary analyses, and one publication used a dataset overlapping with another included study.
  • Most included studies were early-stage or retrospective and conducted in controlled or non-prospective settings, so they cannot show clinical effectiveness.
  • No formal risk-of-bias assessment was performed.

Topics

Medical terms in this study, explained in our Lexicon: Pneumoconiosis, Systematic Review.

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