February 16, 2025
Advanced Applications in Chronic Disease Monitoring Using IoT Mobile Sensing Device Data, Machine Learning Algorithms and Frame Theory: A Systematic Review

REVIEW article

Front. Public Health

Sec. Digital Public Health

Volume 13 – 2025 |
doi: 10.3389/fpubh.2025.1510456

This article is part of the Research Topic Leveraging Information Systems and Artificial Intelligence for Public Health Advancements View all 3 articles

Provisionally accepted

  • 1 Hefei University of Technology, Hefei, China
  • 2 Beijing Xiaotangshan Hospital, Beijing, China

The final, formatted version of the article will be published soon.

    The escalating demand for chronic disease management has presented substantial challenges to traditional methods. However, the emergence of Internet of Things (IoT) and artificial intelligence (AI) technologies offers a potential resolution by facilitating more precise chronic disease management through data-driven strategies. This review concentrates on the utilization of IoT mobile sensing devices in managing major chronic diseases such as cardiovascular diseases, cancer, chronic respiratory diseases, and diabetes. It scrutinizes their efficacy in disease diagnosis and management when integrated with machine learning algorithms, such as ANN, SVM, RF, and deep learning models. Through an exhaustive literature review, this study dissects how these technologies aid in risk assessment, personalized treatment planning, and disease management. This research addresses a gap in the existing literature concerning the application of IoT and AI technologies in the management of specific chronic diseases. It particularly demonstrates methodological novelty by introducing advanced models based on deep learning, tight frame-based methodologies and real-time monitoring systems. This review employs a rigorous examination method, which includes systematically searching relevant databases, filtering literature that meets specific inclusion and exclusion criteria, and adopting quality assessment tools to ensure the rigor of selected studies. This study identifies potential biases and weaknesses related to data collection, algorithm selection, and user interaction. The research demonstrates that platforms integrating IoT and machine learning algorithms for chronic disease monitoring and management are not only technically viable but also yield substantial economic and social advantages in real-world applications. Future studies could investigate the use of quantum computing for processing vast medical datasets and novel techniques that merge biosensors with nanotechnology for drug delivery and disease surveillance. Furthermore, This underscores its potential and value as a future model for chronic disease management.

    Keywords:
    The internet of things, artificial intelligence, machine learning, deep learning, Chronic Disease

    Received:
    13 Oct 2024;
    Accepted:
    20 Jan 2025.

    Copyright:
    © 2025
    Liu and Wang. This is an open-access article distributed under the terms of the
    Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted,
    provided the original author(s) or licensor are credited and that the
    original publication in this journal is cited, in accordance with accepted
    academic practice. No use, distribution or reproduction is permitted which
    does not comply with these terms.

    * Correspondence:
    Boyuan Wang, Beijing Xiaotangshan Hospital, Beijing, China

    Disclaimer:
    All claims expressed in this article are solely those of the authors and
    do not necessarily represent those of their affiliated organizations, or
    those of the publisher, the editors and the reviewers. Any product that
    may be evaluated in this article or claim that may be made by its
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