JMIR Publications

ABSTRACT

Background:

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide, with frequent exacerbations of COPD (ECOPD) significantly impacting patient health and healthcare systems. Predicting ECOPD early would not only increase quality of life for patients but also decrease the economic burden. The advancement of wearable technologies and IoT (Internet of Things) sensors has enabled continuous remote monitoring, offering new opportunities for early ECOPD prediction. However, effectively leveraging wearable data requires robust AI frameworks capable of processing heterogeneous physiological and environmental information.

Objective:

This systematic review aims to provide a comprehensive overview of both hardware and software solutions for predicting ECOPD using remote monitoring. From the reviewed literature, we first focus on the key physiological and environmental variables essential for COPD monitoring, that can be extracted from wearables and IoT sensors. Second, we describe the wearable and IoT devices currently deployed in COPD management. Finally, we review machine learning, including deep learning models, employed for ECOPD prediction, discussing limitations for real-world implementation. By bridging AI-driven data processing with real-world sensor applications, this review aims to outline the current landscape, existing challenges and future directions for developing effective remote monitoring solutions for ECOPD predictions.

Methods:

A comprehensive search was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies using AI/Machine Learning techniques for predicting ECOPD, in in-home contexts.

Results:

This review identified 26 studies that met the inclusion criteria. Twenty studies aimed at predicting exacerbations in advance (several days before onset) and/or detecting them at the moment of onset. The variables tracked most frequently were heart rate (n=9), peripheral oxygen saturation (n=9), and symptoms (n=8). Daily or weekly sampling was most common (n=14). Most studies (n=13) applied machine learning models—primarily random forest (n=5), CatBoost (n=2), decision trees (n=2), and support vector machines (n=2). Deep learning was used in three papers, while the remaining applied rule-based logics and probabilistic models. Wearables and IoT were employed in only six out of 20 studies. Six papers analyzed changes in vital parameters during prodromal phases, defined as the period shortly before the onset of an exacerbation. Three studies collected data continuously, two daily, and one compared once-daily versus overnight monitoring; four of these six used wearable devices.

Conclusions:

Overall, current evidence highlights the potential of continuous monitoring of physiological and environmental variables for early ECOPD prediction, offering advantages over questionnaires or once-daily measurements. While wearables and IoT devices show promise, their use remains limited. Many studies rely on balanced datasets that do not mirror real-world exacerbation patterns and lack external validation across diverse populations. Future research should emphasize large-scale validation, integration of multimodal data, and translation of AI models into clinically feasible tools to enable timely intervention and improve COPD management. Clinical Trial: This systematic review was registered with PROSPERO: CRD420251051302.