Digital Health Strategies for Early Detection of COPD Exacerbations in Rural Areas: A Narrative Review

DOI

10.2991/978-94-6239-770-5_3How to use a DOI?

Keywords

COPD; Digital health; Exacerbation detection; Rural health; Telemonitoring

Abstract

Exacerbations of chronic obstructive pulmonary disease (COPD) are often recognized only after symptoms become severe enough to require urgent care. Digital approaches may support earlier detection through symptom reporting, home physiological monitoring, mobile decision support, and wearable activity data, although most evidence comes from settings unlike rural communities. This narrative review examined digital strategies for COPD screening, clinical deterioration, and pre-exacerbation periods, including requirements for rural implementation. Literature published from 2020 to 2025 was searched in PubMed, SAGE, Scopus, ProQuest, ScienceDirect, ClinicalKey, and Google Scholar using PICO-based terms. Seven original studies covered telemedicine, telemonitoring, mobile expert systems, electronic diaries, wearable devices, smartphone self-management, and app-based pulmonary rehabilitation. Early detection included identifying probable COPD, detecting worsening symptoms or physiological changes before escalation, and recognizing periods preceding a documented exacerbation or hospital visit. Definitions and purposes varied across studies. Continuous respiratory monitoring identified some pre-exacerbation periods, whereas mean daily respiratory rate alone performed less well. Electronic diaries and wrist accelerometers detected symptom and activity changes around exacerbations. A mobile expert system matched expert diagnoses in 86 of 100 medical-record cases but did not report sensitivity or specificity. Telemedicine and smartphone interventions mainly supported adherence, self-monitoring, self-efficacy, and rehabilitation access rather than automated prediction. Only one study involved a rural telemonitoring project. Digital health may strengthen COPD screening and follow-up, but evidence remains insufficient to show that automated prediction reduces hospitalization or mortality. Rural implementation requires reliable connectivity, device and literacy support, data governance, and services able to respond to alerts.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ary Septri Mulainy
AU  - Hiskia Hiskia
PY  - 2026
DA  - 2026/09/04
TI  - Digital Health Strategies for Early Detection of COPD Exacerbations in Rural Areas: A Narrative Review
BT  - Proceedings of the 6th International Nursing and Health Sciences Symposium (INHSS 2025)
PB  - Atlantis Press
SP  - 15
EP  - 22
SN  - 2468-5739
UR  - https://doi.org/10.2991/978-94-6239-770-5_3
DO  - 10.2991/978-94-6239-770-5_3
ID  - Mulainy2026
ER  -