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A Systematic Review of Wearable Patient Monitoring Systems–Current Challenges and Opportunities for Clinical Adoption
Abstract
The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and 'silo' solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.
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In the citing paper
“Wearable technology enables remote monitoring of patients and personalized care.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [4]
In the citing paper
“In addition, they can significantly reduce healthcare costs by reducing the number of hospitalizations and emergency room visits, which can be expensive for both individuals and the healthcare system.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [4]
In the citing paper
“For instance, these algorithms were employed to predict the onset of chronic diseases, such as diabetes, cardiovascular disease, and mental health issues, by measuring heart rate and blood glucose meters [4,186].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [4]
In the citing paper
“Integration with smartphones and mobile devices enhances the accessibility of wearable ECG monitors for real-time monitoring, data storage, and cloud-based analytics, enabling seamless integration into telemedicine and remote patient monitoring systems [4,16,17].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [4]
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Metadata
- Authors
- Baig, M. M., GholamHosseini, H., Moqeem, A. A., Mirza, F., & Lindén, M.
- Affiliations
- Auckland University of Technology (NZ)
- Corresponding author
- Baig, Mirza Mansoor
- Venue
- Journal of Medical Systems
- Published
- 2017-06-19
- Volume
- 41
- Issue
- 7
- Pages
- 115-115
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W2625326488
- Cited by
- 351
- References
- 64
- Citations by year
- 2026: 30 · 2025: 52 · 2024: 37 · 2023: 36 · 2022: 38 · 2021: 54 · 2020: 50 · 2019: 27 · 2018: 22 · 2017: 5
- FWCI
- 2.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medical Systems (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- wearable sensors, clinical adoption, interoperability, clinical usability, chronic disease monitoring, rehabilitation, cardiovascular disease, fall detection, mental health monitoring, wireless data collection, communication platforms, machine learning in healthcare, artificial intelligence in healthcare
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Humans, Monitoring, Physiologic, Software
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