Cited by 121Non-Invasive Vital Sign MonitoringECG Monitoring and AnalysisArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 160 of the 161 references it cites were found in a registry or an index, and 1 are not checked yet; none of the 24 sources its claims cite is checked for support yet.

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Open access

Abstract

Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

Checked 2026-10-09 · 1 reference not checked yet

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This PRISMA-guided systematic review synthesizes 87 studies (2010–2022) on smart wearables for detecting and predicting cardiovascular diseases, reporting high feasibility and accuracy but major gaps in explainability, privacy, and multimodal data use. Interest has grown sharply since 2019, yet the authors conclude further research is needed before broad clinical adoption.

Key findings

  • The search identified 4002 records; after removing 1021 duplicates, 2981 remained, 599 were full-text screened, and 87 studies were included. 78% of included publications appeared in 2019 or later: 9 in 2019, 18 in 2020, 21 in 2021, and 21 in 2022, versus 19 in 2010–2018.
  • ECG was used in 69 of the systems; atrial fibrillation was the most studied disease, addressed in 39 of 87 studies. The PhysioNet MIT-BIH dataset was the most popular, used by 36 of 87 studies, while 25 studies used researchers’ private data.
  • 55 studies built custom devices; others used commercially available wearables or did not specify the device. One unspecified WBAN study achieved 96% accuracy, and the discussion notes several implementations reported accuracy over 99%.
  • None of the reviewed studies implemented explainable AI, federated learning, or multimodal machine learning; the most commonly used models were convolutional neural networks, support vector machines, long short-term memories, and decision trees.

Methods

  • Systematic literature review following PRISMA guidelines, searching IEEE, PubMed, and Scopus for peer-reviewed journal and conference papers in English, published between 2010 and 2022, addressing AI, smart wearables, healthcare, and cardiovascular diseases.
  • Three database-specific queries combined four concept blocks: AI, health, wearables, and CVDs; records were screened first by title/abstract, then by full text, leaving 87 eligible studies.
  • Data extracted from each study included publication year, diseases studied, vital signs recorded, wearable hardware, embedded intelligent models, datasets used, and performance metrics; results were analyzed descriptively by device type, year, vital signs, diseases, models, and datasets.

Limitations

  • Stated: The authors conclude that further research is needed to improve smart wearables for CVD management, citing challenges in data privacy and confidentiality, noise and artifacts, data diversity and heterogeneity, and user technology adoption and engagement.
  • Stated: None of the 87 reviewed studies used explainable AI, federated learning, or multimodal machine learning, which the authors argue limits trust, privacy protection, and the ability to analyze heterogeneous data.
  • Evident in the text: The search was performed in October 2021, yet the review claims coverage through 2022 and includes 2022 publications; the Scopus query also limits publication years to 2010–2021, creating an inconsistency. No formal risk-of-bias or study-quality appraisal is described.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/s23020828

Cites

160 of 161 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1 Increase in number of patients and deaths due to CVDs.
Figure 1 Increase in number of patients and deaths due to CVDs.CC BY · PubMed Central
Figure 2 Set of commonly known wearables.
Figure 2 Set of commonly known wearables.CC BY · PubMed Central
Figure 3 Flow of information through the different phases of a systematic review.
Figure 3 Flow of information through the different phases of a systematic review.CC BY · PubMed Central
Figure 4 Distribution of studies per year.
Figure 4 Distribution of studies per year.CC BY · PubMed Central
Figure 5 Diseases distribution per studies.
Figure 5 Diseases distribution per studies.CC BY · PubMed Central
Figure 6 Artificial intelligence, machine learning, and deep learning relation.
Figure 6 Artificial intelligence, machine learning, and deep learning relation.CC BY · PubMed Central
Figure 7 Training datasets in use.
Figure 7 Training datasets in use.CC BY · PubMed Central
Figure 8 Federated machine learning classical structure.
Figure 8 Federated machine learning classical structure.CC BY · PubMed Central
Figure 9 Data fusion different approaches.
Figure 9 Data fusion different approaches.CC BY · PubMed Central
Figure 10 Research questions arising from analysing usage of wearables in CVDs detection.
Figure 10 Research questions arising from analysing usage of wearables in CVDs detection.CC BY · PubMed Central
Figure 11 Research topics that may serve as solutions to the challenges in the domain.
Figure 11 Research topics that may serve as solutions to the challenges in the domain.CC BY · PubMed Central
Figure 12 Challenges–future solutions chart.
Figure 12 Challenges–future solutions chart.CC BY · PubMed Central

Verification

Claims the paper makes

23 Own result · 19 Own interpretation · 16 Not checked yet · 12 No basis given · 4 Own method

Verify all 24 references — free account

Show all 27 claims

  • Probably not a substantial claim. 36
  • It may state two separate claims as one. 25
47 of 47
  • The paper relies on this without support.

    Publication counts reflect growing interest and widespread acceptance of smart wearables for CVD.

    “The number of publications addressing the use of smart wearables for cardiovascular disease management has jumped, reflecting both the growing interest in this area and the widespread acceptance of such devices.”
  • The paper relies on this without support.

    The data extracted from the included papers accurately represent the studies' details.

    “From each paper, the year of publication, disease(s) treated, vital signs recorded, hardware of the wearable device(s), embedded intelligent models, dataset(s) used, and outcome metrics were extracted.”
  • The paper relies on this without support.

    The performance metrics reported in the included studies are valid and comparable across studies.

    “In addition, the databases used for training and the performance metrics are detailed in Table 2.”
  • The paper relies on this without support.

    More data can lead to more accurate models.

    “it is generally accepted that more data can lead to more accurate models.”
  • The paper relies on this without support.

    Research in medicine has shown that the use of multiple vital signs may be more helpful in detecting a disease than the use of a single vital sign.

    “Research in the field of medicine has shown that the use of multiple vital signs may be more helpful in detecting a disease than the use of a single vital sign.”
  • The paper relies on this without support.

    One of the major barriers to the use of smart wearables to detect and predict cardiovascular disease is user acceptance, adoption, and participation.

    “One of the major barriers to the use of smart wearables to detect and predict cardiovascular disease is user acceptance, adoption, and participation.”
  • The paper relies on this without support.

    When it comes to health, users are only willing to use tools that are highly accurate, understandable, private, and reliable.

    “when it comes to health, users are only willing to use tools that are highly accurate, understandable, private, and reliable.”
7 of 7

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

2 of 2

Citations

Cites

Research works printed but not linked: 5

  1. Poslad, S. (2011). Ubiquitous Computing: Smart Devices, Environments and Interactions, John Wiley Sons.
  2. Thorp, E.O. (1998, January 19–20). The invention of the first wearable computer. Proceedings of the Digest of Papers. Second International Symposium on Wearable Computers (Cat. No. 98EX215), Pittsburgh, PA, USA.
  3. Myles, 2004, Brown. An introduction to decision tree modeling, J. Chemom. A J. Chemom. Soc., 18, 275
  4. Doran, D., Schulz, S., and Besold, T.R. (2017). Besold. What does explainable AI really mean? A new conceptualization of perspectives. arXiv.
  5. Mammen, P.M. (2021). Federated learning: Opportunities and challenges. arXiv.

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Metadata

Authors
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A.
Affiliations
  • Université du Québec à Rimouski (CA)
  • Université du Québec à Chicoutimi (CA)
  • Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
  • Islamic University of Lebanon (LB)
Corresponding author
Moshawrab, Mohammad
Venue
Sensors
Publisher
MDPI AG
Published
2023-01-11
Volume
23
Issue
2
Pages
828
ISSN
1424-8220 (ISSN-L)
OpenAlex
W4315572261
Cited by
120
References
161
Citations by year
2026: 24 · 2025: 36 · 2024: 41 · 2023: 19
FWCI
2.71
Type
journal article
Language
en
Open access
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Natural Sciences and Engineering Research Council of Canada (NSERC) (06351)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
wearable devices, cardiovascular disease detection, cardiovascular disease prediction, vital signs monitoring, wearable sensors, artificial intelligence in healthcare
MeSH terms
Wearable Electronic Devices, Artificial Intelligence, Cardiovascular Diseases, Humans

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