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Chen Y, Xiao X, He Q, Yao RQ, Zhang GY, Fan JR, Xue CX, Huang L. Knowledge mapping of digital medicine in cardiovascular diseases from 2004 to 2022: A bibliometric analysis. Heliyon 2024; 10:e25318. [PMID: 38356571 PMCID: PMC10864893 DOI: 10.1016/j.heliyon.2024.e25318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/22/2023] [Accepted: 01/24/2024] [Indexed: 02/16/2024] Open
Abstract
Objective To review studies on digital medicine in cardiovascular diseases (CVD), discuss its development process, knowledge structure and research hotspots, and provide a perspective for researchers in this field. Methods The relevant literature in recent 20 years (January 2004 to October 2022) were retrieved from the Web of Science Core Collection (WoSCC). CiteSpace was used to demonstrate our knowledge of keywords, co-references and speculative frontiers. VOSviewer was used to chart the contributions of authors, institutions and countries and incorporates their link strength into the table. Results A total of 5265 English articles in set timespan were included. The number of publications increased steadily annually. The United States (US) produced the highest number of publications, followed by England. Most publications were from Harvard Medicine School, followed by Massachusetts General Hospital and Brigham Women's Hospital. The most authoritative academic journal was JMIR mHealth and uHealth. Noseworthy PA may have the highest influence in this intersected field with the highest number of citations and total link strength. The utilization of wearable mobile devices in the context of CVD, encompassing the identification of risk factors, diagnosis and prevention of diseases, as well as early intervention and remote management of diseases, has been widely acknowledged as a knowledge base and an area of current interest. To investigate the impact of various digital medicine interventions on chronic care and assess their clinical effectiveness, examine the potential of machine learning (ML) in delivering clinical care for atrial fibrillation (AF) and identifying early disease risk factors, as well as explore the development of disease prediction models using neural networks (NNs), ML and unsupervised learning in CVD prognosis, may emerge as future trends and areas of focus. Conclusion Recently, there has been a significant surge of interest in the investigation of digital medicine in CVD. This initial bibliometric study offers a comprehensive analysis of the research landscape pertaining to digital medicine in CVD, thereby furnishing related scholars with a dependable reference to facilitate further progress in this domain.
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Affiliation(s)
- Ying Chen
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
- National Integrative Medicine Center for Cardiovascular Diseases, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
| | - Xiang Xiao
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
- National Integrative Medicine Center for Cardiovascular Diseases, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
| | - Qing He
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Rui-Qi Yao
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Gao-Yu Zhang
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Jia-Rong Fan
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Chong-Xiang Xue
- Beijing University of Chinese Medicine, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Li Huang
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
- National Integrative Medicine Center for Cardiovascular Diseases, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
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Goergen CJ, Tweardy MJ, Steinhubl SR, Wegerich SW, Singh K, Mieloszyk RJ, Dunn J. Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data. Annu Rev Biomed Eng 2022; 24:1-27. [PMID: 34932906 PMCID: PMC9218991 DOI: 10.1146/annurev-bioeng-103020-040136] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.
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Affiliation(s)
- Craig J Goergen
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Steven R Steinhubl
- physIQ Inc., Chicago, Illinois, USA
- Scripps Research Translational Institute, La Jolla, California, USA
| | | | - Karnika Singh
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | | | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
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eHealth and mHealth Development in Spain: Promise or Reality? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182413055. [PMID: 34948664 PMCID: PMC8700823 DOI: 10.3390/ijerph182413055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/28/2021] [Accepted: 12/08/2021] [Indexed: 02/06/2023]
Abstract
In the last decades, the use of Information and Communication Technologies (ICTs) has progressively spread to society and public administration. Health is one of the areas in which the use of ICTs has more intensively developed through what is now known as eHealth. That area has recently included mHealth. Spanish health system has stood out as one of the benchmarks of this technological revolution. The development of ICTs applied to health, especially since the outbreak of the pandemic caused by SARS Cov-2, has increased the range of health services delivered through smartphones and the development of subsequent specialized apps. Based on the data of a Survey on Use and Attitudes regarding eHealth in Spain, the aim of this research was to conduct a comparative analysis of the different eHealth and mHealth user profiles. The results show that the user profile of eHealth an mHealth services in Spain is not in a majority. Weaknesses are detected both in the knowledge and use of eHealth services among the general population and in the usability or development of their mobile version. Smartphones can be a democratizing vector, as for now, access to eHealth services is only available to wealthy people, widening inequality.
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