Background: Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area.
Objective: We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field.
Methods: The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization.
Results: The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7%), followed by United Kingdom (1136/12,529, 9.1%), Germany (1067/12,529, 8.5%), China (682/12,529, 5.4%), and Italy (622/12,529, 5.0%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2%), atrial fibrillation (122/12,529, 1.0%), atherosclerosis (116/12,529, 0.9%), heart failure (106/12,529, 0.8%), and arterial stiffness (80/12,529, 0.6%). Recurring modalities were electrocardiography (170/12,529, 1.4%), angiography (127/12,529, 1.0%), echocardiography (127/12,529, 1.0%), digital subtraction angiography (111/12,529, 0.9%), and photoplethysmography (80/12,529, 0.6%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2%) and other JMIR portfolio journals (51/632, 8.0%) were the major publishing venues.
Conclusions: Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth.
Modern health care and medicine are characterized by continuous digital innovation. This innovation is driven by the confluence of, first, technological advances with transformative potential and, second, convincing use cases based on needs and opportunities from the health care domain. This is an area of high-volume activity evidenced in a large and heterogeneous scientific literature base, which warrants a high-level overview and bibliometric analysis.
Current Transformative Developments in Digital Technology
Recent advances in digital technology for health care and medicine have been fundamentally facilitated by a revolution—increasing miniaturization and affordability—in sensing devices, which have been manufactured as both stationary and wearable devices to track a broad and growing range of vital signs and physiological measurements [, ]. These developments have coincided with rapid innovations in interactive, networked, mobile, and ubiquitous computing [ ], which has brought about modern smartphones, wireless connectivity, and Internet of Things computing, networked information systems, and increasingly capable consumer--facing and professional apps [ ]. This enables effective automation in many areas that are highly relevant for health care and medicine, such as communication (eg, telehealth [ ], which has been recently emphasized by an increased need for remote access for medical support in both physical and mental health during the COVID-19 pandemic [ , ]), social support [ - ], and education [ ]. Moreover, there are growing possibilities for the augmentation of sensing and actuation [ ], via biocompatible technologies [ ] and ubiquitous sensing focused on situated functionality [ ].
Technology transfer in these areas follows a general pathway from innovators and early adopters—technology developments are often inspired by hacking, gaming, or similar communities—through applied research and development into actual medical and health care practice . Virtual, augmented, and mixed reality are good examples of current technologies that are beginning to take hold in real-life medical and health care practice, for example, in diagnostic and surgical procedures and rehabilitation, by offering versatility for a broad range of conditions, including pain, stroke, anxiety, depression, fear, cancer, and neurodegenerative disorders [ ].
Other recent developments with transformative potential include initiatives toward digital biomarkers and interventions that promise to enable personalized and precision medicine . Building on foundations developed in enthusiast communities around the quantified self [ ] and personal informatics [ - ], these approaches suggest there is a need for patient data contributions and personal health records [ , ] with advances in data processing and analytics, for example, in artificial intelligence and machine learning for supporting diagnosis [ , ] and medical decision-making [ , ]. Key drivers toward truly personalized and precision medicine [ , ] will arguably be the adaptability and adaptivity of systems that anticipate rather than react [ , ], for example, via predictive modeling [ ], which in turn facilitates a focus toward preventative rather than curative medicine [ ].
Further potentially transformative technologies are conversational interfaces [- ], developments that enable localized and individualized production through 3D printing [ , ], biochemical composition [ ], or personal genomics [ ]. These developments have considerable potential for positive change but also require delicate handling of personal data and privacy issues in accordance with data standards [ ], legal and ethical considerations [ , ], and social considerations [ , ]. A key challenge lies in moving toward more sustainable adoption and use of available technologies, which requires a broad view on complex ecosystems [ , ], motivation [ ] and habituation or behavior change [ - ]. Moreover, there is a need to more closely connect research and industry [ ] and to work in a highly human-centered manner [ ].
Clinical Use Cases of Digital Innovation in Cardiology
The variety of digital technologies in health care and medicine is reflected in the field of cardiology, in which multiple uses can be found. Telecardiology describes the delivery of one-to-one cardiology care without the need for physical meetings between the physician and the patient  and has been facilitated by the improved availability and functionality of remote communication technologies and by digital technologies that enable reliable recording and transmission of clinical measurements from implantable (pacemakers, defibrillators) and consumer devices (blood pressure monitors, scales, thermometers) [ ]. Cardiac telerehabilitation—programs provided at patients’ homes rather than at rehabilitation centers [ ]—uses technology solutions to facilitate the remote instruction, monitoring, and supervision of patients during exercise training, with processes for providing emergency care in case of medical emergencies [ ]. Artificial intelligence and machine learning approaches offer multitudes of possibilities in cardiology diagnostics and therapeutics, for example, individual cardiovascular risk factor identification; profiling, prediction, and management of cardiac arrhythmias; and enhanced cardiac imaging [ , , ].
The field of behaviour change for primary and secondary prevention of cardiovascular disease through digital technologies, for example, to understand and modify behavior, has increased rapidly in recent years . This approach could deliver effective personalized support for heart-healthy lifestyle changes, such as adherence to medication and exercise recommendations [ ] with the measurement of physical activity [ ] and associated parameters such as heart rate [ ] using sensors incorporated in objects of daily use, such as mobile phones and watches [ ]. Existing technologies also provide the ability to capture information about the environment in which behavior takes place, with mobile phone location tracking, and can be used to facilitate understanding of behavior [ ] or to change behavior [ ]. Behavior change interventions can be effective, especially when tailored to the individual [ ]. However, there is room for improvement in terms of using the unique characteristics and full potential of digital technologies, such as the possibility of intervening at the right moment (for example when a person is in need of support). The potential of these so-called just-in-time adaptive interventions has only been explored recently, and insight into their effectiveness is largely still lacking [ ].
Given that digital technologies (specifically, the internet and smartphone apps) are vehicles for information transfer, another highly promising area of application for these digital technologies in cardiology is health literacy (ie, the degree to which individuals can obtain, process, understand, and communicate about health-related information needed to make informed health decisions ). Health literacy is a prerequisite to successfully maintain health and self-care; navigate through the healthcare system; and in case of illness, understand health information, medication, and treatment plans [ ]. Especially in older adults, health literacy is a significant predictor of information-seeking behaviors and health outcomes [ ]. Despite growing global recognition of health literacy as a critical determinant of health and well-being and efforts to improve health literacy [ ], health literacy levels among the global population remain low [ - ].
Digital technologies, including the internet and information communication technologies, seem to offer a convenient way to deliver broadly and rapidly evidence-based health information and thus improve overall health literacy, especially in disadvantaged populations that lack access to health care and relevant health information [, ]. However, a recent study [ ] has shown that persons with lower health literacy report difficulties searching health information and are less likely to use search engines. Moreover, low health literacy is also associated with difficulty judging the quality of health information from the internet [ ]. In order to actively support individuals’ health literacy, digital technologies or services are increasingly promoted in different care contexts to accelerate patient–provider communication and, at the same time, offer an opportunity to educate patients in the appropriate use of web-based health information. In inpatient care, digital tools such as electronic displays can be employed during ward rounds to support the consultation or facilitate the discharge process, and medically vetted electronic health information is shared with patients at the hospital bedside [ ]. Automated systems can be integrated to teach patients about their diagnosis and postdischarge self-care regimen [ ]. In outpatient care, digital technologies often aim to support chronically ill persons. Telehealth systems for synchronous audio- and video-based communication allow patients to report symptoms and preferences to their health care provider remotely [ ], while asynchronous text-based communication through patient portals enables patients more convenient access to their health information [ ]. These technologies offer patients alternative modalities for information transfer and communication with health care providers, thereby facilitating effective information exchange and supporting individual health literacy skills. In the field of cardiology, the importance of a greater focus on supporting health literacy has recently been highlighted, specifically in the context of primary and secondary prevention of cardiovascular disease [ ].
Rationale for a Bibliometric Analysis
The broad range of digital technology use in cardiology is reflected by a large scientific literature base. Bibliometric analysis provides an integral view with quantitative evaluations of publishing metrics of research literature [- ]. The purpose of this bibliometric analysis of digital technology uses in cardiology is to describe and discover current trends, topics, and scientometric characteristics within this body of literature, providing a high-level overview of the scientific literature and enabling insights for future directions in digital health in cardiology. To the best of our knowledge, no such analysis has been published to date.
We searched the Web of Science Core Collection database on November 22, 2021 ().
We excluded digitalin*, digitalis*, supplemental digital and digital ulcer* because these words and their derivatives did not refer to digital technology, but instead referred to the drug digitalin, to the plant genus Digitalis, to supplemental digital content, and to the medical condition digital ulcer, respectively. No additional filters were applied to restrict the search results. The search resulted in 12,529 papers. The Analyze Results and Citation Report functions of the Web of Science platform were utilized for basic frequency counts and the number of citations per publication (mean citations per item within a subset) of the most productive authors, institutions, countries, journals, and journal categories. We also defined a subset—literature that included the terms smartphone*, app, or wearable* in the title, abstract, or keywords—which contained 632 papers.
The full record and cited references were then exported into VOSviewer as tab delimited files to synthesize a term map. For clarity, only terms that appeared in at least 0.5% of the literature set (>63) were included in the map. A list of top 5000 common words from the Corpus of Contemporary American English was entered to remove generic (and therefore, less meaningful) words from the term map . VOSviewer was also used to identify the top 20 recurring author keywords.
As the latest digital technology uses often involve smartphone apps and wearable devices, This analysis described above, except for the term map, was similarly conducted on a subset of the concerned.
Digital technology in cardiology search string. TS: searching for title, abstract, and keywords; WC: searching for the particular journal category.
(#1 OR #2) NOT (#3 OR #4)
- TS=(digital* AND (cardio* OR cardiac* OR heart*) NOT (digitalin* OR digitalis*))
- WC=(CARDIAC CARDIOVASCULAR SYSTEMS) AND TS=(digital* NOT (digitalin* OR digitalis*))
- TS=(“Supplemental Digital”)
- TS=(“digital ulcer*”)
The 12,529 papers were published from 1965 to November 22, 2021. The earliest publication was a report on the development and demonstration of an analog-digital analyzing unit to screen heart sounds in children . The literature growth seemed to be accelerating in the 2000s and especially into the 2010s ( ). Approximately three-quarters of the papers (9271/12,529, 74.0%) were original articles, and review papers accounted for 6.3% (789/12,529). Proceedings papers and meeting abstracts accounted for 14.2% (1779/12,529) and 6.0% (752/12,529), respectively.
The most productive author was Professor David J Sahn from Oregon Health and Science University, whose highly cited papers were focused on real-time 3D echocardiography [- ]. Of the 5 most productive institutions, 4 were based in the United States of America, with Harvard University having the highest number of citations per publication. The most productive journals were from the area of cardiology or cardiovascular system, with Circulation having the highest citations per publication among the top 5 ( ). Cardiac and cardiovascular systems was the most productive journal category, accounting for nearly one-third of the papers.
The variety of digital technology uses in cardiology can be observed (), with uses related to blood pressure (n=727, citations per publication: 20.1), hypertension (n=642, citations per publication: 21.1), arterial stiffness (n=128, citations per publication: 19.4), and stenosis (n=500, citations per publication: 23.7). Terms that appeared in more recent papers included wearable device (n=79, citations per publication: 10.1), smartphone (n=143, citations per publication: 12.0), and COVID (n=111, citations per publication: 3.2) ( ), as well as pandemic (n=74, citations per publication: 3.0), machine learning (n=97, citations per publication: 11.1), artificial intelligence (n=112, citations per publication: 8.8), and app (n=149, citations per publication: 10.3) (not in ).
|Entity||N (%)||Citations per publication|
|Sahn, David J||71 (0.5)||10.7|
|Wong, Tien Yin||53 (0.5)||46.2|
|Molloi, Sabee||35 (0.3)||14.9|
|Jones, Molly||33 (0.3)||14.0|
|Li, Xiang-Ning||33 (0.3)||4.5|
|University of California system||440 (3.5)||32.2|
|University of London||278 (2.2)||30.8|
|Harvard University||263 (2.1)||54.4|
|Duke University||178 (1.4)||35.4|
|US Department of Veterans Affairs||163 (1.3)||44.0|
|United States of America||4224 (33.7)||26.7|
|United Kingdom||1136 (9.1)||24.8|
|Journal of the American College of Cardiology||271 (2.2)||34.4|
|Cardiovascular and Interventional Radiology||251 (2.0)||13.8|
|American Journal of Cardiology||171 (1.4)||29.3|
|European Heart Journal||140 (1.1)||20.2|
|Cardiac cardiovascular systems||4101 (32.7)||22.0|
|Radiology, nuclear medicine, or medical imaging||1289 (10.3)||20.9|
|Peripheral vascular disease||1090 (8.7)||36.3|
|Engineering, biomedical||1001 (8.0)||20.5|
|Engineering, electrical or electronic||965 (7.7)||9.3|
The terms telemedicine, digital health, and mHealth (mobile health) were among the top 20 author keywords (), suggesting that digital technology uses have been a major research focus. Such uses are also of increasing interest in the context of the COVID-19 pandemic.
The literature subset contained 632 papers on smartphone apps and wearable devices. The first paper of this subset was published in 2005, and it introduced a wearable multiparameter (including heart rate and blood pressure) ambulatory physiological monitoring system that could digitally record and continuously stream data to a base station . Approximately 62.2% (393/632) of papers were original articles. Review papers accounted for 15.2% (96/632). The most productive author within this subset was Dr. Mohamed Elgendi affiliated with University of British Columbia and Simon Fraser University. His research interest focused on using data from photoplethysmography (PPG) to detect hypertension, potentially with the aid of machine learning [ , ]. Of the 5 most productive institutions, 4 were based in the United States of America. Furthermore, in the top 5 countries, the first 4 places remained unchanged from the those of the full data set (United States, United Kingdom, Germany, and China). Fifth place was taken by Australia, while Italy (ranked as the fifth most productive country in the full data set) moved to tenth place. JMIR Publications had the top 3 journals, which collectively accounted for more than 10% of the 632 papers ( ).
The top 20 author keywords (), notably, included atrial fibrillation, a cardiac condition that causes rapid and irregular heart rate.
|Author keyword||n (%)||Citations per publication|
|Heart rate||173 (1.4)||56.5|
|ECG (electrocardiography)||170 (1.4)||9.6|
|Digital health||167 (1.3)||10.7|
|Blood pressure||159 (1.3)||18.4|
|Heart rate variability||126 (1.0)||18.1|
|Atrial fibrillation||122 (1.0)||20.6|
|Cardiovascular disease||115 (0.9)||16.2|
|Digital subtraction angiography||111 (0.9)||12.5|
|Coronary artery disease||107 (0.9)||28.1|
|Heart failure||106 (0.8)||14.7|
|Machine learning||97 (0.8)||4.8|
|Arterial stiffness||80 (0.6)||22.6|
|Entity||n (%)||Citations per publication|
|Author (last name, first name)|
|Elgendi, Mohamed||9 (1.4)||18.4|
|Martin, Seth S||8 (1.3)||22.5|
|Sharma, Abhinav||6 (0.9)||15.3|
|Ward, Rabab||6 (0.9)||23.0|
|Benjamin, Emelia J.||5 (0.8)||20.2|
|Majmudar, Maulik||5 (0.8)||4.0|
|Marvel, Francoise A||5 (0.8)||4.0|
|Murabito, Joanne M||5 (0.8)||20.2|
|Shan, Rongzi||5 (0.8)||10.0|
|Tarakji, Khaldoun G||5 (0.8)||42.4|
|Van Hoof, Chris||5 (0.8)||13.4|
|University of California system||31 (4.9)||18.4|
|Harvard University||27 (4.3)||10.6|
|Stanford University||21 (3.3)||29.4|
|University of London||18 (2.8)||14.1|
|Johns Hopkins University||15 (2.4)||13.3|
|United States of America||242 (38.3)||15.8|
|United Kingdom||59 (9.3)||11.2|
|JMIR mHealth and uHealth||30 (4.7)||5.8|
|JMIR Research Protocols||21 (3.3)||2.3|
|Journal of Medical Internet Research||20 (3.2)||11.5|
|IEEE Access||10 (1.6)||7.8|
|Engineering Electrical Electronic||135 (21.4)||8.4|
|Health Care Sciences Services||104 (16.5)||8.5|
|Medical Informatics||90 (14.2)||10.7|
|Cardiac Cardiovascular Systems||89 (14.1)||12.0|
|Engineering Biomedical||69 (10.9)||12.6|
|Author keyword||n (%)||Citations per publication|
|Digital health||72 (11.4)||14.1|
|mHealth (mobile health)||47 (7.4)||12.5|
|Heart rate||22 (3.5)||7.2|
|Mobile phone||21 (3.3)||8.9|
|Wearable devices||20 (3.2)||11.6|
|ECG (electrocardiography)||20 (3.2)||5.9|
|Physical activity||18 (2.8)||13.4|
|Digital medicine||17 (2.7)||18.1|
|Machine learning||17 (2.7)||11.9|
|Cardiovascular disease||16 (2.5)||14.3|
|Heart rate variability||15 (2.4)||6.7|
|Atrial fibrillation||14 (2.2)||20.9|
|Artificial intelligence||14 (2.2)||4.2|
Cardiovascular diseases that were frequently indicated as author keywords in the 12,529 papers included hypertension, atrial fibrillation, atherosclerosis, heart failure, and arterial stiffness. A recent meta-analysis reported that using smartphone app–based interventions could significantly lower blood pressure and improve medication adherence in patients with hypertension . It was found that both wearable, ambulatory, and home monitoring devices recorded blood pressure with comparable values [ ]. Smartphone and smartwatch apps could already readily distinguish atrial fibrillation from sinus rhythm and detect them with high sensitivity and specificity comparable to 12-lead electrocardiography (ECG) [ , ]. Authoritative bodies such as the European Society of Cardiology have also developed smartphone apps for patient education on atrial fibrillation [ ]. The use of smartphone apps could help general physicians and trainee cardiologists decide whether a patient with heart failure should receive an implantable cardioverter defibrillator or cardiac resynchronization therapy [ ]. Researchers found that these apps could potentially reduce hospital staff and facility costs by enabling patients to self-perform simple diagnostic tests, such as the 6-minute walk test, a functional exercise test used to assess patients with cardiopulmonary problems [ ]. Similarly, improved access and participation in cardiac rehabilitation in terms of physical activity counselling and exercise training could be achieved by using digital health interventions that were not facility-based [ ]. Apart from patient and physicians, digital technology could also target people outside of health care. For instance, massively multiplayer virtual worlds could be modified for use as a serious game to efficiently and reliably teach high school students how to perform cardiopulmonary resuscitation, an act that can be life-saving [ ]. Virtual reality, a research hotspot in recent years [ ], could also be utilized to teach cardiopulmonary resuscitation for medical students [ ].
Meanwhile, recurring investigative modalities highlighted by the current analysis included ECG, angiography, echocardiography, digital subtraction angiography, and PPG. PPG is one of the most heavily researched diagnostic tools, and it is noninvasive, inexpensive, and convenient . It could also be performed with a smartphone to detect heart rate with an average error rate as low as 1 to 1.5% [ ]. Applying deep learning to PPG data could also stratify patients’ risk of hypertension [ ]. Moreover, artificial intelligence could interpret ECGs rapidly with human-like performance and even detect signals and patterns largely unrecognizable by humans [ ]. Overall, use cases, in which physiological parameters from wearable sensing devices are extracted and artificial intelligence is applied to draw insights, are a focal point in the literature; there is a large cluster of prominent terms such as parameter, monitoring, detection, and algorithm ( ). Machine learning methods such as deep learning are frequently used to represent data structures and to make predictions or classifications, with the overall intention of supporting clinicians in data-based decision-making [ ]. The expectation is that this will contribute to increasing the efficiency and effectiveness of care delivery, in particular with respect to precision health and personalized care [ ]. In fact, digital technology could be very useful, with predictive models and interventions in the personalized management of cardiovascular disease patients for predicting sudden cardiac death, ventricular tachycardia, and ventricular fibrillation [ , ].
During the COVID-19 pandemic, the value of digital technology use under extreme measures for infection control has become evident. For instance, electronic stethoscopes could be utilized for contactless auscultation with real-time playback, digital storage of data, and subsequent data transmission for further assessment . With the reduction of in-person hospital visits, digital technology could facilitate telemonitoring programs to serve as alternative to support patient access to care [ ]. Indeed, a recent bibliometric analysis on digital health papers listed telemedicine and telehealth as two of the most frequently used keywords, indicating their relevance beyond cardiology [ ].
In the subset of smartphone app and wearable device literature, we found that mHealth, physical activity, and eHealth were among the top author keywords, and most papers had been published in JMIR mHealth and uHealth, JMIR Research Protocols, and Journal of Medical Internet Research. These findings were highly consistent with a recent bibliometric analysis on digital health behavior change technology , but where United States, United Kingdom, and the Netherlands had been the most productive countries, in our findings, the Netherlands was replaced by Germany and China. This suggests that there are some geographical differences in research interest between cardiology-specific and general research on health behavior change. Meanwhile, another recent bibliometric analysis on mobile health apps also identified the 3 abovementioned journals as the most productive [ ].
In principle, smartphone apps could offer an ideal modality for delivering digital interventions to empower patients’ self-management, by providing health literacy support and coaching content (eg, a smartphone coaching app for blood pressure control ). However, in line with findings from this bibliometric analysis, recent reviews [ , ] have highlighted that there is a relative paucity of health literacy interventions and, more specifically, a paucity of digital health literacy interventions for cardiovascular patient groups [ ]. Moreover, apps designed to empower patients often include a narrow range of features and lack explicit linkage with theories of empowerment [ , ]. This is an area for further research—the development of content and features for such apps should be based on relevant theoretical underpinnings.
Interestingly, this bibliometric analysis did not identify top-listed terms related to primary prevention or secondary prevention/cardiac rehabilitation of cardiovascular disease. This may seem surprising, since there has been a rapid growth in the development of health apps and other digital technology interventions, and primary and secondary prevention of cardiovascular disease and cardiac rehabilitation are important areas of application [- ]. A number of recent reviews demonstrate that a sizeable body of literature is available, for example, a systematic review and meta-analysis [ ], which included 51 primary studies of digital health interventions for the primary and secondary prevention of cardiovascular disease; a systematic review and meta-analysis [ ], which reported on 25 original studies of digital technology interventions for cardiovascular risk factor modification; a scoping review [ ], which summarized 13 trials of mobile technology interventions for improving exercise capacity in cardiac rehabilitation; and a systematic review [ ], which reported on 31 primary studies of digital health interventions for physical activity and exercise adherence in cardiac rehabilitation. In the context of this bibliometric analysis, this indicates that the literature on digital technology cardiology uses appears more accessible through disease- and condition-specific key terms (hypertension, atrial fibrillation, atherosclerosis, cardiovascular disease, and heart failure), as opposed to more service- and patient pathway–oriented terms (primary prevention, secondary prevention, and cardiac rehabilitation), which may be a relevant consideration in designing literature search strategies for researchers targeting the latter [ , ].
Another aspect of digital technology use in cardiology that was not featured prominently among the findings of this bibliometric analysis is the use of digital technology to increase the efficiency and quality of research in cardiology . This refers to new possibilities afforded by mobile apps, smart devices, and implantable or wearable technologies for the design and management of research studies. Digital processes for data collection, monitoring, communication, documentation and approvals in research hold potential cost and time savings, and functionalities of digital devices open new avenues in the collection and quality control of real-time continuous data acquisition [ ] (eg, real-time capture of self-reported measures and symptoms in web-based forms, and the verification of subjective data through concurrent objective measurement, for example, by supplementing subjective reports of physical activity with continuously worn activity tracking devices). The use of digital technologies in the design and management of research studies in cardiology is an emerging focus in the literature, with opportunities for robust evaluations of the advantages of digital research designs over traditional nondigital approaches.
We observed that cardiology journals were predominant in the entire literature set. The top 5 journals were also among those that had published the 100 most cited cardiovascular papers in a previous study , with Circulation and European Heart Journal together accounting for 64% of the top 100. However, it should be noted that digital technology use does not only involve cardiology but is an intersection between medical informatics, engineering and health sciences and services in general. With this in mind, when the literature subset on smartphone app and wearable devices was examined, it could be seen that the traditional cardiology journals have given way to newer journals that focus on digital technology and medical informatics. The Journal of Medical Internet Research and JMIR-portfolio journals were found to be the major publishing venues for these papers. Therefore, readers should focus not only on traditional cardiology journals when seeking the latest advancements of digital technology use in the cardiology field.
There are several limitations. First, not all journals (and hence papers) are indexed by Web of Science. Alternative databases are available, each with their own shortcomings. For example, Scopus may contain erroneous data , Google Scholar does not allow automated extraction of title and abstract information, and PubMed does not contain citation data. Second, publication and citation counts do not necessarily equate to scientific quality. Within the diverse cardiology research field, the baseline research productivity in particular areas could be inhomogeneous; therefore, the ranking of clinicians or researchers is given for readers’ general reference only. Notwithstanding, this study should allow readers to gain a better understanding of the literature on digital technology uses in cardiology.
Cardiovascular diseases that were frequently investigated in the literature included hypertension, atrial fibrillation, atherosclerosis, heart failure, and arterial stiffness. Recurring investigative modalities included ECG, angiography, echocardiography, digital subtraction angiography, and PPG. Readers searching for relevant information and authors searching for suitable publication venues for their work may consider that, while cardiology or cardiovascular system–focused journals were predominant in the overall literature set, the major publishing venues for the literature subset on smartphone apps and wearable devices were Journal of Medical Internet Research and JMIR-portfolio journals. Digital uses targeted physicians and patients as well as the general public, and their functions included assisting diagnosis, recording cardiovascular parameters, patient education, and teaching laypersons about cardiopulmonary resuscitation. The scientific body of literature on digital technology use in cardiology is rapidly growing, and its impact on health care is also expected to greatly increase in the near future.
Conflicts of Interest
- Kassal P, Steinberg MD, Steinberg IM. Wireless chemical sensors and biosensors: a review. Sens Actuators B Chem 2018 Aug;266:228-245. [CrossRef]
- Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J Neuroeng Rehabil 2012 Apr 20;9(1):21 [FREE Full text] [CrossRef] [Medline]
- Weiser M. The computer for the 21st century. Sci Am 1991 Sep;265(3):94-104. [CrossRef]
- Yin AL, Hachuel D, Pollak JP, Scherl EJ, Estrin D. Digital health apps in the clinical care of inflammatory bowel disease: scoping review. J Med Internet Res 2019 Aug 19;21(8):e14630 [FREE Full text] [CrossRef] [Medline]
- Kruse CS, Krowski N, Rodriguez B, Tran L, Vela J, Brooks M. Telehealth and patient satisfaction: a systematic review and narrative analysis. BMJ Open 2017 Aug 03;7(8):e016242 [FREE Full text] [CrossRef] [Medline]
- Inkster B, Digital Mental Health Data Insights Group (DMHDIG). Early warning signs of a mental health tsunami: a coordinated response to gather initial data insights from multiple digital services providers. Front Digit Health 2020 Feb 10;2:578902 [FREE Full text] [CrossRef] [Medline]
- Monaghesh E, Hajizadeh A. The role of telehealth during COVID-19 outbreak: a systematic review based on current evidence. BMC Public Health 2020 Aug 01;20(1):1193 [FREE Full text] [CrossRef] [Medline]
- Bellini R, Wilson A, Smeddinck JD. Fragments of the past: curating peer support with perpetrators of domestic violence. 2021 May Presented at: Conference on Human Factors in Computing Systems; May 8-13, 2021; Yokohama. [CrossRef]
- Fortuna KL, Brooks JM, Umucu E, Walker R, Chow PI. Peer support: A human factor to enhance engagement in digital health behavior change interventions. J Technol Behav Sci 2019 Jun 15;4(2):152-161 [FREE Full text] [CrossRef] [Medline]
- Fortuna KL, Naslund JA, LaCroix JM, Bianco CL, Brooks JM, Zisman-Ilani Y, et al. Digital peer support mental health interventions for people with a lived experience of a serious mental illness: systematic review. JMIR Ment Health 2020 Apr 03;7(4):e16460 [FREE Full text] [CrossRef] [Medline]
- Car J, Carlstedt-Duke J, Tudor Car L, Posadzki P, Whiting P, Zary N, Digital Health Education Collaboration. Digital education in health professions: the need for overarching evidence synthesis. J Med Internet Res 2019 Feb 14;21(2):e12913 [FREE Full text] [CrossRef] [Medline]
- Raisamo R, Rakkolainen I, Majaranta P, Salminen K, Rantala J, Farooq A. Human augmentation: past, present and future. Int J Hum Comput Stud 2019 Nov;131:131-143. [CrossRef]
- Wang L, Lou Z, Jiang K, Shen G. Bio‐multifunctional smart wearable sensors for medical devices. Adv Intell Syst 2019 Aug 15;1(5):1900040. [CrossRef]
- Zhang H, Smeddinck J, Malaka R, Shu Y, Chen C, He B, et al. Wireless non-invasive motion tracking of functional behavior. Pervasive Mob Comput 2019 Mar;54:29-44. [CrossRef]
- Granados C, Pareja-Eastaway M. How do collaborative practices contribute to innovation in large organisations? the case of hackathons. Innovation 2019 Apr 05;21(4):487-505. [CrossRef]
- Yeung AWK, Tosevska A, Klager E, Eibensteiner F, Laxar D, Stoyanov J, et al. Virtual and augmented reality applications in medicine: analysis of the scientific literature. J Med Internet Res 2021 Feb 10;23(2):e25499 [FREE Full text] [CrossRef] [Medline]
- Bhavnani S, Parakh K, Atreja A, Druz R, Graham G, Hayek SS, et al. 2017 Roadmap for innovation-ACC health policy statement on health care transformation in the era of digital health, big data, and precision health: a report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol 2017 Nov 28;70(21):2696-2718 [FREE Full text] [CrossRef] [Medline]
- Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data 2013 Jun;1(2):85-99. [CrossRef] [Medline]
- Epstein DA, Ping A, Fogarty J, Munson SA. A lived informatics model of personal informatics. 2015 Presented at: 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing; September 7-11, 2015; Osaka. [CrossRef]
- Hussein R. Medical informatics in the digital personalized health and medicine era: a SWOT analysis and actionable strategies. Stud Health Technol Inform 2020 Jun 16;270:869-873. [CrossRef] [Medline]
- Rooksby J, Rost M, Morrison A, Chalmers M. Personal tracking as lived informatics. 2014 Presented at: SIGCHI Conference on Human Factors in Computing Systems; May 1, 2014; Toronto. [CrossRef]
- Detmer D, Bloomrosen M, Raymond B, Tang P. Integrated personal health records: transformative tools for consumer-centric care. BMC Med Inform Decis Mak 2008 Oct 06;8(1):45 [FREE Full text] [CrossRef] [Medline]
- Tang PC, Ash JS, Bates DW, Overhage JM, Sands DZ. Personal health records: definitions, benefits, and strategies for overcoming barriers to adoption. J Am Med Inform Assoc 2006;13(2):121-126 [FREE Full text] [CrossRef] [Medline]
- Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol 2019 Nov 09;16(11):703-715 [FREE Full text] [CrossRef] [Medline]
- Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 2001 Aug;23(1):89-109. [CrossRef]
- Johnson AEW, Ghassemi MM, Nemati S, Niehaus KE, Clifton D, Clifford GD. Machine learning and decision support in critical care. Proc IEEE Inst Electr Electron Eng 2016 Feb;104(2):444-466 [FREE Full text] [CrossRef] [Medline]
- Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA 2018 Dec 04;320(21):2199-2200. [CrossRef] [Medline]
- Ashley EA. The precision medicine initiative: a new national effort. JAMA 2015 Jun 02;313(21):2119-2120. [CrossRef] [Medline]
- Bahcall O. Precision medicine. Nature 2015 Oct 15;526(7573):335-335. [CrossRef] [Medline]
- Bzdok D, Varoquaux G, Steyerberg EW. Prediction, not association, paves the road to precision medicine. JAMA Psychiatry 2021 Feb 01;78(2):127-128. [CrossRef] [Medline]
- Malaka R, Herrlich M, Smeddinck J. Anticipation in motion-based games for health. In: Nadin M, editor. Anticipation and Medicine. Cham: Springer; 2017:351-363.
- Tyler J, Choi SW, Tewari M. Real-time, personalized medicine through wearable sensors and dynamic predictive modeling: a new paradigm for clinical medicine. Curr Opin Syst Biol 2020 Apr;20:17-25 [FREE Full text] [CrossRef] [Medline]
- Swan M. Health 2050: the realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. J Pers Med 2012 Sep 12;2(3):93-118 [FREE Full text] [CrossRef] [Medline]
- Bickmore TW, Utami D, Matsuyama R, Paasche-Orlow MK. Improving access to online health information with conversational agents: a randomized controlled experiment. J Med Internet Res 2016 Jan 04;18(1):e1 [FREE Full text] [CrossRef] [Medline]
- Kocaballi AB, Berkovsky S, Quiroz JC, Laranjo L, Tong HL, Rezazadegan D, et al. The personalization of conversational agents in health care: systematic review. J Med Internet Res 2019 Nov 07;21(11):e15360 [FREE Full text] [CrossRef] [Medline]
- Skjuve MB, Brandtzæg PB. Chatbots as a new user interface for providing health information to young people. In: Andersson Y, Dalquist U, Ohlsson J, editors. Youth and News in a Digital Media Environment: Nordic-Baltic Perspectives. Gothenburg: Nordicom; 2018.
- Manero A, Smith P, Sparkman J, Dombrowski M, Courbin D, Kester A, et al. Implementation of 3D printing technology in the field of prosthetics: past, present, and future. Int J Environ Res Public Health 2019 May 10;16(9):1641 [FREE Full text] [CrossRef] [Medline]
- Richterich A. When open source design is vital: critical making of DIY healthcare equipment during the COVID-19 pandemic. Health Sociol Rev 2020 Jul 10;29(2):158-167. [CrossRef] [Medline]
- Yetisen AK. Biohacking. Trends Biotechnol 2018 Aug;36(8):744-747. [CrossRef] [Medline]
- Rehm HL. Evolving health care through personal genomics. Nat Rev Genet 2017 Apr 31;18(4):259-267 [FREE Full text] [CrossRef] [Medline]
- Kelman C, Bass A, Holman C. Research use of linked health data--a best practice protocol. Aust N Z J Public Health 2002;26(3):251-255. [CrossRef] [Medline]
- Hodge JG, Gostin LO, Jacobson PD. Legal issues concerning electronic health information: privacy, quality, and liability. JAMA 1999 Oct 20;282(15):1466-1471. [CrossRef] [Medline]
- Nebeker C, Torous J, Bartlett Ellis RJ. Building the case for actionable ethics in digital health research supported by artificial intelligence. BMC Med 2019 Jul 17;17(1):137 [FREE Full text] [CrossRef] [Medline]
- Anderson JG. Social, ethical and legal barriers to e-health. Int J Med Inform 2007;76(5-6):480-483. [CrossRef] [Medline]
- Sankar PL, Parker LS. The Precision Medicine Initiative's All of Us research program: an agenda for research on its ethical, legal, and social issues. Genet Med 2017 Jul;19(7):743-750 [FREE Full text] [CrossRef] [Medline]
- Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res 2017 Nov 01;19(11):e367 [FREE Full text] [CrossRef] [Medline]
- Huang F, Blaschke S, Lucas H. Beyond pilotitis: taking digital health interventions to the national level in China and Uganda. Global Health 2017 Jul 31;13(1):49 [FREE Full text] [CrossRef] [Medline]
- Smeddinck JD. Games for health. In: Dörner R, Göbel S, Kickmeier-Rust M, Masuch M, Zweig K, editors. Entertainment Computing and Serious Games. Cham: Springer; 2016:212-264.
- Crutzen R, de Nooijer J, Brouwer W, Oenema A, Brug J, de Vries NK. Strategies to facilitate exposure to internet-delivered health behavior change interventions aimed at adolescents or young adults: a systematic review. Health Educ Behav 2011 Feb 28;38(1):49-62. [CrossRef] [Medline]
- Marcus BH, Selby VC, Niaura RS, Rossi JS. Self-efficacy and the stages of exercise behavior change. Res Q Exerc Sport 1992 Mar;63(1):60-66. [CrossRef] [Medline]
- Sun S, Folarin AA, Ranjan Y, Rashid Z, Conde P, Stewart C, RADAR-CNS Consortium. Using smartphones and wearable devices to monitor behavioral changes during COVID-19. J Med Internet Res 2020 Sep 25;22(9):e19992 [FREE Full text] [CrossRef] [Medline]
- Arigo D, Jake-Schoffman DE, Wolin K, Beckjord E, Hekler EB, Pagoto SL. The history and future of digital health in the field of behavioral medicine. J Behav Med 2019 Feb 1;42(1):67-83 [FREE Full text] [CrossRef] [Medline]
- Falter M, Scherrenberg M, Dendale P. Digital health in cardiac rehabilitation and secondary prevention: a search for the ideal tool. Sensors (Basel) 2020 Dec 22;21(1):12 [FREE Full text] [CrossRef] [Medline]
- Molinari G, Molinari M, Di Biase M, Brunetti ND. Telecardiology and its settings of application: an update. J Telemed Telecare 2017 Jan 13;24(5):373-381. [CrossRef] [Medline]
- Scherrenberg M, Wilhelm M, Hansen D, Völler H, Cornelissen V, Frederix I, et al. The future is now: a call for action for cardiac telerehabilitation in the COVID-19 pandemic from the secondary prevention and rehabilitation section of the European Association of Preventive Cardiology. Eur J Prev Cardiol 2020 Jul 03;28(5):a. [CrossRef] [Medline]
- Dalal HM, Doherty P, McDonagh ST, Paul K, Taylor RS. Virtual and in-person cardiac rehabilitation. BMJ 2021 Jun 03;373:n1270 [FREE Full text] [CrossRef] [Medline]
- Antoniades C, Asselbergs F, Vardas P. The year in cardiovascular medicine 2020: digital health and innovation. Eur Heart J 2021 Feb 14;42(7):732-739 [FREE Full text] [CrossRef] [Medline]
- Mesko B. A glimpse into the future of cardiology: how can advanced technologies lead to compassionate care? Eur Heart J 2021 Aug 31;42(33):3114-3116. [CrossRef] [Medline]
- Taj F, Klein MCA, van Halteren A. Digital health behavior change technology: bibliometric and scoping review of two decades of research. JMIR Mhealth Uhealth 2019 Dec 13;7(12):e13311 [FREE Full text] [CrossRef] [Medline]
- Gresham G, Schrack J, Gresham LM, Shinde AM, Hendifar AE, Tuli R, et al. Wearable activity monitors in oncology trials: current use of an emerging technology. Contemp Clin Trials 2018 Jan;64:13-21 [FREE Full text] [CrossRef] [Medline]
- Singh N, Moneghetti K, Christle J, Hadley D, Froelicher V, Plews D. Heart rate variability: an old metric with new meaning in the era of using mhealth technologies for health and exercise training guidance. part two: prognosis and training. Arrhythm Electrophysiol Rev 2018 Dec;7(4):247-255 [FREE Full text] [CrossRef] [Medline]
- Nelson BW, Low CA, Jacobson N, Areán P, Torous J, Allen NB. Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research. NPJ Digit Med 2020 Jun 26;3(1):90 [FREE Full text] [CrossRef] [Medline]
- Stappers NEH, Schipperijn J, Kremers SPJ, Bekker MPM, Jansen MWJ, de Vries NK, et al. Combining accelerometry and GPS to assess neighborhood-based physical activity: associations with perceived neighborhood walkability. Environ Behav 2020 Feb 17;53(7):732-752. [CrossRef]
- Crutzen R. Hardwired… to self-destruct? Using technology to improve behavior change science. Health Psychol Bull 2021 May 21;5(1):70-80. [CrossRef]
- Wolfenden L, Nathan N, Williams CM. Computer-tailored interventions to facilitate health behavioural change. Br J Sports Med 2015 Nov 03;49(22):1478-1479. [CrossRef] [Medline]
- Perski O, Hébert ET, Naughton F, Hekler EB, Brown J, Businelle MS. Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: a systematic review. Addiction 2022 May 11;117(5):1220-1241. [CrossRef] [Medline]
- Berkman ND, Davis TC, McCormack L. Health literacy: what is it? J Health Commun 2010 Aug 31;15 Suppl 2(sup2):9-19. [CrossRef] [Medline]
- Jacobs RJ, Lou JQ, Ownby RL, Caballero J. A systematic review of eHealth interventions to improve health literacy. Health Informatics J 2016 Jun 10;22(2):81-98 [FREE Full text] [CrossRef] [Medline]
- Kickbusch I, Pelikan JM, Apfel F, Tsouros AD. Health literacy: the solid facts. World Health Organization Regional Office for Europe. 2013. URL: https://apps.who.int/iris/bitstream/handle/10665/128703/e96854.pdf [accessed 2022-04-22]
- Prince L, Schmidtke C, Beck J, Hadden K. An assessment of organizational health literacy practices at an academic health center. Qual Manag Health Care 2018;27(2):93-97. [CrossRef] [Medline]
- Qi S, Hua F, Xu S, Zhou Z, Liu F. Trends of global health literacy research (1995–2020): analysis of mapping knowledge domains based on citation data mining. PLoS ONE 2021 Aug 9;16(8):e0254988. [CrossRef]
- Sørensen K, Pelikan JM, Röthlin F, Ganahl K, Slonska Z, Doyle G, HLS-EU Consortium. Health literacy in Europe: comparative results of the European health literacy survey (HLS-EU). Eur J Public Health 2015 Dec 05;25(6):1053-1058 [FREE Full text] [CrossRef] [Medline]
- Health Literacy: a Prescription to End Confusion. Washington DC: National Academies Press; 2004.
- Mackert M, Mabry-Flynn A, Champlin S, Donovan EE, Pounders K. Health literacy and health information technology adoption: the potential for a new digital divide. J Med Internet Res 2016 Oct 04;18(10):e264 [FREE Full text] [CrossRef] [Medline]
- Manganello J, Gerstner G, Pergolino K, Graham Y, Falisi A, Strogatz D. The relationship of health literacy with use of digital technology for health information: implications for public health practice. J Public Health Manag Pract 2017;23(4):380-387. [CrossRef] [Medline]
- Diviani N, van den Putte B, Giani S, van Weert JC. Low health literacy and evaluation of online health information: a systematic review of the literature. J Med Internet Res 2015 May 07;17(5):e112 [FREE Full text] [CrossRef] [Medline]
- Wilcox L, Feiner S, Liu A, Restaino S, Collins S, Vawdrey D. Designing inpatient technology to meet the medication information needs of cardiology patients. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium. 2012 Presented at: 2nd ACM SIGHIT International Health Informatics Symposium; January 28-30, 2012; Miami, Florida p. 831-836 URL: http://europepmc.org/abstract/MED/28018992 [CrossRef]
- Bickmore TW, Pfeifer LM, Jack BW. Taking the time to care: empowering low health literacy hospital patients with virtual nurse agents. 2009 Presented at: SIGCHI Conference on Human Factors in Computing Systems; April 4-9, 2009; Boston, Massachusetts. [CrossRef]
- Seljelid B, Varsi C, Solberg Nes L, Stenehjem A, Bollerslev J, Børøsund E. Content and system development of a digital patient-provider communication tool to support shared decision making in chronic health care: InvolveMe. BMC Med Inform Decis Mak 2020 Mar 04;20(1):46 [FREE Full text] [CrossRef] [Medline]
- Sun S, Zhou X, Denny JC, Rosenbloom TS, Xu H. Messaging to your doctors: understanding patient-provider communications via a portal system. 2013 Presented at: SIGCHI Conference on Human Factors in Computing Systems; April 27-May 2, 2013; Paris, France. [CrossRef]
- Magnani JW, Mujahid MS, Aronow HD, Cené CW, Dickson VV, Havranek E, American Heart Association Council on Epidemiology and Prevention; Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; Council on Peripheral Vascular Disease; Council on Quality of Care and Outcomes Research; Stroke Council. Health literacy and cardiovascular disease: fundamental relevance to primary and secondary prevention: a scientific statement from the American Heart Association. Circulation 2018 Jul 10;138(2):e48-e74 [FREE Full text] [CrossRef] [Medline]
- Yeung AWK, Souto EB, Durazzo A, Lucarini M, Novellino E, Tewari D, et al. Big impact of nanoparticles: analysis of the most cited nanopharmaceuticals and nanonutraceuticals research. Curr Res Biotechnol 2020 Nov;2:53-63. [CrossRef]
- Yeung AWK, Tzvetkov NT, Georgieva MG, Ognyanov IV, Kordos K, Jóźwik A, et al. Reactive oxygen species and their impact in neurodegenerative diseases: literature landscape analysis. Antioxid Redox Signal 2021 Feb 10;34(5):402-420. [CrossRef] [Medline]
- Yeung AWK, Tzvetkov NT, Jóźwik A, Horbanczuk OK, Polgar T, Pieczynska MD, et al. Food toxicology: quantitative analysis of the research field literature. Int J Food Sci Nutr 2020 Feb 29;71(1):13-21. [CrossRef] [Medline]
- Yeung AWK, Tzvetkov NT, El-Tawil OS, Bungǎu SG, Abdel-Daim MM, Atanasov AG. Antioxidants: scientific literature landscape analysis. Oxid Med Cell Longev 2019 Jan 08;2019:8278454-8278411 [FREE Full text] [CrossRef] [Medline]
- Durnin R, Glassner H, Jorgensen C, Fyler D. New approach to heart sound screening in children by analog-digital circuitry. Public Health Rep 1965 Sep;80(9):761-770 [FREE Full text] [Medline]
- Pemberton J, Li X, Karamlou T, Sandquist CA, Thiele K, Shen I, et al. The use of live three-dimensional Doppler echocardiography in the measurement of cardiac output: an in vivo animal study. J Am Coll Cardiol 2005 Feb 01;45(3):433-438 [FREE Full text] [CrossRef] [Medline]
- Pemberton J, Li X, Kenny A, Davies CH, Minette MS, Sahn DJ. Real-time 3-dimensional Doppler echocardiography for the assessment of stroke volume: an in vivo human study compared with standard 2-dimensional echocardiography. J Am Soc Echocardiogr 2005 Oct;18(10):1030-1036. [CrossRef] [Medline]
- Shiota T, Jones M, Chikada M, Fleishman CE, Castellucci JB, Cotter B, et al. Real-time three-dimensional echocardiography for determining right ventricular stroke volume in an animal model of chronic right ventricular volume overload. Circulation 1998 May 19;97(19):1897-1900. [CrossRef] [Medline]
- Mundt C, Montgomery K, Udoh U, Barker V, Thonier G, Tellier A, et al. A multiparameter wearable physiologic monitoring system for space and terrestrial applications. IEEE Trans Inf Technol Biomed 2005 Sep;9(3):382-391. [CrossRef] [Medline]
- Liang Y, Chen Z, Ward R, Elgendi M. Hypertension assessment via ECG and PPG signals: an evaluation using MIMIC database. Diagnostics (Basel) 2018 Sep 10;8(3):65 [FREE Full text] [CrossRef] [Medline]
- Liang Y, Chen Z, Ward R, Elgendi M. Photoplethysmography and deep learning: enhancing hypertension risk stratification. Biosensors (Basel) 2018 Oct 26;8(4):101 [FREE Full text] [CrossRef] [Medline]
- Xu H, Long H. The effect of smartphone app-based interventions for patients with hypertension: systematic review and meta-analysis. JMIR Mhealth Uhealth 2020 Oct 19;8(10):e21759 [FREE Full text] [CrossRef] [Medline]
- Islam SMS, Maddison R. A comparison of blood pressure data obtained from wearable, ambulatory, and home blood pressure monitoring devices: prospective validation study. JMIR Data 2020;1(1):e22436 [FREE Full text] [CrossRef]
- Bumgarner JM, Lambert CT, Hussein AA, Cantillon DJ, Baranowski B, Wolski K, et al. Smartwatch algorithm for automated detection of atrial fibrillation. J Am Coll Cardiol 2018 May 29;71(21):2381-2388 [FREE Full text] [CrossRef] [Medline]
- William AD, Kanbour M, Callahan T, Bhargava M, Varma N, Rickard J, et al. Assessing the accuracy of an automated atrial fibrillation detection algorithm using smartphone technology: the iREAD study. Heart Rhythm 2018 Oct;15(10):1561-1565. [CrossRef] [Medline]
- Kotecha D, Chua W, Fabritz L, Hendriks J, Casadei B, Schotten U, European Society of Cardiology (ESC) Atrial Fibrillation Guidelines Taskforce‚ the CATCH ME consortiumthe European Heart Rhythm Association (EHRA). European Society of Cardiology smartphone and tablet applications for patients with atrial fibrillation and their health care providers. Europace 2018 Feb 01;20(2):225-233 [FREE Full text] [CrossRef] [Medline]
- Curcio A, De Rosa S, Sabatino J, De Luca S, Bochicchio A, Polimeni A, et al. linical usefulness of a mobile application for the appropriate selection of the antiarrhythmic device in heart failure. Pacing Clin Electrophysiol 2016 Jul 19;39(7):696-702. [CrossRef] [Medline]
- Salvi D, Poffley E, Orchard E, Tarassenko L. The mobile-based 6-minute walk test: usability study and algorithm development and validation. JMIR Mhealth Uhealth 2020 Jan 03;8(1):e13756 [FREE Full text] [CrossRef] [Medline]
- Wongvibulsin S, Habeos EE, Huynh PP, Xun H, Shan R, Porosnicu Rodriguez KA, et al. Digital health interventions for cardiac rehabilitation: systematic literature review. J Med Internet Res 2021 Feb 08;23(2):e18773 [FREE Full text] [CrossRef] [Medline]
- Creutzfeldt J, Hedman L, Heinrichs L, Youngblood P, Felländer-Tsai L. Cardiopulmonary resuscitation training in high school using avatars in virtual worlds: an international feasibility study. J Med Internet Res 2013 Jan 14;15(1):e9 [FREE Full text] [CrossRef] [Medline]
- Perron JE, Coffey MJ, Lovell-Simons A, Dominguez L, King ME, Ooi CY. Resuscitating cardiopulmonary resuscitation training in a virtual reality: prospective interventional study. J Med Internet Res 2021 Jul 29;23(7):e22920 [FREE Full text] [CrossRef] [Medline]
- Elgendi M. On the analysis of fingertip photoplethysmogram signals. Curr Cardiol Rev 2012 Feb 01;8(1):14-25 [FREE Full text] [CrossRef] [Medline]
- Kwon S, Kim H, Park KS. Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone. 2012 Presented at: Annual International Conference of the IEEE Engineering in Medicine and Biology Society; August 28-September 1. 2012; San Diego, California. [CrossRef]
- Siontis KC, Noseworthy PA, Attia ZI, Friedman PA. Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nat Rev Cardiol 2021 Jul 01;18(7):465-478 [FREE Full text] [CrossRef] [Medline]
- Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. J Am Coll Cardiol 2018 Jun 12;71(23):2668-2679 [FREE Full text] [CrossRef] [Medline]
- Paz O, Zhou X, Gillberg J, Tseng H, Gang E, Swerdlow C. Detection of T-wave alternans using an implantable cardioverter-defibrillator. Heart Rhythm 2006 Jul;3(7):791-797. [CrossRef] [Medline]
- Porthan K, Viitasalo M, Toivonen L, Havulinna AS, Jula A, Tikkanen JT, et al. redictive value of electrocardiographic T-wave morphology parameters and T-wave peak to T-wave end interval for sudden cardiac death in the general population. Circ: Arrhythmia Electrophysiol 2013 Aug;6(4):690-696. [CrossRef]
- Yang C, Zhang W, Pang Z, Zhang J, Zou D, Zhang X, et al. A low-cost, ear-contactless electronic stethoscope powered by Raspberry Pi for auscultation of patients with COVID-19: prototype development and feasibility study. JMIR Med Inform 2021 Jan 19;9(1):e22753 [FREE Full text] [CrossRef] [Medline]
- Wali S, Guessi Margarido M, Shah A, Ware P, McDonald M, O'Sullivan M, et al. Expanding telemonitoring in a virtual world: a case study of the expansion of a heart failure telemonitoring program during the COVID-19 pandemic. J Med Internet Res 2021 Jan 22;23(1):e26165 [FREE Full text] [CrossRef] [Medline]
- Ahmadvand A, Kavanagh D, Clark M, Drennan J, Nissen L. Trends and visibility of "digital health" as a keyword in articles by JMIR publications in the new millennium: bibliographic-bibliometric analysis. J Med Internet Res 2019 Dec 19;21(12):e10477 [FREE Full text] [CrossRef] [Medline]
- Peng C, He M, Cutrona SL, Kiefe CI, Liu F, Wang Z. Theme trends and knowledge structure on mobile health apps: bibliometric analysis. JMIR Mhealth Uhealth 2020 Jul 27;8(7):e18212 [FREE Full text] [CrossRef] [Medline]
- Persell SD, Peprah YA, Lipiszko D, Lee JY, Li JJ, Ciolino JD, et al. Effect of home blood pressure monitoring via a smartphone hypertension coaching application or tracking application on adults with uncontrolled hypertension: a randomized clinical trial. JAMA Netw Open 2020 Mar 02;3(3):e200255 [FREE Full text] [CrossRef] [Medline]
- Cheng C, Beauchamp A, Elsworth GR, Osborne RH. Applying the electronic health literacy lens: systematic review of electronic health interventions targeted at socially disadvantaged groups. J Med Internet Res 2020 Aug 13;22(8):e18476 [FREE Full text] [CrossRef] [Medline]
- Walters R, Leslie SJ, Polson R, Cusack T, Gorely T. Establishing the efficacy of interventions to improve health literacy and health behaviours: a systematic review. BMC Public Health 2020 Jun 30;20(1):1040 [FREE Full text] [CrossRef] [Medline]
- Beauchamp A, Talevski J, Niebauer J, Gutenberg J, Kefalianos E, Mayr B, et al. Health literacy interventions for secondary prevention of coronary artery disease: a scoping review. Open Heart 2022 Jan;9(1) [FREE Full text] [CrossRef] [Medline]
- Brew-Sam N, Chib A. ow do smart device apps for diabetes self-management correspond with theoretical indicators of empowerment? an analysis of app features. Int J Technol Assess Health Care 2019 Apr 24;35(2):150-159. [CrossRef]
- Brew-Sam N, Chib A. How do smart device apps for diabetes self-management correspond with theoretical indicators of empowerment? an analysis of app features—CORRIGENDUM. Int J Technol Assess Health Care 2019 Jun 26;35(03):252. [CrossRef]
- Chow CK, Ariyarathna N, Islam SMS, Thiagalingam A, Redfern J. mHealth in cardiovascular health care. Heart Lung Circ 2016 Aug;25(8):802-807. [CrossRef] [Medline]
- MacKinnon GE, Brittain EL. Mobile health technologies in cardiopulmonary disease. Chest 2020 Mar;157(3):654-664 [FREE Full text] [CrossRef] [Medline]
- Wongvibulsin S, Martin SS, Steinhubl SR, Muse ED. Connected health technology for cardiovascular disease prevention and management. Curr Treat Options Cardiovasc Med 2019 May 18;21(6):29 [FREE Full text] [CrossRef] [Medline]
- Widmer RJ, Collins NM, Collins CS, West CP, Lerman LO, Lerman A. Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. Mayo Clin Proc 2015 Apr;90(4):469-480 [FREE Full text] [CrossRef] [Medline]
- Akinosun AS, Polson R, Diaz-Skeete Y, De Kock JH, Carragher L, Leslie S, et al. Digital technology interventions for risk factor modification in patients with cardiovascular disease: systematic review and meta-analysis. JMIR Mhealth Uhealth 2021 Mar 03;9(3):e21061 [FREE Full text] [CrossRef] [Medline]
- Meinhart F, Stütz T, Sareban M, Kulnik ST, Niebauer J. Mobile technologies to promote physical activity during cardiac rehabilitation: a scoping review. Sensors (Basel) 2020 Dec 24;21(1):65 [FREE Full text] [CrossRef] [Medline]
- Sarwar CM, Vaduganathan M, Anker SD, Coiro S, Papadimitriou L, Saltz J, et al. Mobile health applications in cardiovascular research. Int J Cardiol 2018 Oct 15;269:265-271. [CrossRef] [Medline]
- Shuaib W, Khan MS, Shahid H, Valdes EA, Alweis R. Bibliometric analysis of the top 100 cited cardiovascular articles. Am J Cardiol 2015 Apr 01;115(7):972-981. [CrossRef] [Medline]
- Franceschini F, Maisano D, Mastrogiacomo L. The museum of errors/horrors in Scopus. J Informetr 2016 Feb;10(1):174-182. [CrossRef]
Edited by A Mavragani; submitted 31.12.21; peer-reviewed by I Madujibeya, N Maglaveras; comments to author 07.02.22; revised version received 14.03.22; accepted 15.03.22; published 11.05.22Copyright
©Andy Wai Kan Yeung, Stefan Tino Kulnik, Emil D Parvanov, Anna Fassl, Fabian Eibensteiner, Sabine Völkl-Kernstock, Maria Kletecka-Pulker, Rik Crutzen, Johanna Gutenberg, Isabel Höppchen, Josef Niebauer, Jan David Smeddinck, Harald Willschke, Atanas G Atanasov. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.05.2022.
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