Abstract
Aim
This study aims to use bibliometric methods to analyze highly cited nursing education articles on digital health, particularly those 100 top cited publications between 2020 and 2024, from the Web of Science (WOS) Core Collection.
Design
A retrospective bibliometric analysis was conducted.
Methods
A bibliometric analysis of the most-cited digital health articles on nursing education in English with the highest citations. Data were sourced from the WOS Core Collection. Analysis was conducted using Excel and SPSS, while VOSviewer was used to visualize keyword trends. The analysis included examining journal distribution, author patterns, research types, methodologies, and keyword trends.
Results
The 100 most-cited articles were published between 1993 and 2024 by 91 different first authors from 22 countries. The United States contributed approximately 33% of the articles. Citation counts ranged from 198 to 1. Nurse Education Today was the most frequently cited journal. Review articles had higher average citations (50.96) compared to original articles (24.08). The focus of research has shifted from virtual reality (VR) to artificial intelligence (AI), with ChatGPT emerging as a new trend.
Conclusion
Digital health is becoming a significant focus in nursing education research. While VR has been a dominant topic, AI is now emerging as a key research area. The findings provide insights into citation patterns and research trends, supporting future impactful studies in this field.
Keywords
Introduction
Digital health, though still a somewhat undefined term, encompasses a wide range of existing and emerging applications. It is characterized as an interdisciplinary field that spans various topics, including nursing, medical technology, life sciences, and social sciences. The term “digital health” has now surpassed health informatics and eHealth in common usage. Potential topics for the application of digital health in nursing education are manifold, including artificial intelligence (AI), electronic health records (EHRs), telemedicine/telehealth, distance education, wearable technology, mobile health (mHealth)/electronic health (eHealth), virtual reality (VR)/augmented reality (AR), and nursing informatics, among others (the definition of terms showed Table 1). The primary goal of digital health is to enhance the efficiency of healthcare services and make medicine more personalized and precise. By harnessing both traditional and innovative information and communication technologies, digital health seeks to better understand health issues, meet healthcare demands, and optimize the delivery and management of nursing services, addressing the challenges faced by patients in a more personalized manner.
Definitions of digital health and related concepts.
AI: artificial intelligence; EHR: electronic health record; VR: virtual reality; AR: augmented reality.
Background
Bibliometrics, a quantitative analysis based on published articles, studies research trends, citation counts, publication timelines, authorship, institutions, countries, journals, and collaborative relationships by analyzing data from specific journals or topics.1–4 As a unique method to research evaluation, bibliometrics has gained widespread adoption in addressing researchers’ needs by establishing performance indicators based on the number of publications, citations, and other relevant parameters, forming a key part of academic productivity and research impact assessment.5–7 Another advantage of this method is its ability to objectively evaluate the rapidly growing body of literature, helping to design future research strategies. 8 Bibliometric reports also benefit readers by offering a comprehensive overview of the most influential works within a research field, through the analysis of citation patterns, author behaviors, and journal influence. 9
Citation analysis is a crucial method in bibliometric analysis,10,11 enabling the assessment of the impact of individual studies within a specific field of scientific research.12,13 The impact of a study is typically measured through citation analysis—the more a paper is cited, the greater its value within its field. 14 Although citation count alone cannot determine the quality of an article, it provides essential data for evaluating a paper's contribution to its field and the author's academic influence. 15 Today, citation count plays an important role in the evaluation of researchers, journals, and institutions, as well as in funding decisions. 16 Citations can be interpreted in various ways, including author metrics (such as the h-index) 17 or journal-level metrics. The h-index measures an author's impact based on their most-cited papers and the number of citations each paper has received. 17 Citation analysis can focus on the characteristics and trends of all articles or provide data on the features and tendencies of the most-cited papers. 18 The most-cited papers, also known as “key papers” or “classic papers,” represent significant attention to a specific field and reflect the mainstream research trends and future priorities within a discipline. These papers have a major impact on academic research and serve as excellent subjects for literature reviews.13,19 This method has been applied in multiple bibliometric studies.
Data from the WOS Core Collection indicate that as of 30 August 2024, there were a total of 348 bibliometric publications in the global nursing field. However, our results show that highly cited articles on digital health in the context of nursing education have not yet been thoroughly studied, which this research aims to address. Employing a bibliometric approach, combined with social network analysis (SNA), this study aims to construct a research network and categorize the key studies on digital health in nursing education. Unlike conventional literature reviews, this investigation applies advanced SNA to interpret the relationships among the most-cited articles, focusing on the intellectual structure of the most-cited research in digital health within nursing education over the past 5 years. By doing so, it seeks to uncover patterns and connections, such as widely discussed topics, research designs, key authors, and to explore a deeper understanding of the evolution and current landscape of digital health research in nursing education, shedding light on the most influential studies and their contributions to the field. This information will guide editors, nursing researchers, and readers in future research and clarify the current research network in the field, allowing researchers to recognize global research trends in digital health within nursing education. The focus on the most recent 5 years of literature can ensure that the analysis captures the most contemporary developments and trends in digital health research within nursing education. The rapid pace of technological advancements and the evolving landscape of healthcare necessitate an up-to-date perspective to reflect the current state of knowledge. By examining the most recent publications, the study can identify the most recent innovations, methodologies, and findings that have the potential to shape future research and practice.
Additionally, focusing on recent 5 years literature helps to ensure the relevance and applicability of the findings to contemporary nursing education settings.
As research on digital health in nursing education continues to grow, the need to identify the most-cited and most influential studies becomes more pronounced. Building on previous research recommendations, these studies aim to explore trends in highly cited research. 20 The objective of this study is to conduct a citation analysis of the top 100 most-cited digital health studies in nursing education from 2020 to 2024. Additionally, it aims to further explore the 20 most-cited research articles in this field. By investigating current trends and dynamics within the education research landscape in digital health and describing citation cycles and authorship patterns, the findings are anticipated to provide a solid knowledge base for planning and guiding future research.
Methods
Study design
A retrospective bibliometric analysis was conducted. The primary aim of this study is to clarify the scientific literature on digital health within the field of nursing education. Specifically, from a bibliometric perspective, our analysis strategy is informed by the methodologies used by.21,22 This research aims to describe the field of digital health in nursing education by identifying and analyzing the key characteristics of the top 100 most-cited studies published annually from 2020 to 2024. Additionally, it seeks to explore the top 20 studies from the Web of Science (WOS) Core Collection. Our analysis evaluates citation performance, including publication-related indicators and citation metrics.
The study aims to answer the following questions: 1. What are the distribution characteristics of the publication years for all digital health studies within the field of nursing education? 2. How do citation patterns for these studies? 3. What are the characteristics of the top 100 digital health studies in nursing education, in terms of journals, authors, institutions, countries, article types, and research methodologies? Furthermore, how do these studies perform in terms of citation counts and citation density (CD)? 4. What trends and patterns emerge from the keywords used in these top 100 studies?
By exploring these questions, the study seeks to provide a comprehensive overview of the current landscape of digital health research in nursing education, identifying key trends, influential works, and emerging themes.
Source of biblipmetric data and search strategy
Search was conducted on 30 August 2024, using the WOS Core Collection as the source of literature. The WOS Core Collection is a prestigious database that covers the nursing research field, providing high-quality articles with a rigorous citation count for each publication. 23 For each article, the database displays its citation count across other publications, enabling us to download citation-ranked data. The search strategy was predicated upon two main concepts: 1) “digital health,” which encompassed related descriptors such as digital health, AI, EHRs, telemedicine/telehealth, distance education, wearable technology, mHealth/eHealth, VR/AR, and nursing informatics to capture the most relevant literature; and 2) “nursing education,” which included nursing education at various levels.
The initial search strategy was developed based on several potential keywords to determine the applicability of search terms and the scope of available literature. Minor adjustments were made before finalizing the search strategy. The final search was limited to English-language articles and restricted to Article or Review Article types, with no time constraints on the search.
The specific search string applied in the WOS Core Collection was as follows: TS = (“Chatbot” OR “ChatGPT” OR “Digital health” OR “Artificial intelligence” OR “Remote monitoring” OR “Distance Education” OR “Telehealth” OR “Telemedicine” OR “Digital technology” OR “eHealth” OR “nursing informatics” OR “virtual reality” OR “virtual simulation” OR “Mixed reality” OR “mHealth” OR “mobile health” OR “Wearable Technology” OR “electronic health records” OR “EHRs”) AND TS = (“Nurs* Education” OR “Nurs* Student”). The search was further refined by limiting the results to English-language and Article/Review Article types. Conducted on 30 August 2024, the search yielded a total of 876 publications, including 753 research articles and 123 review articles.
The classification types of articles were determined according to the classification criteria on the WOS online database. Research articles are defined as original studies that report empirical findings, including quantitative, qualitative, and mixed-methods research, etc. Review articles, on the other hand, are defined as scholarly papers that synthesize existing research on a particular topic, summarizing and evaluating the literature to provide a comprehensive understanding. For the purpose of this study, both research and review articles were included to gain a comprehensive view of the current state of nursing education research related to digital health technologies. The selected articles were then analyzed using a mixed-methods approach, combining both quantitative and qualitative data analysis techniques to extract relevant information and insights.
Researchers manually reviewed all studies for relevance, reaching a consensus on inclusion. Results categorized as Early Access, Book Chapters, Proceeding Papers, or Retracted Publications were excluded. Studies that were unrelated to digital health, did not involve nursing participants, or were focused on therapeutic research (e.g. rehabilitation or mental health nursing) were also excluded. Additionally, studies that discussed digital health competencies or nursing informatics competencies—like computer or information literacy—without a clear connection to nursing education were not included. Furthermore, research centered on medical technologies (e.g. computed tomography scans and surgical techniques) was also excluded. Ultimately, 804 articles were included in the final analysis. The searches and refinements are conducted in accordance with the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol as outlined by Paul et al. 24 See Table 2.
The SPAR-4-SLR protocol. As recommended by Paul et al.
This database offers a broad scope and tracks the annual citation record for each article since publication. Within the WOS Core Collection, titles were sorted by citation count per publication year (2020–2024), and the top 20 most-cited studies from 100 top cited publications in total were downloaded into an Excel spreadsheet, along with the full-text articles, which were stored in the Zotero library (vision 7.0.6). Errors such as formatting issues and duplicates were checked and corrected for further analysis and evaluation by the researchers. Figure 1 illustrates the article selection process.
Data selection process.
A total of 1032 publications were found as records belonging to the nursing education in digital health in WOS. After screening research type, we got 876 publications. Remove 72 publications which were non-nursing education OR digital health-related items, we got 804 entries.
Data analysis
Data quality, before the main analysis, was ensured by detecting duplicates, standardizing author and institution names, and identifying other data errors. Following these checks, bibliographic and thematic analyses were conducted to quantify bibliographic indicators such as citation frequencies, patterns, prolific authors, journals, and countries.
Researchers collected the complete dataset for each article from the WOS database for subsequent analysis, including the following variables: (a) the year of publication; (b) the citation count in WOS Core as of 30 August 2024; (c) the journal in which the study was published; (d) the names of the authors; (e) the institution and country of the first author; and (f) the type of publication. A descriptive quantitative analysis of these study variables was conducted using IBM-SPSS Statistics version 27 (IBM Corp., 2020). The top journals from the 100 selected articles were organized, and publication-related metrics were extracted from journal websites. Subsequently, a keyword analysis and data visualization were performed using VOSviewer (version 1.6.20). The results of our study are presented in results.
Analysis of publication year, citation analysis, journal analysis, author analysis and study types, and research methods were calculated based on the extracted data. The publication year analysis provided insights into the temporal distribution of research on the topic. Citation analysis was conducted by counting metrics such as the total number of citations and the average citations per year. Journal analysis involved examining the distribution of articles across different journals, focusing on the prestige of the publishing venue. Author analysis revealed collaboration patterns and the most productive authors in the field. Study types and research methods were classified to understand the prevalent approaches employed in the research. These analyses collectively offer a comprehensive understanding of the research trends and patterns in the selected studies.
Validity, reliability and rigor
All the studies included in the research were examined separately for their suitability and consensus was reached.
Ethical considerations
Since the research does not have a direct impact on people or animals, no approval from an ethical committee was sought.
Result
Analysis of publication year
We analyzed the distribution of publication years. Although no time limits were set during the search, the earliest study was published in 1993 (Figure 2). The median publication year was 2022, making the median age of the articles 3 years. As shown in Figure 2, the years with the highest number of studies were 2023 and 2024 (153 studies each). There has been a significant increase in publications over the past 5 years (since 2020), with 74.25% of the articles published during this period.
Publication years of digital health studies in nursing education.
Citation analysis of the 804 studies revealed a total of 10,093 citations. The year with the highest number of citations was 2023 (2349 citations). Additionally, the data showed a steady increase in citations since 2020, with 81.1% of total citations occurring in the past 5 years (2020–2024).
It is important to note that, following the initial analysis of the 804 studies, this paper will focus on high-citation articles published within the last five years. This approach more accurately reflects recent trends in the field.
Citation analysis of the selected 100 studies
The top 20 most-cited digital health studies in nursing education from 2020 to 2024, ranked by citation count, are shown in Figure 3. The total number of citations for each study ranged from 1 to 198. On average, each study received 30.8 citations, with a total of 3080 citations across the 100 studies. The full list of the 100 studies, along with their citation rankings is detailed in Supplementary Table A.
Distribution of citation counts for the top 20 most-cited digital health studies in nursing education (a total of 100 studies) from 2020 to 2024.
Journal analysis of the selected 100 studies
The top 100 most-cited studies were published across 38 different journals. The number of publications per journal ranged from 1 to 22. Fifteen journals had two or more publications, while 23 journals had only one. Supplementary Table B lists publications and citations by journal authors among the 100 selected studies, including Nurse Education Today, which published 22 studies and accumulated 749 citations, Clinical Simulation in Nursing, which published 9 studies with 219 citations, and Nurse Educator, which published 8 studies with 225 citations. These journals lead research and development in the field. Beyond these, the number of highly cited articles per journal significantly decreased: BMC Nursing (5 studies), Interactive Learning Environments (5 studies), and International Journal of Nursing Studies (5 studies). Another 32 journals, mostly nursing-related, also published studies that were highly cited studies. The Kruskal–Wallis test results showed: Statistic (H): 14.87, p-value: 0.00499. This indicates that there are significant differences in citation rates among various journals, meaning citation distribution is not uniform across journals, with at least one journal showing a significantly different citation rate. The citation analysis ranked Nurse Education Today first (749 citations), followed by Nursing Education Perspectives (296 citations) and Nursing Open (249 citations). CD analysis revealed the top three journals as Academic Medicine (CD = 198.0), Nursing Education Perspectives (CD = 98.7), and Nursing Open (CD = 83.0).
Author analysis of the selected 100 studies
Table 3 highlights the most productive first authors among the top 100 most-cited digital health studies. A total of 91 different first authors Contributed to these studies, with publication counts from 1 to 6 studies. We identified leading authors within this group. Notably, the most productive author was Ching-Yi Chang from Taiwan, China, who led 6 studies and has an h-index of 16. Four other authors led two studies each: Sun-Yi Yang, Domenic Sommer, Evan Keys, and Niki Fogg, with h-indices of 4, 7, 3, and 6, respectively. Additionally, the 100 studies involved authors from 320 different institutions, with institutional publication counts ranging from 1 to 6 studies. State University System of Florida and Taipei Medical University were the most productive institutions, each contributing 6 studies (Table 4). Figure 4 and Table 5 show the distribution of publication count, citation count, and CD by country. It is noteworthy that the top 100 most-cited studies were authored by researchers from 22 countries. Most of the studies (33 studies, 1162 citations) were led by authors from the United States, followed by Taiwan, China (15 studies, 377 citations) and South Korea (11 studies, 220 citations). These three countries account for 57% of the total studies and 77.9% of the total citations. Furthermore, citation count analysis ranked the United States [Times Cited (TC) = 1162], China (TC = 378), and China, Taiwan (TC = 377) among the top contributors.
Distribution of publication count, citation count, and citation density for the selected studies, categorized by country or region.
Number of publications and citations by authors among the 100 selected studies.
Number of publications and citations by affiliations among the 100 selected studies.
Number of publications and citations by country/region among the 100 selected studies.
Analysis of study types and research methods in the selected 100 studies
The 100 studies selected from the past 5 years were further analyzed by publication type: 75 were original articles, and 25 were review articles. The review articles not only had the highest total citation count (1274), but also the highest CD (50.96). Among the original articles, most employed descriptive research methods (n = 30), followed by experimental studies (n = 11) and cross-sectional studies (n = 9). Followed by quasi-experimental studies (n = 6), qualitative studies (n = 6), randomized controlled trials (n = 4), mixed-methods studies (n = 3), and other studies(in Table 6). The Kruskal–Wallis test did not revealed any statistically significant differences in citation count based on research methods used in original articles (H = 14.277, df = 13, p = 0.355).
Number of publications and citations by research methods of original articles among the 100 selected studies.
In review articles, the most common research method was systematic review (n = 12), followed by scoping reviews (n = 6) and Umbrella Review (n = 2). Other methods included Meta-analysis (n = 1) and umbrella reviews (n = 1),etc. (Table 7). The Kruskal–Wallis test found no statistically significant differences in citation counts across research methods used in review articles (H = 11.552, df = 7, p = 0.116).
Number of publications and citations by research methods of review articles among the 100 selected studies.
Social network analysis of the selected 100 studies
Keywords are tools that help indexers and search engines locate relevant publications. By analyzing keywords in scientific research, we can identify topic trends within publications. 25 The most frequently used keywords shed light on the most researched topics. 26 To identify the key characteristics of the top 20 most-cited digital health studies in nursing education from 2020 to 2024 (a total of 100 studies), this research applied SNA. SNA helps generate visual network maps to reveal potential groupings among the literature.
While many sociologists use SNA to analyze human relationships, in this study, we focus on uncovering the relationships among the selected 100 studies. SNA serves as a supplementary method for visualizing the relationships between the studies. Additionally, we attempted to divide the keyword network into subgroups to explore whether research focuses varied across years. For visualizing the network relationships between the studies, 27 some researchers have reported that VOSviewer is a powerful tool for bibliometric network mapping. This study followed prior research and used VOSviewer to visualize the keyword analysis network of the selected 100 studies.
Among the top 100 most-cited studies, authors used a total of 296 different keywords. Only keywords that appeared more than three times were included in the network (Figure 5A, 5B). The size of the circles represents the frequency of keyword usage, while colors indicate keyword clusters. The most frequently used keywords were “Virtual reality (N = 20),” “Artificial intelligence (N = 17),” “Simulation (N = 14),” and “ChatGPT (N = 13).” Keyword clustering identified the main thematic groups in the research. Cluster 1 included “Virtual reality,” “augmented reality,” and “distance education” (blue). Cluster 2 contained “artificial intelligence,” “ChatGPT,” and “nursing informatics” (green). Cluster 3 comprised “virtual simulation” and “simulation training” (purple). Cluster 4 included “simulation” and “immersive virtual reality” (yellow). Cluster 5 featured “chatbot,” “professional training,” “experiential learning,” and “mobile learning” (red). The first group focused on VR, the second on AI, the third on virtual simulation, the fourth on simulation, and the fifth on chatbot. This clustering aligns with the thematic areas of the journals that published the most articles. Considering these groups, it can be stated that digital health research in nursing education covers a wide array of topics. It would be beneficial to consider the most used keywords identified in this study when planning future research. Over the past 5 years, we observed a shift in keyword clusters from an initial focus on VR to a growing emphasis on AI, particularly in the rapid rise of ChatGPT-related studies in the past 2 years (Figure 5C, 5D).
VOSviewer visualization analysis of author keywords.
Detailed analysis of the top 10 studies
Among the top 20 most-cited digital health studies in nursing education from 2020 to 2024 (a total of 100 studies), the top 10 most-cited were selected as milestone studies due to their high-citation counts. These studies are often regarded as having achieved a degree of excellence in the field and thus warrant further analysis and reporting of results. The top10 citation counts of these studies in WOS ranged from 67 to 198 (Table 8).
Top 10 studies related to digital health among the 100 selected studies.
The most highly cited study in the digital health field within nursing education is Virtual Simulation in Nursing Education: A Systematic Review Spanning 1996 to 2018, cited 198 times. This study, conducted by Cynthia L. Foronda and published in Simulation in Healthcare: Journal of the Society for Simulation in Healthcare in 2020, reviewed the effectiveness of virtual simulation in nursing education, emphasizing the need for optimized research design. Its relevance to key topics in digital health likely contributed to its high-citation count. The second most-cited study, authored by Richard A. Smiley (2021), accumulated 196 citations, and examined the education, employment, and diversity of nurses in the U.S., finding improvements in nurse education levels and diversity. The third most-cited study, with 178 citations, was authored by Feng-Qin Chen (2020) and published in the Journal of Medical Internet Research. Titled Effectiveness of Virtual Reality in Nursing Education: Meta-Analysis, the study analyzed the impact of VR on nursing education. Following these top three, citation counts dropped quickly, with only six studies exceeding 100 citations.
The fourth most-cited study, Blended Learning via Distance in Pre-Registration Nursing Education: A Scoping Review by Tanisha Jowsey (2020), was published in Nurse Education in Practice and received 126 citations. It reviewed the positive impact of blended learning on nursing students and called for further exploration of satellite campus learning. In 2020, many studies focused on Simulation, with some focusing on Virtual Simulation. The fifth most-cited study, conducted by Shefaly Shorey (2021), titled The Use of Virtual Reality Simulation among Nursing Students and Registered Nurses: A Systematic Review, was published in Nurse Education Today and cited 119 times. It reviewed the use of VR in nursing education, noting its effectiveness in enhancing theoretical knowledge but with limited impact on skills and emotions. The sixth study, by Christian Plotzky (2021), titled Virtual Reality Simulations in Nurse Education: A Systematic Mapping Review, also published in Nurse Education Today, received 107 citations. It reviewed the application of VR in nursing education, highlighting its effectiveness in theoretical learning but identifying areas for improvement in psychological and practical skills training.
The seventh most-cited study, conducted by Richard A. Smiley (2023), titled the 2022 National Nursing Workforce Survey, cited 85 times, explored nurse turnover and education levels in the U.S., finding a younger and more diverse workforce with increased salaries. The eighth study, conducted by Ching-Yi Chang (2022), titled Effects of Spherical Video-Based Virtual Reality on Nursing Students’ Learning Performance in Childbirth Education Training, was published in Interactive Learning Environments and cited 76 times. It explored the use of spherical video-based VR in childbirth education, improving student motivation and satisfaction. The ninth study, authored by Richard A. Smiley (2020), titled Transitioning from Direct Care to Virtual Clinical Experiences during the COVID-19 Pandemic, received 69 citations and explored the effectiveness of virtual clinical learning during the pandemic, finding that virtual simulation effectively replaced face-to-face clinical learning. The tenth study, cited 67 times, was authored by Hanna von Gerich (2022) and published in the International Journal of Nursing Studies. Titled Artificial Intelligence-Based Technologies in Nursing: A Scoping Literature Review of the Evidence, the study reviewed the use of AI-assisted telehealth in nursing, noting its potential to improve patient care and satisfaction.
The h-index scores of the first authors are widely recognized and are explained as follows: an h-index of 3 means the author has three papers, each cited at least three times. The h-index of the first authors of the top 10 studies ranged from 2 to 35 (Table 8), with the first author of the most-cited study 28 having the second-highest h-index (18). Five of the top 10 first authors had h-indices of 7 or less, indicating that they have relatively few publications. The authors of the sixth and tenth most-cited studies had the lowest h-indices, both at 2 (Table 8). The 10 studies were published across 8 different journals, with impact factors (IFs) ranging from 7.5 for International Journal of Nursing Studies, 5.8 for Journal of Medical Internet Research, and 4.2 for Nurse Education in Practice and Journal of Nursing Regulation, to less than 4 for the other four journals. The higher the IF, the more prestigious the journal (Table 8).
Discussion
This study aimed to analyze the top 20 most-cited digital health studies from a total of 100 studies published in 2020–2024 year in the nursing education field, as indexed in the WOS database. Through bibliometric analysis and SNA, this research identified the most significant articles, journals, authors, countries, institutions, and themes in the field of digital health, providing a reference for future scientific research.
Is there evidence that certain journals are more likely to be cited than others? While no definitive evidence has been found in this regard, some journals do appear to enjoy greater popularity than others. Scientific knowledge is disseminated to professionals in the field through published research articles and scientific literature, making journals influential in sharing research with the nursing community. This study highlights that the most frequently cited research in the field of digital health within nursing education was published in journals such as Nurse Education Today, Clinical Simulation in Nursing, and Nurse Educator. According to the WOS database journal ranking standards, Nurse Education Today falls under the “nursing” category, while Clinical Simulation in Nursing is listed under both the “nursing” and “education, scientific disciplines” categories, and Nurse Educator is categorized under “nursing” as a first-quartile journal (Q1). Furthermore, these journals are indexed in the Science Citation Index Expanded under the nursing category in the Clarivate Analytics WOS database. Their respective IF of 3.6, 3.4, and 2.4 indicate relatively high rankings.
We acknowledge that not all English-language journals are indexed in the WOS database, which requires a subscription from publishers. Journals independently choose which platforms they are indexed in, and some English-language journals are not included in WOS. Nurse Education Today, the most frequently cited journal, is indexed in 14 different databases, including WOS (https://www.sciencedirect.com/journal/nurse-education-today). As a result, articles published in these journals are more likely to be recognized in data searches and subsequently cited. Conversely, articles in journals indexed by fewer or lesser-known databases may be overlooked. This discrepancy suggests that citation counts are better understood as indicative metrics rather than as an entirely objective standard. Nonetheless, we are confident that the data included in this analysis are based on reliable sources from the WOS database.
In this study, we analyzed 100 top cited publications in 2020–2024 years and do not included old publications which may accumulated more citations. Although this approach may limit the scope of our analysis, meanwhile can enable us to focus on the most recent and potentially influential research in the field. By examining these recent publications, we aimed to capture the current trends, advancements, and areas of focus within the research community. Moreover, we also should consider the possible bias such as self-citations and journal IFs can inflate citation counts, geographical biases and overlooks non-citation-based impact. So it is recommended to analyze a random sample of articles instead of only relying on citation rankings as well as using bibliometric studies guidelines. 29 In this study, we use the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol, and other guidelines, such as the San Francisco Declaration on Research Assessment (https://sfdora.org/) 30 and the Leiden Manifesto (http://www.leidenmanifesto.org/), 31 can be used for evaluative purposes, which can highlight the importance of rigorousness in bibliometric methodology and provide a framework for ensuring that such methodologies are used appropriately. Despite these potential biases, our study provides valuable insights into the most recent and influential research trends in the field. By using SNA, we were able to identify key clusters of research and the most-cited studies, which can serve as a roadmap for future research. Additionally, this analysis highlights the importance of considering a variety of factors when evaluating the impact of research, beyond just citation counts. Overall, our study contributes to the ongoing discussion about the most relevant and impactful research in this field, and we hope that it will inspire further exploration and analysis.
This study confirms that review articles are cited more often on average than original research papers. While review articles play a crucial role in synthesizing and summarizing existing knowledge, their prominent citation rates could potentially skew the overall representation of empirical nursing research. This is because review articles often aggregate findings, which making them a comprehensive resource for researchers. It is important to strike a balance between recognizing the value of review articles and ensuring that empirical research continues to receive the attention and recognition it deserves. Precious study indicated the publication way of Open Access affected citation rates. 32 In examining the research methodologies employed in nursing education studies, it was found that most utilized descriptive research, followed by experimental research. Additionally, researchers employed quasi-experimental designs, qualitative studies, and randomized controlled trials to explore various aspects of nursing practice. Therefore, in the field of nursing education, particularly in digital health, it is essential not to rely solely on descriptive research, but to enhance study quality through in-depth experiments and interviews. Moreover, the analysis of publication years revealed a significant increase in the number of studies on nursing education, indicating a growing interest in this field. This growth could be attributed to advancements in technology and the increasing recognition of the importance of nursing education in improving patient outcomes. The SNA of the selected 100 studies further highlighted the interconnectedness of research in this area, with key authors and institutions frequently cited and collaborating. The detailed analysis of the top 10 studies provided insights into the most impactful research, identifying common themes and methodologies that have shaped the field. However, it is important to acknowledge that the potential bias in the selection of studies and the possibility that some relevant studies were not included in the analysis. Despite these, the findings of this study contribute to the existing knowledge base and provide valuable insights for future research in nursing education, particularly in the context of digital health.
Future research in digital health within nursing education should focus on conducting larger scale studies involving more diverse populations to validate the findings from the current body of work. Furthermore, it is essential to examine the long-term impact of digital health interventions on student outcomes and patient care. Additionally, it is necessary to explore the ethical considerations and potential biases associated with digital health technologies in nursing education, ensuring that students are adequately prepared to navigate these complexities in their future practice. Emerging technologies, such as AI, should also be examined for their integration with other innovative technologies like AR and mixed reality to enhance learning experiences and outcomes. Moreover, it is vital to investigate the long-term impact of AI-driven educational tools on nursing students’ clinical judgment, patient care skills, and overall professional development. Future studies should also address ethical considerations and potential biases related to the use of AI in nursing education, ensuring that the integration of these technologies aligns with the values and principles of the nursing profession.
Bibliometric analysis of digital health research in nursing education, as presented in this study, can provide nursing schools and educators valuable insights for developing modern digital health curricula and incorporating them into teaching syllabi. As the largest group of healthcare providers, nurses are at the forefront of the digital health transformation.32,33 Preparing and educating nurses on digital health is a priority to ensure that both existing and emerging digital health technologies are used to deliver safe, effective, and efficient patient care.32,34 This education also enables nurses to participate meaningfully in the design, development, implementation, and evaluation of digital health technologies.
Strengths and limitations
The primary strength of this study lies in its investigation of the bibliometric characteristics of the most-cited studies in digital health within nursing education over the past 5 years. By using SNA to visualize the data, this study represents the first bibliometric analysis of 20 studies annually (100 in total) over the last 5 years in this domain. This comprehensive review demonstrates the breadth of research on digital health within the nursing field over the past 5 years. Citation rates or research gaps, as revealed through citation analysis, have proven to be valuable for informing research policy, such as funding decisions. 35 Moreover, individual citation rates can highlight the performance and impact of specific researchers, emphasizing the importance and influence of their work. 36
We have some limitations in this study. Firstly, we only analyze the publication year, citation analysis, journal analysis, author analysis, and study types and research methods in this study, thus do not deeper analyze the specific content and quality of the articles. And we only citation-based impact citation impact, do not take empirical article, clinical practice, etc. Into account, which may cause some bias in the results. Citations can be supplemented with qualitative assessments of research influence to ensure a more nuanced understanding of the significance and impact of research studies, which we can do in the future. Secondly, our study is confined to the selected 100 studies from the WOS database, which may not fully represent the overall landscape of nursing informatics research. Additionally, the studies selected for analysis were published in English, which may introduce a language bias and limit the applicability of the findings to non-English-speaking contexts. Furthermore, the analysis was conducted based on the information available in the published studies, and it may not capture all the nuances and details of the digital health technologies discussed. Finally, the publication way, such as Open Access publication and traditional publication method, was not discussed in this study, which would also be a potential limitation. Therefore, future studies should consider a broader range of analysis and include more diverse sources to obtain a more comprehensive understanding of the field.
Conclusion
Research on digital health in nursing education is a continuously evolving emerging field, although it currently has relatively low citation rates. As nursing education and practice continue to advance in the digital transformation of healthcare, the concept of digital health within nursing education will keep expanding. Digital health education has become a priority in nursing education, and educators must become expert facilitators in this approach. Concurrently, the exponential growth in publications on digital health in nursing education underscores the importance of identifying the most influential and highly cited articles to enhance knowledge.
This bibliometric analysis identified the top 20 most-cited studies per year (a total of 100 studies) on digital health in the nursing education field over the past 5 years. The analysis revealed the most-cited authors, research articles, and journals in digital health, which included descriptive and/or experimental studies as well as research reviews. Based on these findings, this study provides valuable data to inform future research on digital health in nursing education. Notably, the most-cited articles in this field primarily focus on VR, AI, and chatbots. Future researchers are encouraged to continue exploring these topics to enhance the understanding of digital health in nursing education.
However, among the top 100 cited studies, there were few that involved international author collaborations. Establishing an international center of excellence for digital health nursing education could help promote and facilitate multinational research and collaborative efforts among authors. Lastly, it is important for nursing researchers and educators to understand both the uses and limitations of bibliometric analysis, as it offers unique contributions to research policy and funding decisions and can also be used to assess the productivity of nursing institutions and schools.
Supplemental Material
sj-docx-1-dhj-10.1177_20552076251342165 - Supplemental material for Bibliometric insights into the top 100 most-cited annual studies on digital health in nursing education (2020–2024)
Supplemental material, sj-docx-1-dhj-10.1177_20552076251342165 for Bibliometric insights into the top 100 most-cited annual studies on digital health in nursing education (2020–2024) by Chunxiu Zhou and Lili Ma in DIGITAL HEALTH
Footnotes
Acknowledgments
The authors express their profound appreciation to the dedicated staff at the Precision Health Management Center, Shanghai East Hospital, Tongji University School of Medicine, for their unwavering support and contributions to their research endeavor.
Author contributions
ZHOU and MA made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data. ZHOU involved in drafting the manuscript or revising it critically for important intellectual content. MA given final approval of the version to be published. Each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content. ZHOU and MA agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Statistics statement
The authors affirm that the methods used in the data analyses are suitably applied to their data within their study design and context, and the statistical findings have been implemented and interpreted correctly. The authors agree to take responsibility for ensuring that the choice of statistical approach is appropriate and is conducted and interpreted correctly as a condition to submit to the Journal.
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References
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