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Due to the widespread and unprecedented popularity of mobile phones, the use of digital medicine and mobile health apps has seen significant growth. Mobile health apps have tremendous potential for monitoring and treating diseases, improving patient care, and promoting health.
This paper aims to explore research trends, coauthorship networks, and the research hot spots of mobile health app research.
Publications related to mobile health apps were retrieved and extracted from the Web of Science database with no language restrictions. Bibliographic Item Co-Occurrence Matrix Builder was employed to extract bibliographic information (publication year and journal source) and perform a descriptive analysis. We then used the VOSviewer (Leiden University) tool to construct and visualize the co-occurrence networks of researchers, research institutions, countries/regions, citations, and keywords.
We retrieved 2802 research papers on mobile health apps published from 2000 to 2019. The number of annual publications increased over the past 19 years.
We summarize the recent advances in mobile health app research and shed light on their research frontier, trends, and hot topics through bibliometric analysis and network visualization. These findings may provide valuable guidance on future research directions and perspectives in this rapidly developing field.
Worldwide, the use of mobile phones has reached widespread popularity at an unprecedented rate [
Bibliometric analysis has been widely used in quantitative analysis of academic literature to describe the hot spots, trends, and contributions of scholars, journals, and countries/regions [
Web of Science (WOS) is an extensive international database of academic information, including more than 9000 prestigious and high-impact research journals from all over the world. WOS contains various characteristics that can be used for bibliometric study, including title, author, institution, country/region, publication year, and keywords [
We aimed to exploit bibliometric analysis to identify the knowledge structure, research frontiers, research hot spots, active authors, and other bibliometric information in the mobile health app area. Bibliometric analysis typically consists of the construction of bibliometric maps and the graphical representation of such maps [
In this study, we have applied widely used bibliometric analysis tools on the WOS data. Bibliographic Item Co-Occurrence Matrix Builder version 2.0 [
Using network-mapping techniques, we created different bibliometric maps that included coauthorships of authors, institutions, and countries/regions; co-citations of references; and co-occurrence of keywords. Each node in a map is represented by a circle with a label. Larger circles indicate higher-frequency items. The color of each circle is determined by the clusters it belongs to. The thickness and length of links between nodes represent the association strength between corresponding nodes. A maximum of 500 lines was set to display the 500 strongest links between nodes.
Data from bibliographic information were searched and downloaded from WOS. These were publicly available data. The extraction of these data did not involve interaction with human subjects or animals. Thus, there were no ethical issues involving the use of these data, and no approval from an ethics committee was required.
Based on our search strategy, we identified and incorporated 2802 publications on mobile health apps from WOS. The number of annual publications on mobile health apps increased from 2 publications in 2000 to 692 publications in 2018 (2019 data are incomplete because they reflect only approximately 9 months of publications). Before 2013, the number of annual publications did not exceed 100. However, the number of annual publications in 2014, 2015, 2016, 2017, and 2018 was 122, 263, 430, 507, and 692, respectively.
Publications on mobile health app research were distributed across 1209 journals; 848 of these journals have published only 1 paper on mobile health apps.
Top 10 journals publishing research on mobile health app research, 2000-2019.
Rank | Journal | Country | Categories | Publications, n | Percentagea |
1 |
|
Canada | Medical informatics | 323 | 11.53 |
2 |
|
Canada | Medical informatics | 106 | 3.78 |
3 |
|
Canada | Medical informatics | 82 | 2.93 |
4 |
|
United States | Multidisciplinary sciences | 47 | 1.68 |
5 |
|
United States | Medical informatics | 43 | 1.53 |
6 |
|
England | Medical informatics | 37 | 1.32 |
7 |
|
Ireland | Medical informatics | 37 | 1.32 |
8 |
|
United States | Health care sciences and services | 37 | 1.32 |
9 |
|
Canada | Medical informatics and mental health | 33 | 1.18 |
10 |
|
England | Medicine, general and internal | 31 | 1.11 |
aThe total number of retrieved papers on mobile health apps from 2000 to 2019 (N=2802) was used as the denominator.
According to the search results, 2802 publications came from 104 countries/regions.
Top 30 countries/regions publishing mobile health app research, 2000-2019.
As shown in
The coauthorship network of countries/regions that contributed to mobile health app research, 2000-2019. Peoples R China: People's Republic of China. USA: United States of America.
According to the search results, 3795 research institutions contributed to mobile health app research.
Top 10 most productive institutions in mobile health app research, 2000-2019.
Rank | Institution | Country | Publications, n | Citations, n |
1 | Univa of California San Francisco | United States | 67 | 819 |
2 | Univ of Washington | United States | 58 | 511 |
3 | Univ of Toronto | Canada | 56 | 640 |
4 | Stanford Univ | United States | 46 | 432 |
5 | Univ of Pittsburgh | United States | 45 | 409 |
6 | Harvard Medical School | United States | 42 | 408 |
7 | Columbia Univ | United States | 39 | 387 |
8 | Northwestern Univ | United States | 39 | 296 |
9 | Univ of Sydney | Australia | 39 | 240 |
10 | Seoul National Univ | South Korea | 33 | 184 |
aUniv: university.
Coauthorship analysis was performed by VOSviewer to display the visualization network map of institutions in mobile health app research. The link between institutions is determined by the number of publications coauthored between them. The coauthorship analysis of institutions shows that 99 institutions, each of which published at least 10 papers, formed 8 clusters. These clusters are shown in
The coauthorship network of institutions that contributed to mobile health app research, 2000-2019. Univ: university.
According to the search results, 2802 mobile health app publications were written by 13,040 authors, with an average of 5 authors per publication.
Top 10 most productive authors in mobile health app research, 2000-2019.
Rank | Author | Publications, n | Citations, n |
1 | Schnall R | 15 | 217 |
2 | Kuhn E | 14 | 223 |
3 | Lopez-Coronado M | 14 | 130 |
4 | Kim J | 14 | 43 |
5 | Lee S | 13 | 365 |
6 | Li J | 13 | 49 |
7 | Torous J | 12 | 271 |
8 | Lee JH | 10 | 107 |
9 | Lee J | 10 | 46 |
10 | Zhang Y | 10 | 36 |
Our coauthorship analysis of authors showed that 221 of 13,040 authors had published at least 4 papers, and the largest set of associated authors consisted of 95 authors in 6 clusters. The node label shows the author's name, and the node size represents the number of published publications. Links connecting 2 nodes represent coauthorship between the 2 authors, and thicker links represent more collaboration between the 2 authors, as shown in
The coauthorship network of authors who contributed to mobile health app research, 2000-2019.
Through the co-citation analysis (examining references cited in publications), we explored the knowledge base for the mobile health apps field. We identified 2802 mobile health app publications, which cited 76,721 references, averaging 27 references per publication. The top 10 most frequently cited references are listed in
Top 10 cited references in mobile health app research, 2000-2019.
Rank | Author | Journal | Title | Citations, n |
1 | Stoyanov SR et al (2015) |
|
Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps | 106 |
2 | Free C et al (2013) |
|
The Effectiveness of Mobile-Health Technologies to Improve Health Care Service Delivery Processes: A Systematic Review and Meta-Analysis | 101 |
3 | Braun V and Clarke V (2006) |
|
Using Thematic Analysis in Psychology | 98 |
4 | Donker T et al (2013) |
|
Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review | 85 |
5 | Krebs P and Duncan DT(2015) |
|
Health App Use Among US Mobile Phone Owners: A National Survey | 79 |
6 | Davis FD (1989) |
|
Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology | 78 |
7 | Dennison L et al (2013) |
|
Opportunities and Challenges for Smartphone Applications in Supporting Health Behavior Change: Qualitative Study | 77 |
8 | Mosa AM et al (2012) |
|
A Systematic Review of Healthcare Applications for Smartphones | 71 |
9 | Luxton DD et al (2011) |
|
mHealth for Mental Health: Integrating Smartphone Technology in Behavioral Healthcare | 58 |
10 | Riley WT et al (2011) |
|
Health Behavior Models in the Age of Mobile Interventions | 58 |
The co-citation network of references in mobile health app research, 2000-2019.
Keywords cover the main topics of a publication and are well suited to be used for analyzing related research hot spots. The research hot spots of mobile health app research were identified through co-occurrence analysis of the top 100 keywords. We used VOSviewer to extract and cluster the top 100 keywords.
As shown in
The co-occurrence network of the top 100 keywords in mobile health app research, 2000-2019.
In the current study, we explored the bibliometric characteristics of mobile health app research and we identified research trends, research hot spots, and the knowledge base associated with mobile health apps through our co-word analysis of the top 100 keywords.
The change in the number of academic publications in a field is an important indicator of the evolving trends in this field. Mobile health app research included over 2800 publications around the world. The number of research papers published on mobile health apps annually has been increasing since 2000, with particularly notable gains in the past 5 years.
For journal sources, the top 3 journals publishing mobile health app research belong to the area of medical informatics, accounting for 18.24% (511/2802) of the total publications. Meanwhile, 848 journals had only 1 mobile health app–related publication, accounting for 30.26% (848/2802) of the total publications. Journals publishing mobile health app research were widely distributed across the general health domain, with higher concentration, as expected, in the field of medical informatics.
Due to the accessibility of bibliometric analysis, coauthorship is frequently used as a proxy for research collaboration. A coauthorship network can reflect the collaborative relationship among researchers and provide potential opportunities for other researchers to cooperate; examining this network can highlight potential opportunities for enhanced collaboration both within and outside of the existing network. The coauthorship network reflects author, institution, and country.
We found that the United States was the most significant contributor to mobile health app research. Of the publications identified, 65.06% (1823/2802) were published in the United States, England, Australia, and Canada, the current global leaders in mobile health app research.
We found that 7 of the top 10 most productive institutions are from the United States, and the other 3 are from Canada, Australia, and South Korea.
We identified 13,040 authors who have published research on mobile health apps. Of those authors, only 233 (1.79%) have published more than 4 papers in this emerging field, forming 5 relatively small coauthorship networks. We conclude from this finding that there are many researchers interested in pursuing mobile health app research, but collaboration between authors remains limited. Promoting collaboration between authors, institutions, and countries would expand the number of authors regularly publishing in this field and could contribute to more effective dissemination of innovative practices in mobile health app use.
More than three-quarters of the top 10 most frequently cited references in mobile health app research were published after 2011. This timeline is consistent with our understanding of mobile health app research as a rapidly emerging field of study. As shown in
Keywords are standardized terms used to ensure that publications are indexed uniformly by topic. Therefore, mapping the co-word network by analyzing the co-occurrence frequency of keywords from multiple publications is helpful to study the internal structure and the hot topics in the field of mobile health app research [
Cluster 1 (red cluster) mainly focuses on the technology and system development of mobile health apps and includes 29 high-frequency keywords, such as mobile app, technology, smartphone, system, model, usability, acceptance, design, devices, barriers, privacy, and attitudes. With the continuous development of smartphone and information technology, researchers need to update or develop new mobile health apps to meet the growing needs of patients and medical staff. The system development of mobile health apps mainly includes a user-computer interface, algorithms, privacy, design, and computer security, and it follows the principles of user-centered, convenient operation, safety, and stability [
Cluster 2 (green cluster) mainly focuses on mobile health apps used in mental health and includes 22 high-frequency keywords, such as quality of life, depression, validity, mental health, prevalence, therapy, anxiety, reliability, efficacy, disorders, questionnaire, stress, and cognitive behavioral therapy. It is reported that about 29% of humans suffer from mental illness in their lifetime, and more than 55% of these do not receive the treatment they need [
Cluster 3 (blue cluster) focuses on mobile health apps used as mobile health tools in telemedicine, chronic disease, and medication adherence management and includes 21 high-frequency keywords such as mobile health, care, telemedicine, self-management, eHealth, medication adherence, communication, diabetes, chronic disease, glycemic control, hypertension, and asthma. Telemedicine delivered using mobile health apps is an innovative model of health care, with significant potential to solve challenges in today's health care environment [
Cluster 4 (yellow cluster) mainly focuses on mobile health apps used in health behavior and health promotion and includes 18 high-frequency keywords, such as intervention, health, physical activity, behavior, risk, weight loss, obesity, nutrition, diet, health promotion, and overweight. With the development of portable wearable devices and smart sensors, mobile health apps can provide self-tracking capabilities. People can track measures of interest such as weight, calories consumed, heart rate, respiratory rate, and exercise status, and can also record how they feel or how they are responding to treatment (eg, side effects). Tracking capabilities of this type can be used by people to promote the adoption of healthful behaviors, such as physical exercise, reasonable diet, and obesity prevention [
Cluster 5 (purple cluster) mainly focuses on mobile health apps used in disease prevention via the internet and includes 10 high-frequency keywords, such as internet, prevention, trial, smoking cessation, social media, cancer, and human immunodeficiency virus (HIV). The International Telecommunication Union estimates that 4.1 billion people were using the internet at the end of 2019 [
Our study is, to our knowledge, the first bibliometric analysis of mobile health app–related publications. Still, there are some limitations to this study. First, there may be language bias because, although we did not place any limits on the language of publications in our study, most WOS publications are in English. Second, the quality of publications in WOS is not uniform. Conducting a weighted analysis of publications based on the assessment of quality was outside the scope of our study; therefore, it is possible that our analysis has given equal attention to publications of differing quality. Finally, the current data for analysis were only extracted from WOS, excluding data extracted from other search engines such as Scopus (Elsevier), PubMed, or Google Scholar (Google LLC). Thus, it is possible that publications appearing only through one of these other search engines have been missed. We plan to address this by exploring ways of combining different data sources in future work.
Through the bibliometric quantitative analysis and visualization network map of the data extracted from the WOS database, the current study reveals the research status, research trends, hot spots, and coauthorship network of mobile health app research. Mobile health app research is a new and promising field globally, with great potential for improving patient care and promoting health. By comprehensively summarizing the trends in mobile health app research, we expect this work may serve as a guide for facilitating future research directions to advance this field of research further.
Top 100 keywords and 5 clusters in mobile health app research, 2000-2019.
human immunodeficiency virus
Web of Science
This research was supported by the Humanities Social Science Foundation of Ministry of Education (Grant No. 17YJCZH184), the Philosophy and Social Sciences Promotion Project of China Medical University (Grant No. 111–3110118083), and the Health Big Data Research Project of China Medical University (Grant No. 111–HMB201901101).
None declared.