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Bibliometric analysis of worldwide scientific literature in mobile - health: 2006–2016
https://doi.org/10.1186/s12911-017-0476-7
© The Author(s). 2017
Received: 17 January 2017
Accepted: 23 May 2017
Published: 30 May 2017
Abstract
Background
The advancement of mobile technology had positively influenced healthcare services. An emerging subfield of mobile technology is mobile health (m-Health) in which mobile applications are used for health purposes. The aim of this study was to analyze and assess literature published in the field of m-Health.
Methods
SciVerse Scopus was used to retrieve literature in m-Health. The study period was set from 2006 to 2016. ArcGIS 10.1 was used to present geographical distribution of publications while VOSviewer was used for data visualization. Growth of publications, citation analysis, and research productivity were presented using standard bibliometric indicators.
Results
During the study period, a total of 5465 documents were published, giving an average of 496.8 documents per year. The h-index of retrieved documents was 81. Core keywords used in literature pertaining to m-Health included diabetes mellitus, adherence, and obesity among others. Relative growth rate and doubling time of retrieved literature were stable from 2009 to 2015 indicating exponential growth of literature in this field. A total of 4638 (84.9%) documents were multi-authored with a mean collaboration index of 4.1 authors per article. The United States of America ranked first in productivity with 1926 (35.2%) published documents. India ranked sixth with 183 (3.3%) documents while China ranked seventh with 155(2.8%) documents. VA Medical Center was the most prolific organization/institution while Journal of Medical Internet Research was the preferred journal for publications in the field of m-Health. Top cited articles in the field of m-Health included the use of mobile technology in improving adherence in HIV patients, weight loss, and improving glycemic control in diabetic patients.
Conclusion
The size of literature in m-Health showed a noticeable increase in the past decade. Given the large volume of citations received in this field, it is expected that applications of m-Health will be seen into various health aspects and health services. Research in m-Health needs to be encouraged, particularly in the fight against AIDS, poor medication adherence, glycemic control in Africa and other low income world regions where technology can improve health services and decrease disease burden.
Keywords
Background
Mobile health (mHealth or m-Health) was defined more than a decade ago as the use of mobile communication devices in health services or as an emerging application of mobile technologies for healthcare systems [1–3]. The world health organization (WHO) defined m-Health as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices” [4]. Mobile health is a relatively recent field that emerged as a branch or component of health which involves the use of wireless technology or electronic processes in health services [5]. Many published reports indicated that m-health had a positive impact on individual and national health services [6]. Mobile health had been applied in a wide range of health services including promoting medication adherence, prevention of behaviors associated with certain diseases, psychological support for patients with chronic diseases, weight loss, smoking cessation and many others [7–18].
Bibliometrics and scientometrics are defined as mathematical/statistical methods used to assess the quality and quantity of published scientific literature and to study research trends, citation analysis, authorship, impact of publications, journal analysis, as well as national and international contribution in a particular field. Bibliometrics and scientometrics had been applied to many scientific fields including mobile technology and telecommunication [19–23]. However, up until this date, no bibliometric analysis of scientific literature in m-Health had been carried out and published. Therefore, the aim of this study was to analyze published literature in m-Health. In specific, growth of publications, country contribution, international collaboration, citation analysis, keyword occurrences, authorship analysis, most productive institutions, and most productive journals in the field of m-Health were investigated.
Methods
Bibliographic database
In this study, data pertaining to m-Health were retrieved from Scopus, a bibliographic database that covers nearly 22,000 titles in the scientific, technical, medical, and social sciences. The choice of Scopus was made based on the idea that it is the largest database when compared to Pubmed or Web of Science [24]. Furthermore, several analytical functions can be performed using Scopus (Additional file 1). One such function was “source type” which allowed us to refine retrieved literature based on type of data source whether the source was journal publications or books or book chapters or conference proceedings or trade publications. In this study, we limited our analysis to documents published in journals but not in books or conference proceedings (Additional file 1). Conference proceedings included abstracts which might have been published twice as a conference abstract and as a full journal article and therefore might create false positive results. Once the source type was limited to journal publications, the type of these publications can be explored using the function “document type” (Additional file 1). In document type, errata documents, corrections of an already published articles, were excluded from the analysis since they do not represent true publications. The conference papers that appeared under document type were different from those that appeared under source type. In document type, conference papers refer to papers presented in conferences and were published as full journal articles and therefore could not be published twice. This approach of limiting source data to journal publications will minimize false positive, particularly for those which originated from conference proceedings.
Search strategy and validity
Our search strategy was based on two approaches that were combined at a later step. The first approach included search for journal publications in which the term (“mobile health”) appeared in the title or abstract or author keywords. However, since not all publications in mobile health could be retrieved this way, we had to use a second approach in which we searched for all possible documents that included in their titles words related to mobile technology such as smartphone or texting or cellular phone or mobile application plus a word in any field of health. In each approach, the time was set from 2006 to 2016 and source type was limited to journal publications. The number of documents retrieved using each approach was shown in Additional file 1. The first approach yielded a total of 2452 (Additional file 1) while the second approach yielded a total of 3263 (Additional file 1) and when combined, they yielded a total of 5465 journal publications. The overlap between the two approaches was 250 documents. One might ask why search for the term “mobile health” did not yield all potential documents and the answer for that was the tendency of some researchers to use terms such as smartphones or text message in addition to the disease or ailment being investigated instead of using the recently defined term “m-Health”. Furthermore, some researchers might use broader terms such as telemedicine. Therefore, it was not surprising that the term “mobile health” did not retrieve all needed documents and the use of a second detailed approach was needed. The overall search strategy was shown in Additional file 1. In the second approach, the health related terms were obtained from several systematic reviews and review articles in m-Health to ensure conclusiveness of the search strategy [25–33]. For validity of the search strategy, two co-authors manually reviewed 110 articles chosen as top 10 cited articles for each year in the study period. For all manually reviewed articles, no false positive results were obtained. Furthermore, the preferred journal names in which publications appeared indicated that the retrieved articles were within the correct scope.
Data analysis
Retrieved data were analyzed for document type, annual growth, citation analysis, authorship, country productivity, and for articles with the highest number of citations. Document types were presented as frequency and percentage. For annual growth, we presented data as number of retrieved documents in each year. Furthermore, the annual growth rate (AGR), defined as the percentage change in the number of publications over a period of 1 year was calculated based on the following equation: AGR = [(Ending Value - Beginning Value)/Beginning Value] * 100.
The growth analysis was also presented as relative growth rate (RGR) which was defined as the increase in number of publications per unit of time. The RGR was calculated based on the following equation: RGR = [log e W 2 - log e W 1 ]/(T2 - T1) [19, 34, 35] where loge W1: log of initial number of articles; loge W2: log of final number of articles after a specific period of interval; and T2-T1: the unit difference between the initial time and the final time. The RGR can be presented in a different format called doubling time (DT), defined as the period of time required for the number of publications to double in number in 1 year and was calculated based on the following equation: DT = 0.693/RGR [19, 34, 35]. Finally, the citation analysis was presented as total citations, mean and median (Q1 – Q3) citations. The quality of publications was measured using Hirsch index (h-index) and was obtained directly from Scopus.
Collaboration and authorship analysis
Author details were exported from Scopus to Microsoft Excel where authorship analysis was carried out. Analysis in Microsoft Excel included number of single-authored articles and number of multi-authored (joint) articles. Analysis of overall collaboration in the field of m-Health was calculated using the following equation: Degree of collaboration = C = N m /N m + N s [19, 34, 35] where Nm = number of multi-authored papers and Ns = number of single-authored papers. The collaboration index (CI) was calculated based on the number of multi-authored (joint) papers and the number of authors of these multi-authored (joint) papers. The equation used to calculate the collaboration index was as follows: CI = number of authors in joint articles/number of joint papers [19, 34, 35]. Authorship analysis included analysis of average number of authors per paper which was calculated as: (AAPP) = total number of authors/total number of papers.
Visualization and mapping
Retrieved data were visualized using VOSviewer program, a software tool used for visualization of bibliometric networks. In visualization techniques, maps could be presented as a density map or as a network visualization in which color, circle size, font size, and thickness of connecting lines were used to present certain parameters. For example, units with similar color indicated that these units belonged to one cluster or group of close units that could be countries or authors. The strength of collaboration between countries was measured by the thickness of connecting lines which was numerically represented as relative link strength. Higher relative link strength suggested stronger collaboration. The larger circle size or font size indicated greater productivity or citations [36]. For geographical distribution of publications ArcGIS 10.1 (ArcMap 10.1), a geographic information system (GIS), was used. To create the geographical distribution map, retrieved data were exported to excel and then were transferred to ArcGIS 10.1 program to create the GIS map.
Statistical analysis and ethics
Descriptive statistics for categorical variables such as frequencies and percentages were presented. For continuous data, mean, median, and range were represented. For median, the interquartile range (Q1-Q3) was presented. No statistical testing was carried out. For data analysis and presentation, Microsoft Excel and Statistical Package for Social Sciences (SPSS 20) were used. Graphics was made using SPSS 20. This study included no human subjects and is based on electronic data and therefore it was exempted from ethical approval.
Results
Description of retrieved literature
Types of retrieved documents (2006–2016)
Type of document | Frequency | % N = 5465 |
|---|---|---|
Article | 4194 | 76.7 |
Review | 523 | 9.6 |
Note | 199 | 3.6 |
Letter | 198 | 3.6 |
Article in Press | 116 | 2.1 |
Editorial | 91 | 1.7 |
Conference Paper | 78 | 1.4 |
Short Survey | 66 | 1.2 |
Total | 5465 | 100% |
Network visualization map of author keywords occurrences (i.e., keywords listed by the author). Keywords with minimum occurrences of 25 times were shown in the map. Keywords with the same color were commonly listed together. So, for example, physical activity, obesity, health behavior, and weight loss have similar color suggestive that these keywords have close relation and usually co-occur together
Growth of publications
Annual number of publications and citation analysis per year (2006 – 2016)
Year | Frequency | % N = 5465 | TC | Mean ± SD of citations | Median (Q1 – Q3) of citations |
|---|---|---|---|---|---|
2006 | 155 | 2.8 | 2438 | 15.7 ± 33.8 | 5 (1–17) |
2007 | 176 | 3.2 | 3248 | 18.5 ± 31.5 | 11 (1–21.75) |
2008 | 189 | 3.5 | 3646 | 19.3 ± 32.6 | 6 (1–20.5) |
2009 | 234 | 4.3 | 4955 | 21.2 ± 42.7 | 6 (1–23) |
2010 | 288 | 5.3 | 5994 | 20.8 ± 45.6 | 7 (1–24.75) |
2011 | 343 | 6.3 | 6886 | 16.6 ± 39.7 | 4 (0–17) |
2012 | 505 | 9.2 | 8332 | 20.1 ± 26.7 | 7 (2–21) |
2013 | 630 | 11.5 | 8581 | 13.6 ± 23.4 | 6 (2–16) |
2014 | 861 | 15.8 | 6179 | 7.2 ± 11 | 3 (1–9) |
2015 | 989 | 18.1 | 3749 | 3.8 ± 6.7 | 2 (0–4.5) |
2016 | 1095 | 20.0 | 929 | 0.8 ± 1.8 | 0 (0–1) |
Annual number of publications, AGR, RGRT, and DT (2006–2016)
Year | Frequency | AGR | Cumulative total publications | Loge W | RGR | DT |
|---|---|---|---|---|---|---|
2006 | 155 | - | 155 | 5.0 | - | - |
2007 | 176 | 13.5 | 331 | 5.8 | 0.8 | .9 |
2008 | 189 | 7.4 | 520 | 6.3 | 0.5 | 1.4 |
2009 | 234 | 23.8 | 754 | 6.6 | 0.3 | 2.3 |
2010 | 288 | 23.1 | 1042 | 6.9 | 0.3 | 2.3 |
2011 | 343 | 19.1 | 1385 | 7.2 | 0.3 | 2.3 |
2012 | 505 | 47.2 | 1890 | 7.5 | 0.3 | 2.3 |
2013 | 630 | 24.8 | 2520 | 7.8 | 0.3 | 2.3 |
2014 | 861 | 36.7 | 3381 | 8.1 | 0.3 | 2.3 |
2015 | 989 | 14.9 | 4370 | 8.4 | 0.3 | 2.3 |
2016 | 1095 | 10.7 | 5465 | 8.6 | 0.2 | 3.5 |
Authorship pattern, collaboration, and prolific authors
Average author per documents and author productivity per year (2006–2016)
Year | Frequency | % N = 5465 | Total number of authors | Average number of authors per document |
|---|---|---|---|---|
2006 | 155 | 2.8 | 515 | 3.3 |
2007 | 176 | 3.2 | 696 | 4.0 |
2008 | 189 | 3.5 | 702 | 3.7 |
2009 | 234 | 4.3 | 820 | 3.5 |
2010 | 288 | 5.3 | 1092 | 3.8 |
2011 | 343 | 6.3 | 1352 | 3.9 |
2012 | 505 | 9.2 | 2165 | 4.3 |
2013 | 630 | 11.5 | 2739 | 4.3 |
2014 | 861 | 15.8 | 3943 | 4.6 |
2015 | 989 | 18.1 | 3962 | 4.0 |
2016 | 1095 | 20.0 | 5568 | 5.1 |
Total | 5465 | 100 | 23554 | 4.0 |
Collaboration index (CI) among authors in m-Health field (2006–2016)
Year | Frequency | % N = 5465 | Total number of authors | Number of single authored publications | % | Number of multi- authored publications | % | number of authors in multi-authored publications | CI |
|---|---|---|---|---|---|---|---|---|---|
2006 | 155 | 2.8 | 515 | 48 | 31.0 | 107 | 69.0 | 408 | 3.8 |
2007 | 176 | 3.2 | 696 | 36 | 20.5 | 140 | 79.5 | 556 | 4.0 |
2008 | 189 | 3.5 | 702 | 43 | 22.8 | 146 | 77.2 | 556 | 3.8 |
2009 | 234 | 4.3 | 820 | 65 | 27.8 | 169 | 72.2 | 651 | 3.9 |
2010 | 288 | 5.3 | 1092 | 59 | 20.5 | 229 | 79.5 | 863 | 3.8 |
2011 | 343 | 6.3 | 1352 | 76 | 22.2 | 267 | 77.8 | 1085 | 4.1 |
2012 | 505 | 9.2 | 2165 | 87 | 17.2 | 418 | 82.8 | 1747 | 4.2 |
2013 | 630 | 11.5 | 2739 | 95 | 15.1 | 535 | 84.9 | 2204 | 4.1 |
2014 | 861 | 15.8 | 3943 | 106 | 12.3 | 755 | 87.7 | 3188 | 4.2 |
2015 | 989 | 18.1 | 3962 | 117 | 11.8 | 872 | 88.2 | 3090 | 3.5 |
2016 | 1095 | 20.0 | 5568 | 95 | 8.7 | 1000 | 91.3 | 4568 | 4.6 |
Total | 5465 | 100 | 23554 | 827 | 15.1 | 4638 | 84.9 | 18916 | 4.1 |
Network visualization map of active authors in m-Health. Authors with a minimum of 10 publications and 100 citations were visualized. The map included 30 authors who met the criteria of being an active author. Some names might not be seen due to overlap of names. Closed circles indicated active authors of close research collaboration
Geographical distribution of publications
Geographical distribution of publication in m-Health. The map was created using ArcGIS 10.1 software. The map is color coded where world regions with red color had the highest productivity while world regions with dark green had the lowest productivity. Areas with no color indicates no data available from these areas
Countries with a minimum productivity of 50 documents in the field of mobile health
Rank | Country | Frequency | % N = 5465 |
|---|---|---|---|
1 | United States | 1926 | 35.2 |
2 | United Kingdom | 485 | 8.9 |
3 | Australia | 280 | 5.1 |
4 | Canada | 268 | 4.9 |
5 | South Korea | 205 | 3.8 |
6 | India | 183 | 3.3 |
7 | Germany | 173 | 3.2 |
8 | China | 155 | 2.8 |
9 | Spain | 153 | 2.8 |
10 | France | 149 | 2.7 |
11 | Netherlands | 116 | 2.1 |
12 | Sweden | 113 | 2.1 |
13 | Switzerland | 106 | 1.9 |
14 | Italy | 94 | 1.7 |
15 | South Africa | 80 | 1.5 |
16 | Japan | 72 | 1.3 |
16 | Taiwan | 72 | 1.3 |
18 | New Zealand | 65 | 1.2 |
19 | Turkey | 60 | 1.1 |
20 | Denmark | 55 | 1.0 |
21 | Iran | 54 | 1.0 |
22 | Norway | 50 | 0.9 |
Network visualization map of international collaboration among countries with a minimum productivity of 50 documents. The thickness of connecting line between any two countries indicates strength of collaboration. For example, the link strength (collaboration) between USA and UK was 71 and it represents a thick line. On the other hand, the line between USA and India had a link strength of 28. Countries with similar color form one cluster. For example, countries with red color such as Germany and Netherlands existed in one cluster and had the highest percentage of collaboration within this cluster. India, Japan, Taiwan, and South Korea were clustered in green since the bulk of their collaboration is with the USA, so they are grouped with USA
Institutions and organizations with a minimum productivity of 30 documents in m-Health (2006–2016)
Rank | Institution/Organization | Frequency | % (N = 5465) | Country |
|---|---|---|---|---|
1 | VA Medical Center | 85 | 1.6 | USA |
2 | University of Washington, Seattle | 64 | 1.2 | USA |
3 | The University of Sydney | 60 | 1.1 | Australia |
4 | University of California, San Francisco | 59 | 1.1 | USA |
5 | Karolinska Institutet | 57 | 1.0 | Sweden |
6 | The University of British Columbia | 54 | 1.0 | Canada |
7 | Massachusetts General Hospital | 52 | 1.0 | USA |
8 | London School of Hygiene & Tropical Medicine | 49 | 0.9 | UK |
9 | Harvard Medical School | 48 | 0.9 | USA |
9 | University of Pittsburgh | 48 | 0.9 | USA |
9 | University of Toronto | 48 | 0.9 | Canada |
9 | Columbia University in the City of New York | 48 | 0.9 | USA |
13 | Imperial College London | 44 | 0.8 | UK |
14 | Johns Hopkins Bloomberg School of Public Health | 43 | 0.8 | USA |
15 | University of California, Los Angeles | 41 | 0.8 | USA |
16 | University Michigan Ann Arbor | 38 | 0.7 | USA |
16 | University of Oxford | 38 | 0.7 | UK |
18 | Duke University | 36 | 0.7 | USA |
19 | University of Auckland | 34 | 0.6 | New Zealand |
19 | University of New South Wales UNSW Australia | 34 | 0.6 | Australia |
21 | Johns Hopkins University | 33 | 0.6 | USA |
21 | The University of North Carolina at Chapel Hill | 33 | 0.6 | USA |
23 | David Geffen School of Medicine at UCLA | 31 | 0.6 | USA |
23 | Northwestern University Feinberg School of Medicine | 31 | 0.6 | USA |
23 | University Health Network University of Toronto | 31 | 0.6 | Canada |
23 | UCL (University College London) | 31 | 0.6 | UK |
23 | University of Queensland | 31 | 0.6 | Australia |
28 | Brigham and Women’s Hospital | 30 | 0.5 | USA |
Preferred journals
Journal names with minimum productivity of 20 publications in m-Health (2006–2016)
Rank | Journal | Frequency | % N = 5465 |
|---|---|---|---|
1 | Journal of Medical Internet Research | 193 | 3.5 |
2 | Telemedicine and E Health | 129 | 2.4 |
3 | Plos One | 77 | 1.4 |
4 | Journal of Telemedicine and Telecare | 67 | 1.2 |
5 | Telemedicine Journal and E Health | 52 | 1.0 |
6 | Journal of Medical Systems | 50 | 0.9 |
7 | BMC Public Health | 48 | 0.9 |
8 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society | 46 | 0.8 |
9 | BMC Medical Informatics And Decision Making | 40 | 0.7 |
9 | International Journal of Medical Informatics | 40 | 0.7 |
11 | Journal of Diabetes Science and Technology | 38 | 0.7 |
12 | Health Informatics Journal | 28 | 0.5 |
13 | AIDS and Behavior | 27 | 0.5 |
13 | Trials | 27 | 0.5 |
15 | Journal of Health Communication | 26 | 0.5 |
16 | BMJ Open | 25 | 0.5 |
16 | Diabetes Technology and Therapeutics | 25 | 0.5 |
18 | AMIA Annual Symposium Proceedings | 24 | 0.4 |
19 | Journal of the American Medical Informatics Association | 23 | 0.4 |
20 | Journal of the Royal Army Medical Corps | 21 | 0.4 |
20 | Sensors Switzerland | 21 | 0.4 |
22 | Lancet | 20 | 0.4 |
22 | Soins Gerontologie | 20 | 0.4 |
Network visualization map of journal co-citation analysis for journals which published documents in m-Health with a minimum total of 300 citations. Journal of Medical Internet Research had many connecting lines with various journals indicating that this journal is being co-cited with various journals. Journals in the same cluster with the same color are being commonly co-cited together
Top cited documents
Top 10 cited articles in m-Health literature (2006–2016)
Rank | Title | Year | Journal name | Cited by | Reference | Type of document |
|---|---|---|---|---|---|---|
1 | “Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): A randomised trial” | 2010 | The Lancet | 449 | [49] | Article |
2 | “Text messaging as a tool for behavior change in disease prevention and management” | 2010 | Epidemiologic Reviews | 419 | [12] | Review |
3 | “Healthcare via cell phones: A systematic review” | 2009 | Telemedicine and e-Health | 386 | [18] | Article |
4 | “Mobile phone technologies improve adherence to antiretroviral treatment in a resource-limited setting: A randomized controlled trial of text message reminders” | 2011 | AIDS | 352 | [59] | Article |
5 | “CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions.” | 2011 | Journal of medical Internet research | 308 | [60] | Editorial |
6 | “How smartphones are changing the face of mobile and participatory healthcare: An overview, with example from eCAALYX” | 2011 | BioMedical Engineering Online | 299 | [61] | Review |
7 | “A randomized controlled trial of Sweet Talk, a text-messaging system to support young people with diabetes” | 2006 | Diabetic Medicine | 298 | [62] | Article |
8 | “Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behaviour treatments” | 2010 | British Journal of Health Psychology | 293 | [7] | Article |
9 | “A text message-based intervention for weight loss: Randomized controlled trial” | 2009 | Journal of Medical Internet Research | 278 | [63] | Article |
10 | “Mobile phone based clinical microscopy for global health applications” | 2009 | PLoS ONE | 276 | [64] | Article |
Discussion
In this study, bibliometric indicators of literature in m-Health were sought. English language remained the language of science in m-Health field with more than 93% of retrieved documents were written in English. Literature in m-Health were dominated by multi-authored publications. Data presented in our study indicated that pattern of authorship showed an increasing pattern of number of authors per document with time. Compared with other disciplines, the prevalence of multi-authored publications and collaboration index presented in our study were higher than that in the field of zoology [37], marine science [38], and information technology [35]. This might be due to the nature of the m-Health field which included two disciplines, medicine and technology, and therefore might require authors from different disciplines. Furthermore, the availability of diverse means of communication among researchers in different world regions made team research and collaboration easier and more practical which reflected positively on the prevalence of multi-authored publications seen in our study.
The USA ranked first in productivity, but contribution by European and Asian countries was also prominent. Literature in m-Health covered a wide range of health aspects including diabetes mellitus, medication adherence to anti-HIV medications, and several other aspects related to cardiovascular and chronic diseases. The Journal of Medical Internet Research was the most preferred journal for publishing documents in m-Health. The VA Medical Center in USA was the most productive institution. The volume of literature in m-health showed an exponential increase in the second half of the study, i.e., after 2009. Along with the increase in volume literature, there was an increasing trend of mean number of authors per document with time indicative of an increase in research collaboration among authors.
Mobile health applications had witnessed rapid spread in developed countries were technology and prosperous economy helped in developing healthcare services through most-up-to date technology. The advancement in technology and introduction of health application through smartphones and other mobile technologies will increase the access of people to health services and might decrease the burden on healthcare providers. For example, the new mobile health technologies allow individuals to monitor their blood pressure and heart rate and store these health data for future use. Patients with chronic diseases such as diabetes mellitus, hypertension, hemodialysis patients can use smartphones as medical records to store data regarding their diet, physical activity, life style changes, and even their day to day health changes and symptoms and present the data to healthcare providers when needed. This will help improve health system services especially in low and middle income countries where automated health system services are not well developed and numbers of healthcare providers might be limited. The power of technology in advancing health services should encourage governments, organizations, and individuals to invest in technology for better future health services. Actually, most of the emerging telecommunication technologies will be implemented directly or indirectly to health. Examples of new technologies include next-generation robotics and body-adapted wearable electronics.
The use of m-Heath in low and middle income countries might help overcome the burden of diseases and poor health services in these countries [39–41]. A systematic review study on the impact of m-Health intervention on outcome of chronic diseases reported a positive impact on chronic diseases in low and middle income countries [6]. No doubt that limited health services in many developing countries and the fast-growing mobile technology and its penetration into many low and middle income countries made the shift to m-Health in these countries to be faster than expected. An interesting example of the power of m-Health to advance health services and to reverse spread of diseases is a project called Masiluleke in South Africa which is a project that utilizes the power of communication through mobile devices to fight HIV/AIDS and tuberculosis which are major threatening public health issues for South Africa and Africa in general. The project brings powerful technology companies with non-governmental organization and the government to promote health through technology. It was not surprising that Journal of AIDS and Behavior was among top preferred journals for publishing documents in m-Health because technology have been invested to enhance awareness and prevention regarding AIDS. Documents published in AIDS journals focused mainly on improving adherence, risky sexual behavior, and health care for patients with AIDS [42, 43]. It was not surprising also that some of the top cited articles in the field of m-Health were in the field of medication adherence in HIV patients. Adherence to HIV medication is an important issue in medication adherence field in general and in AIDS patients in particular. A study reported that 95% or greater adherence is needed to optimize virologic outcome for patients with HIV infection [44]. Poor adherence to anti-HIV therapies is believed to be the reason for less than one third of HIV patients in developed countries with adequate viral suppression [45]. Furthermore, the importance of medication adherence in HIV patients is related to avoidance of emergence of resistance to current anti-HIV therapies [46]. The use of m-Health to optimize anti-HIV medication adherence have been notice in several African countries where healthcare services are poor and HIV is widespread [26, 42, 47–49]. Implementation of m-Health had also been applied to non-communicable diseases such as diabetes mellitus and had shown to help patients manage their glycemic control [50–52].
Our study had few limitations that are inherent to the database used and to search query developed by the authors. Such limitations were encountered in previously published bibliometric studies [53–58]. It should be emphasized that despite the fact that Scopus is one of the largest databases, there are still unindexed journals and therefore publications in these un-indexed journals might have been missed. Furthermore, no search query is 100% perfect and false positive and false negative results are always a possibility. The ranking of authors and institutions presented in our study was based on data presented by Scopus. However, in certain cases, some authors or institutions might have more than one name or different name spelling. This might create an inaccuracy in the productivity of these institutions or authors. Despite all these limitations, our study was the first to analyze bibliometric indicators of m-Health literature. Previously published bibliometric studies in the field of mobile technology [34] endorsed the findings of our study regarding growth and authorship pattern.
Conclusion
Mobile health is an emerging and promising field that is expected to change several aspects of health services in both communicable and non-communicable diseases. Investing in m-Health should be a priority for governments, organizations and even individual patients. The emerging new mobile technologies should be tailored to help people and countries to improve national health and face major public health crisis. The data presented here will also serve for comparative future purposes to document the impact of m-Health on future research.
Declarations
Acknowledgements
The authors would like to thank An-Najah National University and WHO’s Health Inter Network Access to Research Initiative (HINARI) for giving us the opportunities to access most recent information sources such as Scopus database.
Funding
No funding was received for writing this study.
Availability of data and materials
Data can be retrieved from Scopus using search query presented in methodology.
Authors’ contributions
W.S. led the study design, data collection, statistical analysis, and drafting of the manuscript, SA, S.Z., A.A., A.S. F.A. participated in the study design, and revised the article for important intellectual content. All authors read and approved the final manuscript and agreed on its submission.
Competing interests
The author declares that he has no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
As a bibliometric study, no patients were involved in this type of analysis. Therefore, there was no need for Institutional Review Board approval and it was exempted based upon the design of this study.
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Authors’ Affiliations
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