BACKGROUND
It has been common in bibliographical studies to apply the citation trend of research to journal articles. There is no such enhanced temporal bar graph (TBG) to provide a better understanding of articles that are most worth reading. The Sankey-type TBG for highlighting articles worth reading is needed.
OBJECTIVE
The purpose of this article is to (1) describe the characteristics of articles in JMIR mHealth and uHealth (JMIRMU), (2) illustrate which JMIRMU articles are most worthwhile to read, and (3) compare the differences in article types in terms of citation trends between China and the United States.
METHODS
On July 11, 2022, we obtained 2,202 abstracts indexed in the Web of Science core collection (WoSCC) by searching for the keywords "JMIR mHealth and uHealth" (Journal) and the time span between 2013 and 2021. Metadata, including author names, research institutes, article identifiers (PMIDs), countries, and article citations, were collected over time. A Newton‒Raphson Iteration Method (NRIM) and a growth/share matrix (GSM) were used to display the burst spot and growth trend. The first and corresponding authors of articles were evaluated and displayed for countries/regions on choropleth maps. Sankey-type TPBs were applied to display (1) the characteristics of articles in JMIRMU, (2) JMIRMU articles that are the most worthwhile for readers, and (3) differences in article types between Chinese and American articles based on citation trends in recent years.
RESULTS
The top two productive entities were years in 2020(n=566) and 2019(420), the US(1251) and China(341), the University Sydney(Australia)(48), and Seoul National University(South Korea)(42) in research institutes, Ura-Vito Albrecht (Germany)(15) and Melvyn Zhang(Singapore)(10) in authors. Mobile phone (380) and mHealth (755) were the most common author-defined keywords in 2016 and 2021. PMID=29650506 and 30470676 are the most worthy-reading articles, with burst strengths of 5.83 and 6.46, respectively, against the two most-cited articles of 25760773 and 26537656. There was no difference in proportional counts of growth increasing articles between China and the US.
CONCLUSIONS
Using the Sankey-type TBG and trend analysis, we illustrated how to select articles that are most worth reading for authors. It is recommended for future bibliometric analysis and is not limited to citation analyses of articles in JMIRMU, as we did in this study.