Using the EISTL Model to Structure Citation Trends of Research in JMIR mHealth and uHealth: Bibliometric Analysis (Preprint)
Authors: Jen Tung-Hui , Chien Tsair-wei , Lin Ju-Kuo , Lin Jui-Chung John , Chou Willy 
Published: December 2021

BACKGROUND

The citation trend of research has frequently been applied to topical entities of interest in bibliographical studies. Although the burst spot and the corresponding burst strength can be highlighted in the traditional temporal bar graph (TBG) as viewing their evolutions of entities, no such growth trends were added to the TBG for a better understanding of future tremd (e.g., reading to rise, increasing, decreasing, or slowdown). A step-by-step scheme for constructing the bibliographical study is thus required to make data meaningful and fruitful.

OBJECTIVE

This article assesses (1) whether an EISTL model (i.e., identifying the topical entity, indicator, selection of top entities, TBG, and using the line-chart plot for verification) can be applied to display the trend analysis of article citations for entities and (2) whether the TBG can be enhanced to release more valuable information for readers.

METHODS

We obtained 2,151 abstracts indexed in PubMed by searching the keywords “JMIR mHealth and uHealth” (Journal) on November 11, 2021. The metadata was collected, including author names, research institutes, article identifiers (PMIDs), countries, and medical subject headings (MeSH terms). The burst spot and the growth trend were displayed along with the inflection point (IP) using the Newton–Raphson Iteration Method (NRIM) and the growth/share matrix (GSM). Cooccurrence analysis was performed to select the top-cited entities using social network analysis (SNA) and Sankey diagrams. The TBG plays a transitive role before drawing the line-chart plot in the EISTL model. Both choropleth map and Kano diagram were used to compare and classify research achievements (RA) for countries using the x-index. The differences in RAs were compared between two groups (i.e., participants of Summit for Democracy (SFD) 2021 and Non-SFD) using the forest plot. All animation-typed dashboards were laid on Google Maps for readers to manipulate entities of interest on their own.

RESULTS

The burst spot and citation trend for the top entities were selected and displayed on the TBG. The most cited entities were sequentially shown in the Sankey diagram, including Stoyan R Stoyanov (Australia), Queensland University of Technology (Australia), PMID=25760773, the US, and standards (MeSH). The top three most cited counties/regions were highlighted in a choropleth map and Kano diagram using the x-index to stratify in descending order: Australia, the UK, and Canada with x-indexes of 23.26, 22.21, and 21.42, respectively, when the US and China were divided into individual states and provinces for comparison. Differences in the six selective bibliometric metrics were not found (p>0.05) in countries between SFDs and non-SFDs.

CONCLUSIONS

We verified that (1) the EISTL model is viable and useful for presenting citation trends in bibliometric research, and (2) the improved TBG mode releases more information about citation trends. The EISTL model makes the bibliometrics clearer and easier to understand. As a corollary, the TBG with citation trends and burst spots is recommended for future bibliometrics and is not merely limited to the citation trends of the JMIR mHealth and uHealth, as we did in this study.