Characterizing Hotspots and Frontier Landscapes of Diabetes-Specific Distress from 2000 to 2018: A Bibliometric Study

Abstract

Objectives: This work aims to comprehensively characterize hotspots and frontier landscapes concerning diabetes-specific distress from 2000 to 2018. Materials and Methods. Firstly, diabetes-specific distress-related literature was retrieved and downloaded from the Web of Science Core Collection (WoSCC). Secondly, WoSCC self-contained toolkits and GraphPad Prism7 were conducted to analyze general characteristics, including literature products, countries, institutes, authors, and journal resource. Finally, CiteSpace V Toolkits was put forward to implement advanced analysis, consisting of keyword-term frequency and co-occurrence, references-cited frequency and co-occurrence, and burst detection for keyword terms and references cited, which uncovers the hotspots and frontiers of diabetes-specific distress.

Results: After preprocessing, our study included a total of 1051 papers concerning diabetes-specific distress. Publication outputs increased smoothly year by year. Compared with other journals, diabetic medicine delivered the largest number of documents. The United States occupied the leading positions, and the most productive institution was the University of California System in terms of literature products. Fisher L. has the highest references-cited frequency. Prevalence of diabetes-specific distress, diabetes-specific distress and glycemic control, diabetes-specific distress and depression comorbidity, and diabetes-specific distress and risk factors were the research hotspots, whereas the measure of diabetes-specific distress and latent and serious/severe diabetes-specific distress was the research frontiers.

Conclusions: Overall, our study may inspire researchers to show great interest in diabetes-specific distress in the next few years.

Figure 1

(a) Flow diagram of diabetes-specific distress-related literature inclusion and exclusion criteria. (b) The annual quantities of diabetes-specific distress-related literature from 2000 to 2018. (c) The polynomial fitting curve of growth trends on diabetes-specific distress-related literature.

Figure 2

(a) The treemap of top 20 countries that published literature about diabetes-specific distress. (b) The treemap of top 20 institutes that published literature about diabetes-specific distress.

Figure 3

(a) The treemap of the top 20 authors that published literature about diabetes-specific distress. (b) The treemap of top 20 journals that published literature about diabetes-specific distress.

Figure 4

(a) Keyword-term co-occurrence cluster map related to diabetes-specific distress from 2000 to 2018. (b) Keywords/terms co-occurrence timeline map related to diabetes-specific distress from 2000 to 2018. Notes: rings represent the keyword-term history of keyword term; the color of ring denotes the time of corresponding frequency; the thickness of a ring is proportional to the number of keyword term in a given time slice.

Figure 5

(a) References-cited cluster map related to diabetes-specific distress from 2000 to 2018. (b) References-cited timeline map related to diabetes-specific distress from 2000 to 2018. Notes: citation rings represent the citation history of an article; the color of a citation ring denotes the time of corresponding citations; the thickness of a ring is proportional to the number of citations in a given time slice.

Figure 6

Top 20 keyword terms with the strongest citation bursts related to diabetes-specific distress from 2000 to 2018. Notes: the red bars mean keywords cited frequently; the green bars were keywords cited infrequently.

Figure 7

Top 25 references cited with the strongest citation bursts related to diabetes-specific distress from 2000 to 2018. Notes: the red bars mean keywords cited frequently; the green bars were keywords cited infrequently.