A Bibliometric Analysis of Publications on Obesity and Hypertension.

Background: No bibliometric study of published research subjects was conducted on obesity and hypertension to identify trends and novelties. As a result, this study aims to look into the trend of number of publications, trend of citations, journal with the most publications, area with the most publication approval, network visualization, overlay visualization, and density visualization on the topic of obesity and hypertension using bibliometric analysis. Methods: This study uses a bibliometric analysis. The data in this study are based on Internet searches conducted using Dimensions app. The VOSviewer software, version 1.6.18 was used to create and display the latest trends in network visualization, overlay visualization, and density visualization. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) flowcharts were used to show the steps after data collection. Step 1 (identification), step 2 (screening), step 3 (study eligibility) and the final sample (included) in phase 4 were all documented. Results: The search for publications on obesity and hypertension yielded 995.13 articles. After screening using the specified criteria, 9 541 articles were found. Most publications on obesity and hypertension were published in 2021, the fewest in 2003. Research on obesity and hypertension is conducted by health sciences. In addition, trends in obesity with hypertension currently focus on the impact of obesity and hypertension on specific populations (e.g. children, and elderly), novel therapeutic approaches, or the role of technology in monitoring and managing these conditions. From the density visualization, the topics that visualized the low category are mortality rate, severity, risk of hypertension, and metabolic abnormalities. Conclusion: From the results of the bibliometric analysis using these keywords, researchers can identify information about trends and innovations in obesity research topics in the future. This study recommends other researchers choose topics from the low visualization category to conduct new studies in the future. [ABSTRACT FROM AUTHOR]