Research trends and hotspots of health-related quality of life: a bibliometric analysis from 2000 to 2019
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Si Zheng, Anqi He, Yan Yu, Lingling Jiang, Jing Liang and Peigang Wang*
Research trends and hotspots of health-related quality of life: a bibliometric analysis from 2000 to 2019.
Health and Quality of Life Outcomes. 2021 Apr 23;19(1):130.
https://doi.org/10.1186/s12955-021-01767-z
Abstract
Background: The number of research articles on health-related quality of life (HRQoL) has been strikingly increasing. This study aimed to explore the general trends and hotspots of HRQoL.
Methods: Based on the Web of Science database, research on HRQoL published between 2000 and 2019 were identifed. A bibliometric analysis was performed based on the number of articles, citations, published journals, authors’ addresses, and keywords. Descriptive analysis, visualization of geographic distribution and keyword clustering analysis were applied to the collected data.
Results: The annual number of articles showed growth over the past twenty years, but the annual total citations and annual citations per article were both in decreasing trends. Articles about HRQoL were more likely to be published in journals of multi-subject categories. The HRQoL research was mainly distributed across North America and Europe throughout the twenty years and ushered in a vigorous development worldwide after 2015. Cooperation strength between domestic institutions was much greater than that of international institutions. HRQoL research had six concentrated clusters: HRQoL, Depression, Obesity, Disability, Oncology, Fatigue.
Conclusion: This study provided an overall perspective of global research trends and hotspots in HRQoL, and a potential insight for future research. HRQoL research had experienced signifcant increasing development during 2000-2019, especially the HRQoL measurement instruments, however, there were signifcant regional disparities in scientifc output in HRQoL.
Keywords: Health-related quality of life, Bibliometric, Scientifc output, Keyword clustering