ArticleDigital health
Global research trends and hotspots in digital health in hypertension: A comprehensive bibliometric analysis (1992-2025).
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Digital health interventions are transforming hypertension management, yet the evolution and focus of this research domain remain underexplored. This study provides a comprehensive bibliometric analysis of global research trends and hotspots in digital health for hypertension management. Methods: Relevant publications were retrieved from the Web of Science Core Collection. Bibliometric and visualization analyses were conducted using VOSviewer, CiteSpace, and R-Bibliometrix, covering the period from 1992 to 2025. Results: A total of 1368 English-language articles, authored by 8918 researchers from 5268 institutions in over 100 countries/regions, were identified. These articles appeared in 435 journals, with publication output showing rapid growth since 2011 and peaking in 2022. The United States led in both productivity and international collaboration, with Duke University, the University of California System, and Harvard University as the top institutions. Bosworth HB emerged as the most prolific and influential author, while Conclusion: This study identified rapid growth and diversification in digital health research for hypertension, with the United States, leading academic institutions, and journals such as
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.