Evidence map›Paper›PMID 42719396›Full record

ArticleDigital health

Global research landscape, hotspots, and emerging trends of digital biomarkers: A bibliometric analysis.

Yufei Tian, Xin Tian, Zheng Li, Zhongkai Wang, Haoxin Guo, Fanyu Meng, Zhongqing Wang

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yufei TianDepartment of Information Center, the First Hospital of China Medical University, Shenyang, China.
Xin TianDepartment of Information Center, the First Hospital of China Medical University, Shenyang, China.
Zheng LiDepartment of Information Center, the First Hospital of China Medical University, Shenyang, China.
Zhongkai WangDepartment of Information Center, the First Hospital of China Medical University, Shenyang, China.
Haoxin GuoDepartment of Information Center, the First Hospital of China Medical University, Shenyang, China.
Fanyu MengDepartment of Information Center, the First Hospital of China Medical University, Shenyang, China.
Zhongqing WangDepartment of Information Center, the First Hospital of China Medical University, Shenyang, China.ORCID https://orcid.org/0000-0002-5330-7538

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: In recent years, research interest in digital biomarkers has grown rapidly, driven by their capacity to enable continuous, objective, and personalized health monitoring. However, comprehensive bibliometric analyses of global research output in this field remain limited. This study aims to systematically evaluate the current status, hotspots, and emerging trends of global digital biomarker research using bibliometric analysis. Methods: On August 11, 2026, we retrieved publications related to digital biomarkers from the Web of Science Core Collection (WoSCC). This study encompassed articles and reviews published from January 1, 2014 to August 11, 2026. Publication years, journals, authors, institutions, countries/regions, cited references, and keywords were systematically analyzed. VOSviewer was employed to conduct co-authorship, co-occurrence, and co-citation analyses and to construct network visualization maps. Results: We evaluated a total of 1056 publications from 96 countries/regions, of which the United States was the main contributor. Harvard University, King's College London and the Massachusetts General Hospital were the primary research institutions. Among the 7,188 contributing authors, Najafi, Bijan was the most prolific, while Horak, Fay B. was the most frequently cited. Keyword cluster analysis reveals four main research topics: (1) Parkinson's disease and mobility monitoring in older adults, (2) AI-assisted diagnosis, classification, and prediction, (3) remote mental health assessment and passive physiological monitoring, and (4) cognitive dysfunction and neurodegenerative disease assessment. Conclusion: Digital biomarker research shows the characteristics of continuous expansion and increasingly diversified themes. This bibliometric analysis clarifies major research directions and knowledge structures, supporting digital biomarker development and clinical translation.

Indexed as

bibliometric analysisbiomedical engineeringdigital biomarkershealth monitoringwearable sensors

Identifiers

PMID42719396
PMCPMC13554553

What OpenQuestion holds

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Registered trials

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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.