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. 2026 Sep 5;12:20552076261487029. doi: 10.1177/20552076261487029

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

Yufei Tian 1,2, Xin Tian 1,2, Zheng Li 1,2, Zhongkai Wang 1,2, Haoxin Guo 1,2, Fanyu Meng 1,2, Zhongqing Wang 1,✉
PMCID: PMC13554553  PMID: 42719396

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.

Keywords: bibliometric analysis, digital biomarkers, biomedical engineering, health monitoring, wearable sensors

1. Introduction

Over the past decade, the level of digitalization in health care has continued to improve, completely changing medical research, diagnosis and treatment. 1 In this context, digital biomarkers, which are defined by the US Food and Drug Administration (FDA) as characteristics collected from digital health technologies that indicate normal or pathogenic biological processes or responses to interventions, 2 have emerged as a novel and increasingly utilized technology. Digital biomarkers have the capacity to provide personalized assessments throughout the entire treatment process, thereby reducing diagnostic uncertainty and informing therapeutic decisions through continuous, objective monitoring and AI-driven analysis.3–7 These advantages have prompted their rapid application in many medical fields. There is growing evidence demonstrating their potential clinical utility. In neurology, they can objectively monitor neurodegenerative diseases, such as Parkinson’s disease and Alzheimer’s disease, helping to detect subtle changes at an early stage.8,9 In psychiatry, speech, facial expressions, and behavioral patterns captured via mobile and wearable devices sensitively reflect symptom fluctuations in depression, anxiety, and schizophrenia.10–13 Similarly, wearable sensors in movement and rehabilitation sciences generate quantitative gait and motion biomarkers for personalized evaluation of mobility and balance.14,15 Recent work has extended the applications of digital biomarkers to non-invasive vascular and metabolic markers for diabetes,16,17 AI-driven quantification of subjective symptoms such as fatigue, 18 and spinal medicine assessments integrating IoT-based sensing and biomechanical monitoring. 19

Despite the rapid development of digital biomarker research, there is still a lack of systematic quantitative analysis to sort out its development process, emerging trends and future direction, which is crucial to guide future research and clinical transformation. 20 Bibliometric analysis is a quantitative approach that extracts measurable information from published studies and their citation relationships. 21 Given the great potential of digital biomarkers, covering multiple dimensions of human health, including the fields of physiology, hearing, cognition, vision and overall health, and in consideration of their anticipated integration into future medical practice, it is necessary to conduct a comprehensive bibliometric analysis.22,23 Therefore, this study employs bibliometric methods to systematically examine the research progress of digital biomarkers and clarify their knowledge evolution, developmental trends, and frontier directions.

2. Method

2.1. Retrieval methods and process

Web of Science include Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), and Arts & Humanities Citation Index (A&HCI), etc. Given that through the advanced search function of the Web of Science database, publications related to digital biomarkers can be obtained and Web of Science Core Collection can provide more bibliometric information, such as citations and cited references.24,25 Therefore, we chose data from Web of Science Core Collection for this study. The retrieval process primarily utilized two sub-databases within WOSCC: the Science Citation Index Expanded (SCI-EXPANDED) and the Social Science Citation Index (SSCI). As shown in Figure 1, the figure outlines the process and steps of data retrieval. We employed the advanced search function using the search query TS = “digital biomarkers*” to conduct the literature screening. Considering that digital biomarker research is still an emerging field, no time limit was applied to the retrieval process to ensure that all relevant publications were fully covered. The inclusion criteria for this study were as follows: document types were limited to “Article” and “Review,” and language was restricted to English. Other document types (such as “Editorial” and “Proceedings Paper”) and non-English documents were excluded. The initial search yielded a total of 1296 records. After applying the aforementioned screening process, 1056 publications were ultimately included in the analysis, including 241 publications retrieved as of August 11, 2026. Data retrieval and export from WoSCC were completed on August 11, 2026, to minimize the impact of subsequent database updates. The study period covered publications indexed in WoSCC from January 1, 2014 to August 11, 2026. As the analysis exclusively focused on bibliometric characteristics and did not involve human subject data, no ethical concerns arise. 26

Figure 1.

Figure 1.

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The search process and steps for retrieving publications on digital biomarkers.

2.2. Analytical methods and tools

VOSviewer 1.6.20 was used to construct and visualize bibliometric networks, including co-authorship, co-citation, and keyword co-occurrence networks. The software was developed by Van Eck and Waltman at the Centre for Science and Technology Studies, Leiden University, and is widely used for bibliometric mapping. 27

2.3. Data analysis

This study evaluates publication output based on the number and proportion of publications by year, country/region, institution, and author. Citation impact was evaluated using total citation counts and average citations per publication. To reduce biases arising from synonymous terms and spelling variations, during data preprocessing, VOSviewer was initially used to identify potential inconsistencies and duplicate entries. Subsequently, manual verification was performed to merge duplicate records and standardize synonymous terms, spelling variations, author names, and institutional affiliations to ensure data consistency. For descriptive tables, countries/regions, journals, institutions, and authors were ranked by the number of publications as the primary criterion and total citation count as the secondary criterion. Highly cited publications were ranked by total citation count. Finally, this study applied analytical tools to perform co-authorship, co-citation and keyword co-occurrence analyses to explore collaboration networks and research frontiers, and assessed the strength of network relations by total link strength.

3. Result

3.1. Global annual publication volume on digital biomarkers

The trend of annual publications over time provides a clear reflection of the development dynamics within a research field. 28 Figure 2 illustrates the annual publication volume of digital biomarkers since 2014 (Note: Data for 2026 include publications indexed up to August 11, 2026 and should therefore be interpreted as partial-year data). The first publication on digital biomarkers appeared in 2014, and sustained annual publication output began in 2016. By 2019, the annual number of publications had increased to 33. The publication count first exceeded 100 in 2021 (n=102), increased to 118 in 2023, and reached the highest annual output during the study period in 2025 (n=255). In 2026, although data were available only up to August 11, 241 publications had already been indexed, approaching the full-year output of 2025. Overall, publication output expanded rapidly, with a compound annual growth rate of 71.4% from 2016 to 2025.

Figure 2.

Figure 2.

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The annual number of publications on digital biomarkers.

3.2. Distribution of countries/regions

A total of 96 countries/regions contributed publications on digital biomarkers. Among them, 95 countries/regions formed the largest collaboration network, as shown in Figure 3(c). The world distribution map of all 96 countries/regions, based on publication volume, is shown in Figure 3(b). The United States ranked first with 415 publications, substantially exceeding the United Kingdom (TP=149), China (TP=116), and Germany (TP=113), which also produced more than 100 publications. Twenty-nine countries/regions fall into the group with 10–99 publications, distributed across Europe (n=15), Asia (n=9), South America (n=3), North America (n=1), and Oceania (n=1). The remaining 63 countries/regions have fewer than 10 publications, covering Europe (n=19), Asia (n=19), Africa (n=14), South America (n=6), North America (n=4), and Oceania (n=1). This indicates that digital biomarker research has achieved global reach, with Europe and Asia showing the broadest participation.

Figure 3.

Figure 3.

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(a) Trends in Single Country Publications (SCP) and Multiple Country Publications (MCP) in the digital biomarkers field from 2014 to 2026 (b) Global publication distribution map of digital biomarkers research (c) The co-authorship network map of 95 countries/regions on digital biomarkers.

To characterize the publication distribution and international collaboration patterns in digital biomarker research, Figure 3(a)–(c) were generated. As shown in Figure 3(a), the proportion of multiple country publications remained relatively stable at approximately 33% during 2020-2023. Beginning in 2023, international collaboration started to rise, reaching 40% by 2025. Figure 3(c) illustrates the collaborative relationships among 95 countries/regions. Based on both the number of links and the total link strength (TLS), the United States holds a central position in the co-authorship network (links=85; TLS=523), followed by the United Kingdom (links=81; TLS=455), Netherlands (links=80; TLS=284), and the Germany (links=79; TLS=362), highlighting the dominance of North American and European countries. Among all 96 countries/regions, the United States and the United Kingdom exhibit the closest collaborative link, recording a link strength of 58.

Table 1 lists the top 10 countries with the most publications on digital biomarkers. The United States has the highest number of publications (TP=415), far exceeding that of other countries/regions, accounting for 39.3% of the total publications. The United Kingdom and China ranked second and third, with 149 and 116 publications, respectively. The United States also has the highest number of citations (TC=10,006). In terms of the average number of citations, the top three countries are Canada, United Kingdom and the United States, with an average number of citations of 25.02, 24.52 and 24.11 respectively, indicating that these countries have relatively high research influence and high academic recognition. (GDP rank (current US$) in 2025 was obtained from World Bank WDI, indicator NY.GDP.MKTP.CD (https://data.worldbank.org/indicator/NY.GDP.MKTP.CD)).

Table 1.

The top 10 productive countries on digital biomarkers.

Rank Country GDP rank1 TP TC ACP APY
1 United States 1 415 10,006 24.11 2023.21
2 United Kingdom 5 149 3,653 24.52 2023.28
3 China 2 116 869 7.49 2024.87
4 Germany 3 113 2,326 20.58 2023.39
5 Switzerland 20 93 2,008 21.59 2022.98
6 Netherlands 18 75 1,574 20.99 2023.47
7 South Korea 13 74 748 10.11 2024.07
8 Italy 9 73 1,371 18.78 2024.23
9 Spain 12 63 640 10.16 2024.21
10 Canada 10 59 1,476 25.02 2023.12
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Abbreviations: TP = total number of publications; TC = total number of citations; ACP = average citations per publication; APY = average publication year.

3.3. Productive journals on digital biomarkers

A total of 379 journals have contributed on digital biomarkers. Table 2 lists the top 10 productive journals. Sensors (TP=64) is the most productive journal in the domain, with the Journal of Medical Internet Research (TP=50) ranking second. NPJ Digital Medicine (TC=1,771) is the journal with the highest total citation count, followed by Journal of Medical Internet Research (TC=1,169) and Scientific Reports (TC=820). These findings indicate that digital biomarker research is distributed across journals from diverse disciplines.

Table 2.

The top 10 productive journals on digital biomarkers.

Rank Journal TP TC ACP APY
1 Sensors 64 747 11.67 2023.77
2 Journal of Medical Internet Research 50 1,169 23.38 2023.08
3 Scientific Reports 42 820 19.52 2023.60
4 NPJ Digital Medicine 39 1,771 45.41 2023.05
5 JMIR mHealth and uHealth 26 771 29.65 2022.23
6 Frontiers in Neurology 25 216 8.64 2023.60
7 Frontiers in Psychiatry 17 215 12.65 2022.82
8 Alzheimer’s & Dementia 17 197 11.59 2025.12
9 IEEE Journal of Biomedical and Health Informatics 17 166 9.76 2023.88
10 Brain Sciences 15 156 10.40 2024.40
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Abbreviations: TP = total number of publications; TC = total number of citations; ACP = average citations per publication; APY = average publication year.

3.4. Institutional distribution and collaboration network

A total of 2,389 institutions have contributed to publications on digital biomarkers. This study used VOSviewer to analyze their collaborative network. After setting a threshold of 5, a maximum network containing 173 institutions was obtained, consisting of 8 clusters, as shown in Figure 4. The co-authorship network is structured into several distinct clusters. Cluster 1 (red) was the largest, comprising 48 institutions. Harvard University was the most connected institution in this cluster, with 103 links, followed by Massachusetts General Hospital (links=48) and the University of California, San Francisco (links=47). Cluster 2 (green) consists of 28 institutions centered around Charité – Universitätsmedizin Berlin, which maintains collaborative ties with numerous productive institutions (links=61). Additional highly connected institutions in this cluster included Imperial College London, Karolinska Institutet, and the University of Turku, each with 54 links. Cluster 3 (dark blue) was the third largest grouping, comprising 27 institutions, with Fraunhofer Institute for Algorithms and Scientific Computing SCAI showing the highest number of links (links=49).

Figure 4.

Figure 4.

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The co-authorship network map of productive research institutions on digital biomarkers.

Table 3 shows the top 10 productive institutions. Among the institutions leading the field, six are based in the United States, two in the United Kingdom, and the remaining are situated in Switzerland and South Korea, respectively. Harvard University has been found to demonstrate the highest levels of both publication volume and citation frequency, with 58 publications and 2,013 citations respectively. King’s College London ranked second in terms of publication output (TP=30), with 698 citations. The Massachusetts General Hospital occupies third position, with 25 publications and 916 citations. In terms of citations, Stanford University (TC=1,307) and Oregon Health & Science University (TC=949) followed Harvard University, ranking second and third, respectively. These institutions showed high research productivity and, in some cases, substantial citation impact within digital biomarker research.

Table 3.

The top 10 productive institutions on digital biomarkers.

Rank Institution Country TP TC APY
1 Harvard University United States 58 2,013 2022.98
2 King’s College London United Kingdom 30 698 2023.20
3 Massachusetts General Hospital United States 25 916 2022.88
4 Swiss Federal Institute of Technology Switzerland 23 378 2022.70
5 Stanford University United States 22 1,307 2023.36
6 Oregon Health & Science University United States 22 949 2022.55
7 University of California, San Francisco United States 22 501 2024.14
8 University College London United Kingdom 22 381 2024.00
9 Baylor College of Medicine United States 20 370 2022.00
10 Seoul National University South Korea 20 137 2024.60
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Abbreviations: TP = total number of publications; TC = total number of citations; APY = average publication year.

3.5. Distribution and co-authorship of authors

A total of 7,188 authors have contributed to digital biomarkers. However, only 49 authors have published five or more publications, while 6,838 authors have produced only one or two publications, accounting for 95.13% of all contributors. Table 4 lists the top 10 authors on digital biomarkers. The top three authors by publication volume are Najafi, Bijan (TP=18), Jacobson, Nicholas C. (TP=14), and Horak, Fay B. (TP=10). Horak, Fay B. received the highest number of citations among the top authors (TC=636; ACP=63.60). Recent contributors included Li, Kai (APY=2024.56), Wang, Chen (APY=2024.56), and Li, Shuwu (APY=2024.38). Several authors with relatively fewer publications demonstrated high citation impact, including Little, Max A. (TP=5; TC=583; ACP=116.60), Bloem, Bastiaan R. (TP=5; TC=510; ACP=102.00), and Espay, Alberto J. (TP=6; TC=490; ACP=81.67).

Table 4.

The top 10 productive authors on digital biomarkers.

Rank Author TP TC ACP APY
1 Najafi, Bijan 18 259 14.39 2022.22
2 Jacobson, Nicholas C. 14 621 44.36 2021.57
3 Horak, Fay B. 10 636 63.60 2022.80
4 Fleisch, Elgar 9 133 14.78 2022.78
5 Li, Kai 9 29 3.22 2024.56
6 Wang, Chen 9 29 3.22 2024.56
7 Hadjileontiadis, Leontios J. 8 242 30.25 2023.63
8 Nef, Tobias 8 139 17.38 2022.13
9 Barata, Filipe 8 131 16.38 2022.75
10 Li, Shuwu 8 28 3.50 2024.38
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Abbreviations: TP = total number of publications; TC = total number of citations; ACP = average citations per publication; APY = average publication year.

3.6. Keyword co-occurrence and thematic structure

In bibliometric analysis, keywords, as a concise representation of research topics, effectively reflect the development trends and frontiers of a subject area. This study employed VOSviewer to screen and consolidate keyword data, yielding 4,328 keywords. The minimum frequency threshold was set to 10, and 148 high-frequency keywords were ultimately incorporated to form a co-occurrence network map, which has four clusters (Figure 5). Node size represents keyword frequency, while line width reflects co-occurrence strength between nodes. The four thematic clusters are as follows:

Figure 5.

Figure 5.

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The co-occurrence network keywords map of keywords on digital biomarkers.

First Cluster (Red Cluster): Keywords in this cluster focus on the application of digital biomarkers in Parkinson’s disease, older-adult activity, and mobility monitoring. Terms such as Parkinson disease (n=116), gait (n=77), wearable electronic devices (n=75), older-adults (n=66), and remote monitoring (n=35) underscore this trend.

Second Cluster (Green Cluster): Keywords in this cluster focus on the application of digital biomarkers in AI-assisted diagnosis, classification, and prediction. Terms like machine learning (n=160), artificial intelligence (n=133), diagnosis (n=90), classification (n=53), and prediction (n=40) underscore this trend.

Third Cluster (Blue Cluster): Keywords in this cluster focus on the application of digital biomarkers in remote mental health assessment and passive physiological monitoring. Terms such as telemedicine (n=75), depression (n=64), digital phenotyping (n=56), sleep (n=55), and mental health (n=30) underscore this trend.

Fourth Cluster (Yellow Cluster): Keywords in this cluster focus on the application of digital biomarkers in cognitive dysfunction and neurodegenerative disease assessment. Terms such as cognitive dysfunction (n=137), Alzheimer disease (n=125), dementia (n=117), neurodegenerative diseases (n=17), and cognitive assessment (n=16) underscore this trend.

3.7. Co-citation analysis of cited references

The 1056 publications on digital biomarkers cited a total of 50,404 references, among which 884 references were cited more than five times. Table 5 presents the top 10 co-cited references, with citation frequencies ranging from 37 to 68. The most frequently co-cited publication was “Developing and adopting safe and effective digital biomarkers to improve patient outcomes”, published in NPJ Digital Medicine in 2019. Kourtis et al.’s 2019 NPJ Digital Medicine publication is the second most frequently cited reference, which explores the application of mobile and wearable device-based digital biomarkers in Alzheimer’s disease monitoring. The third most frequently cited publication was Goetz et al.’s 2008 article in Movement Disorders, which introduced and validated the MDS-UPDRS, a revised scale for assessing motor and non-motor aspects of Parkinson’s disease. A comprehensive review of the extant publications reveals a broadening of sources, encompassing prominent high-impact journals in the disciplines of digital medicine, neurology, geriatrics, and psychiatry. This reflects the multidisciplinary foundation of digital biomarker research.

Table 5.

The top 10 highly co-cited references on digital biomarkers.

Rank Year First author Title Journal TC
1 2019 Coravos, A Developing and Adopting Safe and Effective Digital Biomarkers to Improve Patient Outcomes NPJ Digital Medicine 68
2 2019 Kourtis, LC Digital biomarkers for Alzheimer’s disease: the mobile/wearable devices opportunity NPJ Digital Medicine 63
3 2008 Goetz, CG Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale Presentation and Clinimetric Testing Results Movement Disorders 47
4 2019 Piau, A Current State of Digital Biomarkers Technologies for Real-Life, Home-Based Monitoring of Cognitive Function for Mild Cognitive Impairment to Mild Alzheimer Disease and Implications for Clinical Care: Systematic Review Journal of Medical Internet Research 44
5 1975 Folstein, MF “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician Journal of Psychiatric Research 43
5 2005 Nasreddine, ZS The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool for Mild Cognitive Impairment Journal of the American Geriatrics Society 43
7 2019 Babrak, Lmar M Traditional and Digital Biomarkers:
Two Worlds Apart?		 Digital Biomarkers 40
7 2017 Insel, TR Digital Phenotyping Technology for a New Science of Behavior Journal of the American Medical Association 40
7 2022 Vasudevan, S Digital biomarkers: Convergence of digital health technologies and biomarkers NPJ Digital Medicine 40
10 2001 Breiman, L Random forests Machine Learning 37
10 2017 Lundberg, SM A Unified Approach to Interpreting Model Predictions Advances in Neural Information Processing Systems 37
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Abbreviations: TC = total number of citations.

4. Discussion

In this study, we conduct a thorough bibliometric analysis to map the current global research landscape for digital biomarkers. The present study employed VOSviewer to analyze 1056 relevant publications within the digital biomarker field, thereby revealing publication trends, key contributors, thematic evolution, and other pertinent characteristics. The findings underscore contemporary research trends, providing valuable insights and recommendations for future investigative endeavors.

4.1. Research overview and characteristics of publications

The temporal trajectory of publications reveals the rapid expansion of digital biomarker research. 29 Since the first publication on digital biomarkers in 2014, the number of annual publications overall increased rapidly, reaching 255 in 2025. Notably, publications from 2020 to 2025 accounted for approximately 72.35% of the total output during the entire research period, indicating that digital biomarker research has entered a phase of rapid and sustained growth in recent years.

A substantial corpus of research findings on digital biomarkers has been published in a range of academic journals. The top 10 journals in this field account for approximately 29.55% of total publications. These publications are primarily concentrated in the high-quality journal quartiles (Q1 and Q2). 30 Core journals such as Sensors, the Journal of Medical Internet Research, Scientific Reports and NPJ Digital Medicine span disciplines from engineering to digital health, underscoring the interdisciplinary publication landscape of digital biomarker research.31,32 In this field, medicine, artificial intelligence, and bioengineering converge to drive innovations in precision health and data-driven clinical care.1,33–35

4.2. Analysis of research contributors and collaborative networks

International collaboration accounted for 35.89% of all publications, 36 underscoring the highly globalized and interdisciplinary nature of digital biomarker research. 37 As demonstrated in Figure 3(c), despite the prevalence of international collaboration, cross-regional connections remain disproportionate. Europe and North America have been observed to engage in extensive intra-regional collaboration and robust cross-regional linkages. These stronger collaborative links may be related to the long-standing central position of Europe and North America in international research collaboration networks. In contrast, the extent of collaboration between Europe and Asia remains comparatively limited. This contrast may be partly related to greater differences between Europe and Asia in healthcare systems, data governance frameworks, device ecosystems, and clinical validation practices. Digital biomarker research typically requires long-term sensor data, wearable or mobile device platforms, clinical cohorts, and home-based or multi-center monitoring settings.38–41 These requirements make international collaboration dependent on compatible data governance processes, privacy protection mechanisms, and standardized measurement protocols, especially when vulnerable populations and home-based monitoring are involved.41,42 In addition, the broader challenges in multi-center medical data sharing and digital health data harmonization may further increase the difficulty of cross-regional collaboration.43,44 These obstacles may partly explain why, despite high publication activity in both Europe and Asia, their connections in the co-authorship network remain relatively weak.

In addition, Some regions, including Africa, Oceania, and the Middle East, participate to a lesser extent in the global co-authorship network, underscoring the necessity of further deepening international collaboration. The United States maintained a clear lead in both productivity (TP=415) and citation impact (TC=10,006), reflecting its crucial role in shaping the digital biomarker research landscape. Canada and United Kingdom exhibited high quality and strong recognition within the academic community, as indicated by their average citation of 25.02 and 24.52, respectively. China has shown rapid growth in publication output, indicating expanding engagement with digital biomarker research. Taken together, digital biomarker research shows broad global participation, but collaboration and international influence remain uneven across regions.

In the analysis of institutional contributions, the top 10 most productive organizations are mainly concentrated in high-income countries such as the United States, the United Kingdom, Switzerland, and South Korea. 34 Especially, American institutions have a significant advantage. This distribution might be related to these countries’ strong foundation in biomedical research, mature digital health technology environment, and high research investment. 45 At the same time, the top 10 high-yield institutions include comprehensive universities, medical schools, hospitals, and technical universities, demonstrating the obvious interdisciplinary nature of digital biomarker research. The institution cooperation network further shows that several regional cooperation cores have formed in this field, especially the cooperation groups represented by institutions in North America and Europe. Multi-institutional cooperation may help improve the reliability and clinical applicability of research findings.46,47 However, the high-yield institutions are mainly situated in a limited number of high-income countries, which also indicates that research participation on a global scale is still unbalanced. In the future, it is still necessary to strengthen cooperation and data sharing among different regions.

In the analysis of author contributions, leading researchers such as Najafi, Bijan, Jacobson, Nicholas C., and Horak, Fay B., rank highly in terms of both publication volume and citation impact, playing a pivotal role in the field of digital biomarkers. Although a small group of highly productive scholars contributed a large share of publications, most authors had relatively few publications, suggesting that the author community in digital biomarker research is still developing. Furthermore, the average publication year of some highly productive authors is relatively late, indicating that the author structure of digital biomarker research is still in a state of dynamic change. Strengthening collaborative networks and expanding institutional participation are crucial for sustaining the field’s continued growth and enabling large-scale, multi-center studies in the future.

4.3. Knowledge structure in digital biomarkers research

Co-citation analysis of cited references effectively revealed the underlying knowledge base and research background of digital biomarkers research. 20 Table 5 lists the top 10 most co-cited references, which mainly focus on the conceptual foundations of digital biomarkers, clinical assessment of neurodegenerative and cognitive disorders, mobile and wearable device-based monitoring, digital phenotyping, and machine learning methods. Based on this foundation, subsequent keyword and cluster analysis further clarified the thematic landscape and emerging research frontiers of digital biomarkers, providing valuable perspectives on the evolving intellectual landscape of the field. 48 As illustrated in Figure 5, four major thematic domains related to digital biomarkers are identified through clustering analysis.

4.3.1. First cluster (red cluster)

The red cluster centers on the application of digital biomarkers in Parkinson’s disease, older-adult activity, and mobility monitoring. Core keywords include “Parkinson disease”, “gait”, “older-adults”, “wearable electronic devices” and “remote monitoring”.

The growing global ageing population and the increasing prevalence of mobility-limiting conditions, such as Parkinson’s disease,49,50 dementia, 51 and Alzheimer’s disease, 52 have created an urgent demand for scalable, low-burden monitoring solutions beyond clinical settings. With advances in wearable sensing and motion analysis technologies, digital devices can continuously capture gait and motor function data, supporting the development of digital biomarkers for monitoring mobility-related changes. 53 Moreover, falls remain a major cause of morbidity and mortality among older adults.54,55 Wearable-based digital biomarkers may help identify changes in mobility and fall risk, thereby supporting fall-risk prevention. These developments in digital biomarkers technology not only align with global initiatives promoting healthy ageing and independent living, but also support the transition towards more proactive and technology-enabled elderly care.

However, further applying these research findings to the daily care of older adults remains challenging. Algorithms developed in controlled clinical settings may not be directly applicable to real-life scenarios, as there are significant differences in behavioral patterns, environmental conditions, and the use of assistive devices during free-living monitoring. Projects such as the Mobilise-D consortium have started to compare wearable-derived digital mobility outcomes with reference measurement systems across a range of health conditions and real-world scenarios. This provides an important reference for the technical and clinical validation of gait-related digital biomarkers. 56 From a regulatory perspective, wearable device-derived digital indicators have so far received limited regulatory qualification or recognition. 57 To realize the clinical potential of gait- and mobility-related digital biomarkers, not only will algorithmic optimization be required, but also cross-sector validation and a clearer regulatory approval pathway, so that gait-derived digital biomarkers can be integrated into clinical decision support systems for fall risk assessment within routine geriatric care.

4.3.2. Second cluster (green cluster)

The green cluster centers on the application of digital biomarkers in AI-assisted diagnosis, classification, and prediction. Core keywords include “machine learning”, “artificial intelligence”, “diagnosis”, “classification”, and “prediction”.

Over the past decade, with the rapid advancement of technologies such as artificial intelligence (AI) and machine learning (ML), governments and health organizations worldwide have actively promoted the application of AI and ML in clinical research and healthcare services. 58 In this policy and technological context, AI- and ML-driven analytics have substantially expanded the capabilities of digital biomarkers, enabling the integration and interpretation of large-scale multimodal physiological and behavioral data. 59 In disease surveillance, wearable sensors and mobile devices can continuously capture physiological and behavioral data, providing objective measures for monitoring disease trajectories in the real world beyond traditional clinical assessments.60,61 In diagnostic and predictive applications, AI and ML algorithms can identify subtle physiological and behavioral patterns from digital biomarker data, thereby improving early disease detection, diagnostic precision, and the monitoring of individualized treatment across neurodegenerative, cardiometabolic, and oncological disorders.62–64 In summary, the integration of digital biomarkers with AI-driven analytics reflects the growing role of data-driven approaches in diagnosis, classification, and prediction. 65

However, the frequent appearance of keywords related to “artificial intelligence” and “machine learning” does not directly indicate that clinical translation has been achieved. First, sensor data collected from different devices and platforms and at varying sampling rates are not fully standardized, making it difficult to compare and replicate research findings. 66 Second, real-world longitudinal data often contain missing values, noise, and adherence-related issues, which may affect data quality and subsequently influence model robustness.67,68 Third, if the training data do not adequately represent different ages, disease stages, ethnic backgrounds, or healthcare settings, algorithmic bias may arise.69,70 Finally, regulatory validation remains challenging because many AI-based digital biomarkers are device-dependent, subject to continuous updates, and difficult to evaluate through a single standardized pathway. 71 Therefore, future research should not stop at model development but should place greater emphasis on sensor standardization, data quality control, external validation, model transparency, and regulatory coordination.66,71

4.3.3. Third cluster (blue cluster)

The blue cluster centers on the application of digital biomarkers in remote mental health assessment and passive physiological monitoring. Core keywords include “telemedicine”, “depression”, “digital phenotyping”, “sleep” and “mental health”.

In psychiatric research, clinical practice, and mental health care, digital biomarkers may offer unique advantages for continuous assessment.72,73 Digital biomarkers can capture symptom dynamics and context-dependent behavioral patterns. This makes it possible to conduct continuous and long-term longitudinal monitoring of individuals, thereby complementing traditional intermittent clinical evaluations based on outpatient visits and facilitating the early identification of disease recurrence or treatment response. 74 This indicates that mental health assessments are gradually shifting from single evaluations that rely on patients’ regular visits to continuous monitoring based on changes in daily behaviors.75,76 The COVID-19 pandemic further amplified this trend by both heightening the global mental-health burden and restricting face-to-face care. 77 However, the effective application of remote monitoring still requires sustained patient engagement with devices and continuously generate high-quality data, which has also become an important factor affecting its clinical implementation.78,79 In this context, digital phenotyping, which quantifies behavior, mood, and cognition in real time using smartphones and wearable devices, provides a promising approach for continuous and ecologically valid assessment of depression, anxiety, and related disorders. For example, smartphone sensor-derived passive digital phenotyping signals, such as reduced mobility and decreased communication frequency, have been shown to function as digital biomarkers. These biomarkers serve not only to index the severity of social anxiety, 80 but also predict depressive worsening through reductions in daily movement and disruptions in sleep and wake patterns. 10

However, the application of passive digital phenotyping still faces challenges. Due to the significant differences in symptom manifestations and behavioral patterns between diseases such as depression and anxiety, the behavioral signals in different populations and environments may not have the same meaning, which increases the difficulty of verification.75,81 At the same time, the continuous collection of personal data such as location and movement also brings privacy and ethical issues.82,83 Moreover, regulatory and clinical implementation pathways for mental health-related digital biomarkers remain insufficiently established, and only a few tools have entered the actual clinical decision-making stage.81,84 In the future, efforts need to be made to strengthen data governance and cross-population verification to promote its application in medicine.

4.3.4. Fourth cluster (yellow cluster)

The yellow cluster centers on the application of digital biomarkers in cognitive dysfunction and neurodegenerative disease assessment. Core keywords include “cognitive dysfunction”, “Alzheimer disease”, “dementia”, “neurodegenerative diseases” and “cognitive assessment”.

The aging global population and rising prevalence of Alzheimer’s disease and related dementias create an urgent need for scalable methods to monitor cognitive function in the early stages of these conditions. 85 Furthermore, cognitive decline usually progresses slowly and often goes unnoticed in routine clinical practice,86,87 highlighting the need for more sensitive tools for early identification. In this context, digital biomarkers from routine smartphone usage, wearables, and speech recordings have emerged as sensitive indicators of subtle cognitive and behavioral changes in real life. For example, speech-derived biomarkers, such as reduced lexical richness, slower articulation, and prolonged pauses, can sensitively reflect early cognitive deterioration in Alzheimer’s disease and mild cognitive impairment.88–90 Similarly, digital biomarkers extracted from typing dynamics, gait characteristics, and touchscreen interaction patterns may help identify psychomotor slowing and executive dysfunction at preclinical stages.91,92 In addition, digital biomarkers from wearable devices and environmental sensors enable passive monitoring of daily activities and sleep patterns, allowing for continuous, ecologically valid assessments of cognitive health. 93 However, applying digital biomarkers to early cognitive decline and neurodegenerative diseases also raises ethical concerns, as individuals may receive risk information without clear intervention pathways, and issues around psychological harm and the right to know remain unresolved. 94

Taken together, these advances highlight the promise of digital biomarkers for early detection and longitudinal monitoring of neurodegenerative diseases. 95 By providing objective and unobtrusive measures, they may support more proactive and personalized disease management. However, large-scale validation, ethical governance and regulatory alignment remain essential for their clinical adoption.66,96

4.4. Limitation

This study employed a bibliometric approach to systematically map the global research on digital biomarkers, but it has several inherent limitations.97,98 First, only English-language publications were included, which could have introduced language bias and underestimated the contributions of non-English-speaking regions. Second, the bibliometric indicators used in this study, such as citation counts and co-authorship networks, reflect the quantitative aspects of research activity yet they cannot capture the qualitative depth, methodological rigor, or scientific impact of individual studies. 99 Despite these limitations, bibliometric analysis remains a powerful and efficient tool for mapping scientific trends, identifying leading contributors, and guiding future research directions in the rapidly evolving digital biomarker field.

5. Conclusion

This bibliometric analysis provides a comprehensive overview of research on digital biomarkers, highlighting their rapid growth and interdisciplinary nature. Although international collaboration is extensive, publication contributions and citation influence remain concentrated in a few high-income countries/regions and leading institutions. Keyword analysis revealed four principal research hotspots and emerging themes: Parkinson’s disease and mobility monitoring in older adults; AI-assisted diagnosis, classification, and prediction; remote mental health assessment and passive physiological monitoring; cognitive dysfunction and neurodegenerative disease assessment. These findings indicate that clinical and public health applications represent important directions within digital biomarker research. Strengthening global collaboration and conducting large-scale, longitudinal studies will be important for advancing evidence generation and supporting future clinical applications of digital biomarkers. The integration of digital biomarkers with emerging technologies and real-world data may provide new opportunities for precision medicine and individualized health management.

Acknowledgements

The author would like to express sincere gratitude to all those who provided support and assistance in this study.

Footnotes

Author contributions: Yufei Tian:Methodology, Writing-Original draft, Writing-Review&Editing. Xin Tian: Data curation. Zheng Li: Investigation. Zhongkai Wang: Software. Haoxin Guo: Visualization. Fanyu Meng: Validation. Zhongqing Wang: Conceptualization, Supervision, Project administration, Writing-Review&Editing.

Funding: The authors received no financial support for the research, authorship, and/or publication of this article.

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iD

Zhongqing Wang https://orcid.org/0000-0002-5330-7538

Ethical considerations

Ethical approval was not required for this study, as it was based on publicly available data and did not involve human participants or animals.

Data Availability Statement

All data generated or analysed during this study are included in this published article.*

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Data Availability Statement

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