Structural biases in digital health intervention and health equity research: a bibliometric knowledge graph analysis (2015–2025) - Shengtao Li; Zhongyue Li; Cheng Chen; Wu Chen; Wenqian Du; Xiaolin Yao (Frontiers in Digital Health, 2026)
Objective This study systematically analyzed the knowledge structure, evolution patterns, and structural biases in the intersection of digital health interventions, patient engagement/adherence, and health equity from 2015 to 2025. Methods We searched Scopus, Web of Science Core Collection, and PubMed. We included 4,677 original articles or systematic reviews. We used Bibliometrix, SciExplorer, and CiteSpace for bibliometric and knowledge graph analysis. Results The annual number of publications increased from 75 to 1,317 (R 2 = 0.9955). The average annual growth rate was 33.18%. However, author keywords were highly dispersed (9,302 vs. 4,971). Geographical distribution showed “one superpower and multiple strong countries.” The United States accounted for more than 54% of the top ten countries’ frequencies. China ranked fifth but did not enter the core collaboration network. JMIR series journals published 58.8% of the top ten journals’ total articles. Keyword clustering identified ten themes, including telemedicine and chronic disease management. “Health equity” did not become an independent high-frequency cluster. The author collaboration network showed a multi-center structure with methodologists, theory builders, and tool developers as cores. Conclusion This field faces “knowledge disorder under exponential growth.” There are systematic biases in geography, theme, and power structure. We recommend building a core literature database, creating interdisciplinary journals, supporting reverse innovation led by low- and middle-income countries, and reforming academic evaluation systems to bridge the gap between research and policy translation.