Digital health meets Parkinson’s disease: a bibliometric exploration of wearable sensors research and emerging frontiers (2005–2026)

Abstract

Wearable and sensor-based monitoring technologies are increasingly used to support objective, longitudinal assessment in Parkinson’s disease (PD). However, the global development, knowledge structure, and emerging research frontiers of this interdisciplinary field remain incompletely mapped. Bibliographic records on wearable and sensor-based monitoring technologies in PD were retrieved from the Web of Science Core Collection (WoSCC) and Scopus for the period from 1 January 2005 to 2 February 2026. Searches were conducted in title, abstract, and author keyword fields. Microsoft Excel, VOSviewer, CiteSpace, and the bibliometrix R package were used to analyze publication trends, collaboration networks, co-citation patterns, keyword co-occurrence, clustering, citation bursts, historiographic patterns, and source impact. A total of 3,332 publications were included. Publication output increased rapidly over the study period, particularly after 2019, while 2026 represented an incomplete publication year because the search was conducted on 2 February 2026. The United States (U.S.), China, and European countries constituted the main knowledge-production and collaboration structure. Thematic analyses suggested a gradual shift from engineering validation and objective motor-symptom quantification toward AI-enabled digital biomarkers, remote monitoring, multimodal monitoring, and closed-loop neuromodulation. These patterns reflect research activity and citation structures rather than direct evidence of clinical efficacy. This bibliometric analysis provides science-mapping evidence on the evolution of wearable and sensor-based monitoring technologies in PD. Future work should prioritize standardization, multicenter real-world validation, clinical workflow integration, multimodal data fusion, and cautious evaluation of closed-loop applications.