Mapping knowledge structure and research trends in gerontechnology for older adults: A multitool bibliometric and text mining analysis

With the increasing global aging population, gerontechnology has emerged as a key approach to improving older adults’ health and quality of life. Spanning rehabilitation engineering, intelligent interaction, digital health, and medical services, the field has expanded rapidly over the past decade. Yet prior work has been limited in articulating the field’s knowledge structure and its evolving trends. To address this gap, this study conducts a bibliometric analysis using the Web of Science Core Collection as the analytical dataset. We integrate CiteSpace, VOSviewer, Scimago Graphica, and Pajek to examine research structure and collaboration networks, and cross-validate keyword signals across Scopus, MEDLINE, and CNKI to enhance sensitivity to emerging topics. In addition, TF-IDF and topic modeling are used to refine keyword extraction and cluster labels, constructing a multidimensional knowledge map of gerontechnology. The findings indicate: (1) a shift of focus from ‘rehabilitation engineering and health management’ toward ‘intelligent ecosystems and data-driven health,’ extending to digital literacy, artificial intelligence, virtual reality, and smart homes – suggesting a movement from compensatory assistance to more preventive and predictive applications; (2) in international collaboration, the United States, the United Kingdom, and China occupy central positions, forming a ‘Western-dominated, Asia-Pacific-rising’ pattern; and (3) since 2020, frontier keywords point to rapid growth in AI-enabled health interventions, cross-scenario applications, and psychosocial factors. Overall, this field-level overview complements traditional narrative and systematic reviews across structure, semantics, and evolution, and offers evidence-informed guidance for researchers and educators designing programs for older adults.