ReviewFrontiers in digital health2026
Thematic landscapes and temporal trends of disability technology adoption: insights from Structural Topic Modelling.
Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Authors and funding
2 authors.
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Abstract
Introduction: In recent years, the importance of accessible and inclusive technologies has increasingly supported people with disabilities. However, prior studies on the adoption of technology remain fragmented, often focusing on specific disabilities or tools without exploring broader connections. Addressing this, the current study addresses the gaps by identifying core topics, examining temporal variations, and analyzing interrelations across domains. Methods: By Adapting Structural Topic Modelling (STM), we integrated publication year metadata to uncover thematic shifts in disability technology adoption research. Following the PRISMA framework, 211 English-language articles published between 2015 and 2024 were identified and collected from the Web of Science and SCOPUS databases. Titles, abstracts, and keywords were consolidated, cleaned, and analyzed using the STM in R. Results: The STM analysis examined six distinct themes: Assistive technologies for a person with visual and physical impairments, m-Health and communication tools for older adults, Robotics integrating cognitive-sensory innovations, Artificial Intelligence (AI)-driven solutions for Cognitive and Sensory challenges, Rehabilitation and Therapy Systems enhancing recovery and mobility, and Educational Technologies with inclusive learning solutions for individuals with disabilities. While six distinct themes were found, and the subsequent topic correlation analysis confirmed their separation, emphasizing negative connections that affirm their conceptual individuality. Discussion: This study utilizes STM to highlight the growing role of AI in Sign Language interpretation, rehabilitation, and digital learning tools. Beyond technical advances, this study emphasizes inclusive design, policy development, and cross-domain collaboration and areas often overlooked in previous research.
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