Latent obstacles in older adults’ digital health participation: a community-based hybrid cluster analysis with natural language processing

The intensification of global aging has made older adults’ health issues increasingly prominent. Although digital health tools play a crucial role in aging societies, older adults still face multi-dimensional potential obstacles that restrict their effective participation. Natural Language Processing (NLP) can efficiently analyze the data of older adults’ feedback in digital health to uncover hidden patterns. Therefore, this study constructs a community-based hybrid NLP analysis method to reveal the deep obstacle mechanisms affecting the digital health participation of the elderly. In this study, interview data from 35 older adults were collected through semi-structured focus group interviews in five community health activities. After pre-processing the data, a document-term matrix was constructed using Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. By integrating the three methods of Latent Dirichlet Allocation (LDA) topic modeling, BERTopic topic modeling, and SBERT with K-means clustering analysis, the semantic features and topic distribution within the text data are systematically analyzed. Among them, BERTopic (K = 4) has become the most effective clustering model in this study with high word-level consistency (0.5328), reasonable topic diversity (0.5583), and good document-level consistency (0.8467). The research findings indicate that the potential obstacles affecting older adults’ digital health participation are primarily the functional and design manifestations of deeper, socio-psychological, and social environmental latent barriers. These are clustered into four aspects: psychological barriers to digital technology use, clarity and usability of health information, a mismatch between technology provision and user needs, and user interface (UI) design and accessibility challenges. Older adults’ relatively low digital literacy leads to fear and anxiety toward technology. In obtaining effective information, they face the issue of information overload and have doubts about the authenticity and reliability of the information. The lack of positive technology learning experiences and external support makes older adults prone to a sense of low self-efficacy. The uneven distribution of community resources and the limited technical support capabilities of family members further exacerbate the obstacles to older adults’ digital health participation. This study proposes a new method for in-depth exploration of the potential obstacles to older adults’ digital health participation from a semantic level by combining multiple clustering analysis methods. It provides valuable references and technical guidance for subsequent related research.