Latent profile analysis of digital health literacy among community-dwelling older adults and its influencing factors

Introduction Digital health advances universal health coverage, yet insufficient digital health literacy exacerbates the digital divide and health inequalities among older adults. Existing studies lack in-depth exploration of within-group digital health literacy heterogeneity among Chinese community-dwelling older adults. This study aimed to identify digital health literacy latent profiles and associated factors to inform targeted interventions. Methods A cross-sectional survey was conducted among 535 community-dwelling older adults (aged ≥60 years) from urban and suburban communities in China. Latent profile analysis was used to identify digital health literacy subgroups based on three core subdomains: Self-Perception (SP), Information Acquisition (IA), and Interactive Judgment (IJ). Multinomial logistic regression was applied to examine factors associated with profile membership. Results Three distinct digital health literacy profiles were identified: Low SP-IA-IJ (38.88%), Medium SP-IA & Low IJ (47.85%), and High SP-IA & Medium IJ (13.27%). Notably, Interactive Judgment (IJ) was a universal weak dimension across all groups. Older age, cognitive decline, depressive symptoms, and loneliness were significant risk factors; higher education, better economic status, favorable self-rated health, and stronger social interaction were protective factors, with differential associations across profiles. Conclusions Overall digital health literacy among Chinese community-dwelling older adults is generally low with significant within-group heterogeneity. Targeted stratified interventions, universal Interactive Judgment (IJ) capacity building, and age-friendly digital environment optimization can help narrow the digital divide and promote equitable access to digital health services for older adults. This study is limited by its cross-sectional design and regional urban/suburban sample, restricting causal inference and generalizability to rural or other regions.