Heterogeneity of eHealth literacy and treatment burden in older adults with heart failure: a multidimensional latent...

Heart failure (HF) management imposes a substantial multidimensional treatment burden (BoT) on older adults. While digital health interventions offer potential solutions, their success heavily depends on patients' eHealth literacy (eHL). Previous variable-centered research evaluating total scale scores has systematically masked the individual heterogeneity and dimensional interplay between eHL and BoT.

To identify the multidimensional latent profiles of eHealth literacy and treatment burden among older adults with chronic HF, and to explore the independent sociodemographic and clinical predictors of these profiles.

A cross-sectional study was conducted involving 425 older adults with HF in China. Data were collected using demographic/clinical abstraction forms, the Chinese eHealth Literacy Scale (C-eHEALS), and the Patient Experience with Treatment and Self-Management (PETS). Latent profile analysis (LPA) was executed using 14 specific continuous dimensional indicators from these scales. Multinomial logistic regression was utilized to identify factors influencing profile membership.

Three distinct latent profiles were identified. As visually supported by the standardized Z-score patterns, the "Vulnerable" profile (28.9%) exhibited a starkly inverse clinical state characterized by profound deficits across all eHL dimensions and overwhelmingly high systemic BoT. The "Capable" profile (22.1%) demonstrated the opposite, optimal pattern (high eHL, minimal BoT). The "Transitional" profile (48.9%) displayed intermediate scores but high dimensional discordance (e.g., adequate information acquisition but poor evaluation, paired with high diet/exercise burdens). Multinomial logistic regression revealed that older age, primary-level education, living alone, and a higher Charlson Comorbidity Index were significant independent predictors of membership in the Vulnerable profile.

Older adults with HF do not experience digital health demands and self-care workloads uniformly. Identifying these highly distinct, dimension-specific typologies highlights the potential value of moving away from "one-size-fits-all" digital deployments toward precision-based, stratified care models designed to mitigate specific vulnerabilities.