Researchers surveyed the participants from October through December 2024. Data came from a demographic and clinical questionnaire, the Older Digital Health Literacy Scale and the Online Health Information Seeking Behavior Scale. The mean digital health literacy score for the entire sample was 39.31 points, with a standard deviation of 7.27. The survey also included people across three reported digital-health-literacy patterns, even though all participants were already able to use digital devices. This makes the profiles a description of variation within a digitally connected population rather than a comparison between users and non-users.

The researchers used chi-square tests and multinomial logistic regression to examine characteristics associated with profile membership. Place of residence, monthly household income and living arrangements were significantly associated with the profiles, as was the duration of coronary heart disease. Digital behavior also showed significant associations: the frequency with which participants used digital devices for health purposes and their online health-information-seeking behavior differed across the three groups. These were statistical associations observed in the survey, and the cross-sectional design cannot determine whether any of the measured demographic, clinical or behavioral factors produced the profiles.

The authors proposed different emphases for digital support aimed at the three profiles: basic digital-skills training for the low-literacy group, guided and confidence-enhancing strategies for the middle-literacy group, and approaches to optimize advanced engagement for the high-literacy group. These proposals were not evaluated as interventions in the study. The researchers also reported that higher digital health literacy was associated with greater potential capacity for disease self-management. The finding concerns capacity rather than a demonstrated change in symptoms, clinical outcomes or quality of life, and the study did not test the effects of a health-care intervention on participants.

Several limitations constrain the findings. The data were collected at one point in time, so the analysis did not track changes in digital literacy, health status or behavior over time. Recruitment was limited to community-dwelling participants in one city, and older adults who did not use digital devices were excluded, meaning the profiles cannot be generalized to the full population of older adults with coronary heart disease. Within this scope, the study’s central result is that the ability to use a digital device does not correspond to one uniform level of digital health literacy: among the surveyed participants, low-, middle- and high-literacy profiles were distinguished by significant differences in demographic, clinical and online-behavior characteristics.

Subject of Research: Digital health literacy profiles among older digital-device users with coronary heart disease

Article Title: Latent profile analysis of digital health literacy among digital device-using older adults with coronary heart disease: a cross-sectional study

Article References: Tu, X., Hu, Y., Jiang, Y., Wang, H., Tang, M., & Wang, A. (2026). Latent profile analysis of digital health literacy among digital device-using older adults with coronary heart disease: a cross-sectional study. BMC Geriatrics. https://doi.org/10.1186/s12877-026-07987-6

Image Credits: AI Generated

DOI: 10.1186/s12877-026-07987-6

Keywords: digital health literacy, older adults, coronary heart disease, latent profile analysis, digital devices, online health information, cross-sectional study, Qingdao, health self-management, Latent, profile, analysis