Digital health literacy and associated factors in older adult patients with chronic obstructive pulmonary disease: a latent profile analysis.

OBJECTIVE

To identify latent profiles of digital health literacy (DHL) among older adult patients with chronic obstructive pulmonary disease (COPD) and to examine the factors associated with profile membership.

METHODS

Between December 2025 and February 2026, a total of 575 older adult patients with COPD were assessed for eligibility from both the inpatient wards and the respiratory medicine outpatient clinic of a Grade A tertiary hospital in Anhui Province. The study site was selected using a convenience sampling method. To minimize selection bias and ensure representativeness within this setting, all eligible patients who presented during the study period were consecutively recruited. Of these, 550 provided valid responses and were included in the final analysis. Data collection was conducted using a set of structured questionnaires, which comprised the following instruments: a General Information Questionnaire, the digital health literacy (DHL), the COPD Self-Efficacy Scale (CSES), the Technophobia Scale (TS), and the Perceived Social Support Scale (PSSS). To identify distinct subgroups based on digital health literacy profiles, latent profile analysis (LPA) was performed using Mplus 8.3 software. Univariate and multinomial logistic regression analyses were conducted using SPSS 27.0 software to identify factors associated with profile membership.

RESULTS

Three DHL profiles were identified: low literacy-restricted basic skills type (37.1%), moderate literacy-guided learning dependent type (28.9%), and high literacy-independent efficient type (34.0%). Factors significantly associated with profile membership included education level, income, health information seeking intention, self-efficacy, technophobia, and social support.

CONCLUSION

Overall DHL levels were generally low among older adult patients with COPD, with significant variation across demographic subgroups. Personalized interventions tailored to patients' specific profile characteristics and associated factors may represent a promising strategy for enhancing DHL in this patient population. However, as these intervention strategies are derived from correlational profile data, their effectiveness requires prospective validation in future interventional studies.