Abstract
Cardiovascular disease (CVD) is a leading global health concern with rising morbidity and mortality. Despite medical advancements, effective self-management remains challenging due to patients' limited health literacy. While digital health interventions offer promising solutions, their efficacy depends on eHealth literacy. The mobile eHealth Literacy Scale (m-eHEALS) was developed to assess this construct, but its validity and applicability in CVD populations require further evaluation. This study evaluated the psychometric properties of the m-eHEALS in CVD patients using Rasch analysis. Patients from cardiology and neurology departments in two tertiary hospitals were consecutively enrolled (February 20-May 4, 2023). Demographic data and m-eHEALS scores were collected. Rasch analysis assessed unidimensionality, item fit, reliability, item difficulty, characteristic curves, and differential item functioning (DIF). A total of 302 patients' data were analyzed. The scale exhibited good psychometric properties, with a person separation index of 4.02 (person reliability = 0.94) and an item separation index of 8.96 (item reliability = 0.99). The unidimensionality analysis revealed a total explained variance of 70.5%; however, the first component residual eigenvalue was 3.1, suggesting potential multidimensionality. Item fit analysis identified five items (N4, N5, N6, N10, and N11) with misfit statistics outside the acceptable range (Infit MNSQ 0.49-1.55). ICC analysis confirmed good item discrimination for most items, though deviations were observed for N10 and N11. DIF analysis indicated gender-based differences for N12 (0.71 logits harder for females). The m-eHEALS demonstrated strong psychometric properties for assessing eHealth literacy in individuals with CVD, though some items exhibited poor fit with the data. Future research should explore subgroup characteristics to further enhance the scale's accuracy and broaden its applicability across different patient groups.