ArticleFrontiers in public health2026
Measuring navigational and digital health literacy in individuals with long-term conditions: latent trait analyses using Rasch modelling.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Mapping health literacy (HL) enables health information and treatment to align with individuals' preferences and potentially makes healthcare truly person-centered. Without valid and reliable measurement tools however, we risk misreading what people actually need. This study aimed to evaluate two measurement scales developed to assess navigational and digital HL in a population sample of individuals with long-term conditions (LTCs). Methods: This secondary study used cross-sectional data from the large-scale Health Literacy Population Survey 2019-2021. The two scales were evaluated in a national population (≥ 18 years) with self-reported long-term conditions (duration ≥ 6 months). A latent trait analysis using the unidimensional Rasch partial credit model (PCM) for polytomous responses was performed to evaluate overall and item-level fit. The social determinants gender, age, education level, employment status, population density, economic situation, and social status level were included as person factors when testing for differential item functioning (DIF). Results: A total of 1,064 participants were included in the analysis. Both the Navigational Health Literacy scale (NHL) ( Conclusion: These findings contribute to the ongoing refinement and validation of the NHL and DHI scales within the LTC population and highlight the need for further improvement to strengthen the conceptual understanding of navigating the health care environment and assessing, understanding, applying and using digital health information.
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