Many Ways to Engage: Uncovering How Adults Navigate a Digitally Presented Health-Related Literacy Item

Background/Objectives: Digital health literacy refers to the ability to find, understand, and use health information from digital sources. Disparities in these skills contribute to health inequalities and poorer outcomes. This study examines how adults with varying literacy skills navigate a digitally presented health-related literacy task. We identify engagement profiles and examine relations to demographics, daily skills-use, self-reported health, and literacy performance. Methods: We analyzed public-use data from the 2022–2023 U.S. Program for the International Assessment of Adult Competencies (PIAAC) Cycle 2 assessment. The sample included 3722 adults who completed a digitally presented medicine-label literacy item. Process indicators included total item time, number of actions, visits, latency before the first action, and interaction speed. Latent profile analysis was used to identify engagement patterns, which were then descriptively examined in relation to demographics, home skills-use, and literacy proficiency. Results: Latent profile analysis of total time, actions, visits, hesitation and speed revealed five engagement patterns: Early Exit (8 s, 0 actions, 0% correct; minimal interaction); Hesitant-Minimal (36 s, 2 actions, 30% correct; long pauses with minimal interaction); Rapid Exploratory (54 s, 44 actions, 51%; fast, high-volume clicking); Direct/Routine (57 s, 6 actions, 58%; few actions at a moderate pace); and Deliberate Exploratory (80 s, 20 actions, 62%; longer task time with higher action counts). The identified profiles showed descriptive variation in age, education, self-rated health, skills-use, and literacy level. Conclusions: Process data revealed distinct engagement patterns on a digitally presented health-related literacy task that were not captured by accuracy alone. These findings highlight the value of examining how adults interact with digitally presented health-related information, while additional tasks are needed to determine whether similar patterns generalize to broader digital health contexts.