Association Between Psychosocial Characteristics and eHealth Literacy: Cross-Sectional Study of Hybrid Secondary Prevention in Mental Health

Abstract Background Increasing psychosocial burdens, such as stress and anxiety, underscore the need for accessible and effective prevention programs. Hybrid approaches, combining in-person and digital components, aim to reduce barriers and enhance flexibility. However, their effectiveness depends on participants’ eHealth literacy, which is associated with their ability to engage with digital tools. Understanding how psychosocial characteristics relate to eHealth literacy can provide insights for improving intervention design. Objective This study uses cluster analysis to explore the relationship between psychosocial characteristics and eHealth literacy in a hybrid mental health prevention program. By identifying distinct psychosocial profiles and analyzing their differences in eHealth literacy levels and patterns, this person-centered approach enables a nuanced understanding of eHealth literacy disparities beyond traditional variable-centered linear models. In addition, the study examines how sociodemographic variables are associated with eHealth literacy, providing insights into the role of psychosocial diversity in hybrid prevention programs. Methods A cross-sectional study was conducted with participants of the RV Fit Mental Health intervention (January 2024–December 2024). Psychosocial characteristics, including anxiety, depression, optimism, pessimism, quality of life, self-efficacy, stress, and work ability, were assessed alongside eHealth literacy (eHealth Literacy and Use Scale [eHLUS], eHealth Literacy Scale [eHEALS]). To identify distinct psychosocial profiles, cluster analysis was used. A generalized linear model was applied to analyze associations between cluster membership, eHealth literacy, and sociodemographic variables. Finally, correlation matrices were used to further explore the relationships between psychosocial characteristics and eHealth literacy. Results A total of 173 participants were included. Four clusters were identified based on psychosocial characteristics. Significant associations were found between cluster membership and eHealth literacy, including the overall eHLUS score ( P =.004) and its dimension of autonomous use and technical access ( P =.003). Cluster 3 (n=65) had the most favorable psychosocial characteristics and the highest eHealth literacy levels. Cluster 4 (n=36) exhibited the least favorable psychosocial characteristics but mid-range eHealth literacy levels. Cluster 2 (n=45) showed the lowest eHealth literacy levels despite a mid-range psychosocial profile. Cluster 1 (n=27) demonstrated mid-range eHealth literacy levels and mid-range psychosocial characteristics. In addition, age and subjective socioeconomic status were significantly associated with eHealth literacy levels. Beyond the identified clusters, significant correlations were observed between individual psychosocial characteristic variables and eHealth literacy. Conclusions The cluster analysis identified distinct psychosocial profiles with varying levels of eHealth literacy, demonstrating that psychosocial characteristics are associated with eHealth literacy in diverse ways. These findings underscore the need to consider subgroup-specific needs in hybrid prevention programs. Certain groups may require additional support to effectively navigate eHealth tools. These findings emphasize the relevance of tailored intervention strategies that account for psychosocial diversity in eHealth engagement.