The digital transformation of the health care system has accelerated following the COVID-19 pandemic, leading to a rapid expansion in the accessibility and utilization of digital health care services. While this shift offers potential benefits, such a...
The digital transformation of the health care system has accelerated following the COVID-19 pandemic, leading to a rapid expansion in the accessibility and utilization of digital health care services. While this shift offers potential benefits, such as more efficient chronic disease management and personalized health care, it may also exacerbate health care inequalities driven by the digital divide. Accordingly, increasing attention has been paid to digital health readiness, defined as an individual’s competence and overall preparedness to effectively use digital technologies for health management.
Middle-aged adults experience major life transitions, including retirement, their offsprings’ independence, aging-related changes, and onset of chronic diseases, which increase their need for health management. Ageism, defined as stereotypes, prejudice, and discrimination based on age, is known to intensify structural social inequalities in aging societies.
Negative expectations regarding aging that arise from ageism may influence health outcomes and health behaviors, and may further limit digital engagement, in turn strengthening digital exclusion. Yet, there is limited research on middle-aged adults’ digital health readiness that incorporates aging expectations and other psychosocial factors.
Therefore, this study developed and tested a structural model of digital health readiness among middle-aged adults, based on the Digital Engagement and Ageism (D-EngAge) model. In the hypothesized model, social support and loneliness were specified as exogenous variables, and expectations regarding aging, health self-efficacy, and digital health readiness were specified as endogenous variables. Participants were 217 middle-aged adults between 50 and 64 years of age. Data were collected through an online survey and analyzed using IBM SPSS Statistics version 29.0 and AMOS version 29.0.
The hypothesized model showed acceptable fit with the following indices, CMIN/df=2.693, TLI=.880, CFI=.900, SRMR=.078, RMSEA=.089. The model explained 33.7% of the middle-aged adults’ digital health readiness, and seven of the nine hypothesized paths were statistically significant. Health self-efficacy had the strongest direct effect on digital health readiness. Social support and loneliness did not directly affect digital health readiness, but both had significant indirect effects through health self-efficacy. Expectations regarding aging did not have a significant direct effect on digital health readiness. However, there was a significant indirect pathway whereby loneliness was associated with expectations regarding aging, which in turn influenced health self-efficacy and ultimately digital health readiness.
This study provides evidence for the complex structural relationships among psychosocial factors and ageism-related expectations in explaining digital health readiness among middle-aged adults, using the D-EngAge model. The findings suggest that strategies to improve digital health readiness in the middle-aged population should integrate efforts to strengthen social support, reduce loneliness, foster positive expectations regarding aging, and enhance health self-efficacy. By confirming an indirect pathway through which expectations regarding aging operate via health self-efficacy, the study offers a basis for a more nuanced understanding of the relationship between ageism and digital health readiness. Based on these results, the study recommends sustainable and inclusive digital health care services and nursing intervention programs tailored to middle-aged adults, supporting a healthier transition into older age and ultimately promoting digital health equity.