Determinants of Digital Health Technology Acceptance Among Healthcare Caregivers: A Structural Equation Modeling Approach

Background: Digital health technologies, including artificial intelligence (AI)-powered tools and virtual reality (VR) interventions, are increasingly being deployed to support caregivers of patients with chronic conditions. However, the factors influencing caregiver acceptance of these technologies remain poorly understood. Objective: This study aimed to develop and validate a structural equation model (SEM) to examine the determinants of digital health technology acceptance among caregivers of patients with end-stage kidney disease (ESKD). Methods: A cross-sectional survey was conducted among 342 caregivers recruited from nephrology clinics across three tertiary hospitals in Singapore. The survey instrument measured perceived usefulness, perceived ease of use, social influence, facilitating conditions, caregiver burden, technology anxiety, and behavioral intention to use digital health tools. Confirmatory factor analysis (CFA) and structural equation modeling were performed using maximum likelihood estimation. Results: The final SEM demonstrated good model fit (CFI = 0.952, TLI = 0.943, RMSEA = 0.048, SRMR = 0.041). Perceived usefulness ({beta} = 0.42, p < 0.001), perceived ease of use ({beta} = 0.31, p < 0.001), and social influence ({beta} = 0.28, p < 0.001) were significant positive predictors of behavioral intention. Caregiver burden had an indirect effect on intention mediated through technology anxiety ({beta} = -0.18, p = 0.003). The model explained 64% of the variance in behavioral intention to adopt digital health technologies. Conclusions: This study provides a validated framework for understanding caregiver acceptance of digital health technologies. Interventions targeting perceived usefulness and addressing technology anxiety among burdened caregivers may enhance adoption rates. These findings have implications for the design and implementation of AI-powered and VR-based caregiver support systems.