Using a Technology Acceptance Model to Explore the Intention to Use Digital Health Technologies Among People With Disabilities: Cross-Sectional Survey Study

Abstract Background Electronic personal health records (e-PHRs) can improve health management; however, people with disabilities face adoption barriers. Identifying acceptance drivers in this population is essential. Objective This study aims to determine factors shaping intention to use e-PHRs among people with disabilities within a technology acceptance model (TAM) framework, including external determinants (health consciousness [HC], health information consent [HIC], content characteristics [CC], information security [IS], eHealth literacy [eHL], and effectiveness [EF]). Methods A nationwide survey of people with disabilities in South Korea (N=800) was conducted across rehabilitation hospitals, disability welfare centers, and public health centers (August 30 to November 30, 2023) using proportionate stratified and systematic stratified cluster sampling. Hypotheses were tested via structural equation modeling with bootstrapped mediation (2000 resamples) and multigroup analyses by disability severity. Results Usage intention (UI) was primarily driven by perceived usefulness (PU; β=0.662; P <.001) and additionally by perceived ease of use (PEU; β=0.203; P <.001). Ease of use increased usefulness (β=0.452; P <.001). External predictors of PEU were HC (β=0.233; P <.001), CC (β=0.163; P <.001), HIC (β=0.167; P <.001), IS (β=0.089; P =.005), and EF (β=0.276; P <.001); eHL was not significant (β=0.025; P =.41). Predictors of PU were EF (β=0.368; P <.001) and HIC (β=0.243; P <.001), while CC (β= −0.121; P =.002) and eHL (β= −.068; P =.003) were negative; HC and IS were not significant. Indirect effects supported PEU→PU→UI (β_indirect=0.299; 95% CI 0.210‐0.404). The largest total upstream effects on associations with intention were EF (β_total=0.382; P <.001) and HIC (β_total=0.245; P <.001). Multigroup structural equation modeling (mild, n=432; severe, n=368) indicated PU was a stronger driver of intention in the mild group (β=0.727) than the severe group ( β =.511). PEU also contributed (severe β=0.272; mild β=0.171). CC predicted PEU only in the mild group (β=0.201; P <.001), whereas IS predicted PEU only in the severe group (β=0.119; P =.003). Conclusions This study highlights that PU and PEU are crucial mediators driving the adoption of e-PHR among people with disabilities. These findings suggest the need for designing user-friendly digital health solutions that integrate robust support systems, address privacy concerns, and deliver high-quality, relevant content tailored to this population. The restriction to people with disabilities using rehabilitation, public health, or welfare centers introduces selection bias. Future studies should broaden sampling to include a diverse population.