ArticleJournal of medical Internet research2025
Using a Technology Acceptance Model to Explore the Intention to Use Digital Health Technologies Among People With Disabilities: Cross-Sectional Survey Study.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Dynamics of Digital Support Platform Use Among Family Caregivers of Children With Disabilities: Longitudinal Study.JMIR mHealth and uHealth · 2026Article
- Determinants of Behavioral Intention to Use Digital Healthcare Services: A Cross-Sectional PLS-SEM Study on the Shukhee App in Bangladesh.Health science reports · 2026Article
- Determinants of Patients' Intention to Use Remote Monitoring Service for Cardiac Implantable Electronic Devices: An Extended Technology Acceptance Model Study in Taiwan.Healthcare (Basel, Switzerland) · 2026Article
- User Acceptance of Remote Care Assist, a Telecare System for Home Care Among Care and Nursing Staff: Cross-Sectional Pilot Study.JMIR rehabilitation and assistive technologies · 2026Article
- Factors Associated with the Intention to Adopt Digital Health Technologies for Physical Activity Among People with Disabilities: An Integrated Technology Acceptance Model-Theory of Planned Behavior Framework.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.