ArticleScientific reports2025
Factors associated with the acceptance of telemedicine services in Dusit model prototype area.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Determinants of Patient Satisfaction in a Cardiac Telemedicine Clinic: A Cross-Sectional Study From Saudi Arabia.Cureus · 2026Article
- Bridging the digital divide: student-led literacy initiatives in diabetes management.Frontiers in clinical diabetes and healthcare · 2025Review
- Mobile Health Clinics and Telehealth Outreach in Thailand: A Focus on Elderly Care and NCDs.Journal of multidisciplinary healthcare · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Dusit Model, a prototype area in Bangkok, Thailand, integrates telemedicine into primary and tertiary care to reduce overcrowding and promote equitable access. This study aimed to identify factors influencing telemedicine acceptance using an extended Technology Acceptance Model (TAM). A cross-sectional study was conducted among 389 participants using Vajira@Home. Variables included demographics, the extended Technology Acceptance Model (TAM), and telemedicine acceptance. Descriptive statistics, chi-square tests, and multivariate binary logistic regression were employed. Telemedicine acceptance was significantly correlated with generation, perceived ease of use (adjusted OR = 3.95, p = 0.047), and facilitating conditions (adjusted OR = 5.78, p = 0.013). Compared to Generation Z, Baby Boomers and Generation X had lower odds of acceptance (OR = 0.01 and 0.22, respectively). Model fit was confirmed (AUC = 0.79; Hosmer-Lemeshow p > 0.05). Generation, usability perceptions, and infrastructure support critically influence telemedicine acceptance. Policy should prioritize digital literacy for older adults, improve user experience, and invest in infrastructure to enhance equitable adoption.
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Registered trials
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