ArticleJMIR human factors2023
Social Determinants of Health and Patients' Technology Acceptance of Telehealth During the COVID-19 Pandemic: Pilot Survey.
Article in JMIR human factors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Acceptability and Use of Digital Health and Artificial Intelligence-Enabled Chatbots for Sexual and Reproductive Health Among Lesbian, Bisexual, and Queer Women of Color in the United States: Cross-Sectional Survey Study.Journal of medical Internet research · 2025Article
- Exploring Attitudes and Obstacles Around Digital Public Health Tools: Insights From a Statewide Cross-Sectional Survey on Washington's Vaccine Verification System.Journal of medical Internet research · 2025Article
- Applications of human factors approaches and methods to the development of teleconsultations in primary care: a systematic scoping review.BMJ open quality · 2025Article
- Has telemedicine come to fruition? Parents' and pediatricians' perceptions and preferences regarding telemedicine.Pediatric research · 2024Article
- Narrowing the Digital Divide: Framework for Creating Telehealth Equity Dashboards.Interactive journal of medical research · 2024Article
- Telehealth Research and Innovation for Veterans with Cancer: the THRIVE Center.Journal of the National Cancer Institute. Monographs · 2024Article
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Authors and funding
2 authors.
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Abstract
backgroundTelehealth has been widely adopted by patients during the COVID-19 pandemic. Many social determinants of health influence the adoption.
objectiveThis pilot study aimed to understand the social determinants of patients' adoption of telehealth in the context of the pandemic.
methodsA survey methodology was used to capture data from 215 participants using Amazon Mechanical Turk. The study was guided by the technology acceptance model and the social determinants of health framework. The questionnaire included technology acceptance model variables (eg, perceived usefulness [PU] and perceived ease of use [PEOU]), social determinants (eg, access to health care, socioeconomic status, education, and health literacy), and demographic information (eg, age, sex, race, and ethnicity). A series of ordinary least squares regressions were conducted to analyze the data using SPSS Statistics (IBM Corp).
resultsThe results showed that social determinant factors-safe neighborhood and built environment (P=.01) and economic stability (P=.05)-are predictors of the PEOU of telehealth adoption at a statistically significant or marginally statistically significant level. Furthermore, a moderated mediation model (PROCESS model 85) was used to analyze the effects of COVID-19 on the neighborhood, built environment, and economic stability. PEOU and PU significantly positively affected users' intention to use technology for both variables.
conclusionsThis study draws attention to 2 research frameworks that address unequal access to health technologies. It also adds empirical evidence to telehealth research on the adoption of patient technology. Finally, regarding practical implications, this study will provide government agencies, health care organizations, and health care companies with a better perspective of patients' digital health use. This will further guide them in designing better technology by considering factors such as social determinants of health.
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