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Social Determinants of Health and Patients’ Technology Acceptance of Telehealth During the COVID-19 Pandemic: Pilot Survey

BACKGROUND: Telehealth has been widely adopted by patients during the COVID-19 pandemic. Many social determinants of health influence the adoption. OBJECTIVE: This pilot study aimed to understand the social determinants of patients’ adoption of telehealth in the context of the pandemic. METHODS: A s...

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Detalles Bibliográficos
Autores principales: Anil Kumar Vaidhyam, Sneha, Huang, Kuo-Ting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10631497/
https://www.ncbi.nlm.nih.gov/pubmed/37934556
http://dx.doi.org/10.2196/47982
Descripción
Sumario:BACKGROUND: Telehealth has been widely adopted by patients during the COVID-19 pandemic. Many social determinants of health influence the adoption. OBJECTIVE: This pilot study aimed to understand the social determinants of patients’ adoption of telehealth in the context of the pandemic. METHODS: A 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). RESULTS: The 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. CONCLUSIONS: This 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.