Patterns and Influencing Factors of eHealth Tools Adoption Among Medicaid and Non-Medicaid Populations From the Health Information National Trends Survey (HINTS) 2017-2019: Questionnaire Study
dc.contributor.author | Yang, Xin | |
dc.contributor.author | Yang, Ning | |
dc.contributor.author | Lewis, Dwight | |
dc.contributor.author | Parton, Jason | |
dc.contributor.author | Hudnall, Matthew | |
dc.contributor.other | University of Alabama Tuscaloosa | |
dc.date.accessioned | 2023-09-28T19:08:47Z | |
dc.date.available | 2023-09-28T19:08:47Z | |
dc.date.issued | 2021 | |
dc.description.abstract | Background: Evidence suggests that eHealth tools adoption is associated with better health outcomes among various populations. The patterns and factors influencing eHealth adoption among the US Medicaid population remain obscure. Objective: The objective of this study is to explore patterns of eHealth tools adoption among the Medicaid population and examine factors associated with eHealth adoption. Methods: Data from the Health Information National Trends Survey from 2017 to 2019 were used to estimate the patterns of eHealth tools adoption among Medicaid and non-Medicaid populations. The effects of Medicaid insurance status and other influencing factors were assessed with logistic regression models. Results: Compared with the non-Medicaid population, the Medicaid beneficiaries had significantly lower eHealth tools adoption rates for health information management (11.2% to 17.5% less) and mobile health for self-regulation (0.8% to 9.7% less). Conversely, the Medicaid population had significantly higher adoption rates for using social media for health information than their counterpart (8% higher in 2018, P=.01; 10.1% higher in 2019, P=.01). Internet access diversity, education, and cardiovascular diseases were positively associated with health information management and mobile health for self-regulation among the Medicaid population. Internet access diversity is the only factor significantly associated with social media adoption for acquisition of health information (OR 1.98, 95% CI 1.26-3.11). Conclusions: Our results suggest digital disparities in eHealth tools adoption between the Medicaid and non-Medicaid populations. Future research should investigate behavioral correlates and develop interventions to improve eHealth adoption and use among underserved communities. | en_US |
dc.format.medium | electronic | |
dc.format.mimetype | application/pdf | |
dc.identifier.citation | Yang, X., Yang, N., Lewis, D., Parton, J., & Hudnall, M. (2021). Patterns and Influencing Factors of eHealth Tools Adoption Among Medicaid and Non-Medicaid Populations From the Health Information National Trends Survey (HINTS) 2017-2019: Questionnaire Study. In Journal of Medical Internet Research (Vol. 23, Issue 2, p. e25809). JMIR Publications Inc. https://doi.org/10.2196/25809 | |
dc.identifier.doi | 10.2196/25809 | |
dc.identifier.orcid | https://orcid.org/0000-0003-3063-2458 | |
dc.identifier.orcid | https://orcid.org/0000-0003-2018-6693 | |
dc.identifier.uri | https://ir.ua.edu/handle/123456789/10935 | |
dc.language | English | |
dc.language.iso | en_US | |
dc.publisher | JMIR Publications | |
dc.rights.license | Attribution 4.0 International (CC BY 4.0) | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Medicaid program | |
dc.subject | eHealth | |
dc.subject | internet access | |
dc.subject | digital divide | |
dc.subject | health information technology | |
dc.subject | COMMUNICATION | |
dc.subject | ENGAGEMENT | |
dc.subject | ADHERENCE | |
dc.subject | ACCESS | |
dc.subject | INCOME | |
dc.subject | Health Care Sciences & Services | |
dc.subject | Medical Informatics | |
dc.title | Patterns and Influencing Factors of eHealth Tools Adoption Among Medicaid and Non-Medicaid Populations From the Health Information National Trends Survey (HINTS) 2017-2019: Questionnaire Study | en_US |
dc.type | Article | |
dc.type | text |
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