%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 23
%N 2
%P e25809
%T 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
%A Yang,Xin
%A Yang,Ning
%A Lewis,Dwight
%A Parton,Jason
%A Hudnall,Matthew
%+ Institute of Data and Analytics, The University of Alabama, 250 Bidgood Hall, Tuscaloosa, AL, 35406, United States, 1 2053483267, xyang15@cba.ua.edu
%K Medicaid program
%K eHealth
%K internet access
%K digital divide
%K health information technology
%D 2021
%7 18.2.2021
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 33599619
%R 10.2196/25809
%U http://www.jmir.org/2021/2/e25809/
%U https://doi.org/10.2196/25809
%U http://www.ncbi.nlm.nih.gov/pubmed/33599619