@Article{info:doi/10.2196/25809,
author="Yang, Xin
and Yang, Ning
and Lewis, Dwight
and Parton, Jason
and Hudnall, Matthew",
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",
journal="J Med Internet Res",
year="2021",
month="Feb",
day="18",
volume="23",
number="2",
pages="e25809",
keywords="Medicaid program; eHealth; internet access; digital divide; health information technology",
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. ",
issn="1438-8871",
doi="10.2196/25809",
url="http://www.jmir.org/2021/2/e25809/",
url="https://doi.org/10.2196/25809",
url="http://www.ncbi.nlm.nih.gov/pubmed/33599619"
}