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.

Background: Patients’ withholding information from doctors can undermine medical treatment, create barriers for appropriate diagnoses, and increase systemic cost in health care systems. To date, there is limited literature detailing the association between trends of patients withholding information behavior (WIB) and the patient-physician relationship (PPR).Objective: The aim of this study was to explore the prevalence trend of WIB after 2011 and examine the effects of PPR on WIB and its time trend.Methods: A total of 5 iterations of data from the Health Information National Trends Survey (years: 2011-2018; n=11,954) were used to explore curvilinear trends of WIB among the US population. Multiple logistic regression models were used to examine curvilinear time trends of WIB, effects of PPR on WIB, and moderation effects of PPR on the WIB time trend.Results: The WIB prevalence has an increasing trend before 2014, which has the highest rate of 13.57%, and then it decreases after 2014 to 8.65%. The trend of WIB is curvilinear as the quadratic term in logistic regression model was statistically significant (P=.04; beta=−.022; SE=0.011; odds ratio [OR] 0.978, 95% CI 0.957-0.999). PPR is reversely associated with WIB (P<.001; beta=−.462; SE=0.097; OR 0.630, 95% CI 0.518-0.766) and has a significant moderation effect on time trends (P=.02; beta=−.06; SE=0.025; OR 0.941, 95% CI 0.896-0.989). In general, poor quality of PPR not only significantly increased the WIB probability but also postponed the change of point for WIB curvilinear trend.Conclusions: Findings suggest that the time trend of WIB between 2011 and 2018 is curvilinear and moderated by the quality of the PPR. Given these results, providers may reduce WIB by improving PPR. More research is needed to confirm these findings.

Understanding how patients engage with digital health technologies is critical for improving the reach and equity of telehealth services. While prior research has largely focused on demographic predictors of telehealth use, this study applies a hybrid data mining approach to uncover behavioral engagement patterns and evaluate their predictive power. Using data from the 2022 U.S. Health Information Trends Survey (HINTS; N = 3525), we identified four distinct eHealth engagement typologies through Latent Class Analysis (LCA): (1) Highly Digital Engagers, (2) Moderate Digital Users, (3) Social Media and App Enthusiasts, and (4) Wearable and Health App Enthusiasts. We then modeled telehealth utilization as the outcome using multivariable logistic regression and eXtreme Gradient Boosting (XGBoost) with Shapley Additive Explanations (SHAP). Compared to Highly Digital Engagers, Moderate Digital Users had significantly lower odds of telehealth use (OR = 0.52), while the other two classes had higher odds. SHAP analyses confirmed that depression status and geographic region interacted with engagement profiles to shape telehealth access, with a notably negative effect of depression within Class 2. These findings demonstrate the value of integrating behavioral segmentation with interpretable machine learning to characterize heterogeneity in digital health engagement and its association with telehealth utilization. Our study offers a scalable, population-level analytic framework that can inform targeted telehealth planning and outreach strategies aligned with real-world patterns of digital engagement.

To prepare for the next generation of digital health policies, we must understand how diverse populations build trust and find meaning in engaging with digitally mediated public health systems. Clinical trials (CTs) remain essential to health innovation, yet participation is persistently low. While digital platforms are increasingly used to expand outreach, disparities in institutional trust and digital access threaten equitable inclusion.To examine how institutional trust, digital engagement, and sociodemographic factors shape willingness to participate in clinical trials, as a case of digitally mediated health system engagement.This study used nationally representative survey data from the United States (N = 3,437). Exploratory and confirmatory factor analyses identified latent motivational pathways. Multivariate regression models examined associations between financial- and medical-based willingness to participate.Two distinct motivational profiles emerged: medical-based willingness (MBW) and financial-based willingness (FBW). MBW was positively associated with digital health-seeking behavior, clinical trial knowledge, and perceived care quality, which are the indicators of institutional trust and system engagement. FBW was associated with younger age, lower education, and economic vulnerability. Racial disparities in MBW were observed, with non-White respondents significantly less likely to report medically motivated participation.Equitable clinical trial participation depends on addressing both digital inclusion and institutional trust for the next generation. Policymakers should develop outreach strategies that tailor messaging by motivational profile and embed culturally responsive trust signals into digital platforms. These strategies can help foster a sense of meaning, enhance connection, and promote well-being among underrepresented populations which are key goals for next-generation health policy.

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.