Over-the-counter hearing aids (OTC HAs) have the potential to help adults with perceived mild-to-moderate hearing loss across the US, especially in rural communities, where access to hearing healthcare is extremely limited or non-existent. The purpose of this study was to describe an OTC HA clinical trial being conducted in five rural counties of Alabama and to provide preliminary anecdotal data related to the use and care of these hearing aids by the participants. In brief, for this clinical trial, adults with hearing loss were randomly placed in one of three groups where they received varying levels of support for setting, using, and maintaining their OTC HAs. Listening tests and surveys were administered to assess the extent to which they benefitted from the hearing aids as related to word understanding, communication with others in natural settings, and hearing aid use and care. Currently, anecdotal findings suggested that, although some participants required very little support to successfully use their hearing aids, others had difficulty setting and caring for their devices and could have benefitted from individualized guidance. Future quantitative studies will assess the extent of support needed for successful hearing aid benefit and use. Potentially, collaborations among pharmacy and audiology professionals could lead to increased access to hearing healthcare by supporting the use and purchase of OTC HAs in rural pharmacy settings.

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.