Navigating the Trade-off between Explainability and Privacy in Connected Healthcare with Explainable AI

The integration of Artificial Intelligence (AI) into consumer wearable devices promises to revolutionize preventative medicine, yet the \black box\ nature of these algorithms frequently undermines user trust. While Explainable AI (XAI) is proposed as a solution, its implementation introduces a critical ethical tension: the trade-off between the need for transparent health insights and the privacy risks associated with revealing granular behavioral data. This study employs a sequential exploratory mixed-methods design to investigate this \Privacy-Transparency Paradox\ from a consumer-centric perspective. First, in-depth interviews (n=16) and scenario simulations identified that while XAI significantly enhances trust and clinical compliance, it simultaneously heightens users' sense of surveillance, creating a demand for \honest surveillance\ over ambiguous data practices. Second, a large-scale quantitative survey (n=224) validated these findings, revealing that \Ethical and Accountability Beliefs\ are the strongest predictor of users' intention to continue using the device, surpassing both trust and perceived value. Furthermore, results indicate that XAI serves as a crucial trust-repair mechanism in the event of algorithic failure. However, this effect is moderated by privacy cyni :: n, particularly among younger, experienced users who are more sensitive to data intrusion. This paper contributes a nuanced theoretical framework for ethical XAI in connected healthcare, arguing that future systems must move beyond binary consent models to offer tiered, context-aware explainability that balances the right to know with the right to privacy.