The burgeoning integration of wearable devices into healthcare promises to revolutionize precision medicine, yet their “black-box” algorithms pose a significant epistemological challenge, hindering independent validation and scientific reproducibility. This paper proposes a comprehensive framework to assess algorithmic transparency in wearable health technology, grounded in an exploratory, nine-participant “Bring Your Own Device” (BYOD) case study conducted under free-living conditions. This approach facilitated observation of real-world data generation across diverse user contexts. Our methodology involved a comparative analysis of data accessibility from consumer wearables, specifically Apple Watch data, complemented by observations from other leading devices (e.g., Fitbit, Garmin, Polar) for common metrics like heart rate, heart rate variability, and actimetry. We performed a structured evaluation of publicly reported algorithmic details. Through this investigation, we concretely identified critical gaps in algorithmic transparency: for instance, a consistent lack of detailed information on motion artifact compensation for heart rate, proprietary filtering techniques for heart rate variability, and undisclosed machine learning models for sleep stage classification. We demonstrate how these black-box practices directly affect the reliability and interpretability of biometric data, impeding personalized health interventions and reproducible research outcomes, particularly for user-generated health data. The proposed framework aims to guide future research towards more transparent and robust wearable technologies, fostering collaboration aligned with precision medicine's ethical and epistemic demands, and supporting the opportunity for person-generated health data while addressing the critical need for better reporting guidelines.