Self-tracking technology for behavior monitoring is prevalent in various aspects of human life. It enables users' activities tracking with data produced “in the wild”, namely capturing real-world physical activity, sleep patterns, and stress levels, among others. Advanced new sensors integrated into commercial self-tracking devices have empowered a new era of sensing data exploration and self-improvement recommendations, aiming to enhance physical and mental well-being. However, the collected data and related inferred knowledge are not always well-explained or well-presented and discourage users' commitment leading to sensing devices' abandonment. To sustain user engagement with self-tracking technology for well-being, this paper introduces a comprehensive framework and respective full-stack web service called “UnStressMe” for the analysis of diverse data modalities tracked in the wild, the prediction of future stress behavior and the production and provision of personalized, model-agnostic explanations and interactive visualizations. We showcase the utility of our framework through a mental health use case, paving the way for explainable, transparent, and human-centric self-tracking technology.