Wearable devices have revolutionized personal health management by enabling continuous, real-time tracking of physiological and behavioral metrics. The rapid innovation and development of these technologies is reshaping industries, driving advancements in digital health, enhancing user engagement, and enabling data-driven healthcare solutions. This study examines how varying levels of health motivation and consciousness influence users’ interactions with self-tracked data. It identifies six key dimensions of self-quantification and data interpretation: data awareness, data interpretation & trust, goal setting & health management, intuition vs. data reliance, collaborative & contextual data interpretation, and tolerance for inaccuracy. A survey then clusters participants into four user groups: active goal-setters, casual health observers, reactive health monitors, and proactive health managers. Findings indicate that highly motivated users seamlessly integrate wearable-generated data into their health routines and adapt to minor inaccuracies, whereas those with lower motivation exhibit intermittent usage and skepticism. As wearable technologies continue to play a critical role in modern healthcare, these insights underscore the need for tailored mHealth systems to address diverse user needs—ultimately fostering sustained engagement and healthier behaviors.