Health-related Quality of Life (HRQoL) encompasses physical, psychological, and social well-being and significantly influences healthcare outcomes and policy decisions. Traditional HRQoL measurement tools, such as the SF-36 and EQ-5D questionnaires, face consistent user engagement and interpretation challenges. This paper presents a method to enhance HRQoL interpretability by utilizing clustering algorithms to derive four health indicators: daily mobility, physical activity level, loneliness, and social mobility. These indicators were chosen based on their correlation with physical and psychological QoL domains. By clustering HRQoL data achieved in a longitudinal study with 44 volunteers for six months, we aim to identify patterns and provide insights, addressing the limitations of both traditional and modern QoL inference methods. We also conducted a survey with health and eHealth professionals to evaluate the effectiveness and relevance of these indicators. Our findings indicate that the proposed indicators are highly correlated with HRQoL domains, offering a practical and scalable solution for personalized healthcare. This method does not require continuous human intervention and can evolve with new data, presenting a low-maintenance and cost-effective alternative to rule-based systems.