Lifestyle management systems aim to provide personalized health guidance by interpreting patient's self-reported data. However, these systems often overlook the temporal consistency of behavioral patterns, risking inaccurate or misleading recommendations. To address this, we present an adapted version of a diabetes lifestyle management system that integrates a conflict detection component into its knowledge graph (KG) architecture. This addition enables the system to identify temporal inconsistencies in patient data based on formally defined rules. We developed handcrafted constraints to capture clinically meaningful relationships, such as the expected timing between physical activity and glucose responses. A tunable threshold parameter, $\varepsilon$, is introduced to account for minor fluctuations and variability in activity intensity, enhancing the physiological realism of constraint evaluations. Handcrafted rules were favored over mined constraints due to the limited availability of real-world data, though future work may incorporate mining techniques when larger datasets become accessible. The system was tested on synthetic case studies that simulate typical lifestyle scenarios, demonstrating its potential to flag inconsistencies and improve the reliability of temporal reasoning. These results contribute to the design of more trustworthy, context-aware decision support tools for personalized health management.