Deceptive self-reporting in diabetes lifestyle management (DLM) systems limits their ability to offer meaningful and accurate support. Deception can function as a self-protective mechanism, driven by factors such as low self-esteem or the desire to protect self-image. This research builds on CHIP, a chatbot-based DLM prototype, to explore whether the language framing of its responses can influence the psychological determinants of deception. Two framing strategies, empathic and affirming, were implemented and evaluated through a pilot user study, which offers insights for refining the intervention and experimental design in future research.

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.