Treffer: Qualitative and quantitative analysis of the dietary approaches to stop hypertension diet for personalized hypertension management.

• A novel PHSM integrates AHP, FMCGP, and NLMSGP for hypertension management. • First application of NLMSGP to solve non-linear multi-objective decision problems. • Tailored dietary and lifestyle recommendations based on the DASH diet for hypertensive care. • PHSM enhances healthcare efficiency and reduces costs through advanced methodologies. Hypertension is a major global health concern, yet personalized planning for diet and lifestyle remains a challenge in clinical practice. To address this issue, this study proposes a novel Personal Health Support Model (PHSM) based on the principle of the dietary approaches to stop hypertension (DASH) diet, aiming to improve the precision and adaptability of personalized recommendations. It integrates the analytic hierarchy process (AHP), fuzzy multi-choice goal programming (FMCGP), and nonlinear multi-segment goal programming (NLMSGP) to construct a comprehensive multi-criteria decision-support framework that considers individual characteristics, dietary preferences, and clinical guidelines. The main contributions of this study are as follows: 1. PHSM calculates daily calorie and nutrient requirements based on gender, age, and activity level to ensure dietary plans align with individual needs. 2. PHSM provides a personalized exercise plans tailored to individual health conditions and lifestyle preferences to support blood pressure management and overall well-being. 3. Utilizing FMCGP techniques to accommodate diverse dietary and lifestyle preferences, offering precise personalized blood pressure management solutions. 4. Introducing the NLMSGP approach addresses non-scalar coefficient issues, optimizing dietary plans by considering the timing and quantify of food intake. The study results indicate that PHSM significantly outperforms conventional dietitian-led approaches in term of user satisfaction, engagement, and perceived effectiveness. Its structured design and real-time optimization features are also associated with stronger behavioral intention and adherence. In conclusion, this PHSM shows strong potential for clinical applications and can be integrated with AI-driven health platforms, contributing to both the practical advancement of intelligent dietary planning and the theoretical development of multi-objective health decision-making. [ABSTRACT FROM AUTHOR]

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