Enhancing Ai-driven personalized exercise and nutrition planning for elderly through prompt refinement and expert consensus / Nur Athirah Rosli ... [et al.]. - Rosli, Nur Athirah; Mohd Dan, Azwa Suraya; Sazali, Razif; Md Yusoff, Yusandra; Zulqarnain, Muhammad; Haziq, Amrun; Linoby, Adam (Universiti Teknologi MARA, Negeri Sembilan, 2025)
This study aims to refine the NExGEN Prompt Generator–ChatGPT Framework for personalized exercise and nutrition planning tailored to Malaysia's elderly population using the Fuzzy Delphi Method [1]. Addressing gaps in AI-driven health interventions, the research focuses on enhancing prompt accuracy, scalability, and adaptability to meet elderly-specific health needs effectively. A convenience sample of 18 elderly (>60 yrs old) Malaysians. A purposive sample of 21 experts was recruited to evaluate the NExGEN framework using a custom-designed questionnaire based on personalized nutrition and exercise constructs. The Fuzzy Delphi Method was employed for consensus-building, with responses analyzed using Triangular Fuzzy Numbers and defuzzification techniques to assess expert agreement [2]. Expert feedback, collected through Likert scales and open-ended responses, informed iterative framework improvements.