Enhancing Ai-driven personalized exercise and nutrition planning for elderly through prompt refinement and expert consensus / Nur Athirah Rosli ... [et al.].
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 accur...
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| Main Authors: | , , , , , , |
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| Format: | Book Section |
| Language: | en |
| Published: |
Universiti Teknologi MARA, Negeri Sembilan
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/116141/1/116141.pdf https://ir.uitm.edu.my/id/eprint/116141/ |
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| Summary: | 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. |
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