Frontiers in artificial intelligence · 2026
Digital twin-supported behavioral intention in mothers of young children to prevent childhood obesity: a large language model-based intervention study.
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Nakamura Kenji, Shinoda Genki, Noda Aoi, Ishikuro Mami, Obara Taku, Matsubara Taeka, Ishii Hideki, Onishi Masahiro, Ohyama Yoshiaki
Аннотация
INTRODUCTION: Childhood obesity is a major determinant of lifelong non-communicable disease risk, yet early intervention may modify this trajectory. Digital twins and large language models may provide a scalable means of translating individualized risk prediction into understandable and motivating lifestyle guidance.
METHODS: We developed a precision-preventive intervention integrating a childhood-overweight digital twin with an empathic, supervised-fine-tuned open-source nutrition-guidance large language model. The model was based on Llama 3-70B and fine-tuned using the Standard Tables of Food Composition in Japan 2020 and its 2023 amendment. Response appropriateness was evaluated using 30 nutrition-related questions independently assessed by three registered dietitians. The digital twin adopted a published Tohoku Medical Megabank Birth and Three-Generation cohort model predicting overweight risk at 36-47 months, 6 years, 11 years, and 14 years. A total of 121 women with a child under 14 years of age participated in the digital-twin visualization and a single chat-based test.
RESULTS: The off-the-shelf Llama 3-70B produced a mean of 18 appropriate responses out of 30, whereas the fine-tuned model produced 27 out of 30. The digital-twin visualization and chat consultation were rated highly for clarity, perceived value, behavioral intention, and willingness to continue, with core-item means above 4.9 on a five-point scale. Thematic analysis showed that 112 of 121 participants perceived the recommended lifestyle changes as feasible and achievable gradually.
DISCUSSION: The integrated intervention demonstrated preliminary operational feasibility and acceptability. Individualized future-risk visualization combined with supportive conversational guidance was associated with high self-reported behavioral intention. Controlled and longitudinal studies are required to determine whether these preliminary motivational responses translate into sustained behavior change and improved health outcomes.
METHODS: We developed a precision-preventive intervention integrating a childhood-overweight digital twin with an empathic, supervised-fine-tuned open-source nutrition-guidance large language model. The model was based on Llama 3-70B and fine-tuned using the Standard Tables of Food Composition in Japan 2020 and its 2023 amendment. Response appropriateness was evaluated using 30 nutrition-related questions independently assessed by three registered dietitians. The digital twin adopted a published Tohoku Medical Megabank Birth and Three-Generation cohort model predicting overweight risk at 36-47 months, 6 years, 11 years, and 14 years. A total of 121 women with a child under 14 years of age participated in the digital-twin visualization and a single chat-based test.
RESULTS: The off-the-shelf Llama 3-70B produced a mean of 18 appropriate responses out of 30, whereas the fine-tuned model produced 27 out of 30. The digital-twin visualization and chat consultation were rated highly for clarity, perceived value, behavioral intention, and willingness to continue, with core-item means above 4.9 on a five-point scale. Thematic analysis showed that 112 of 121 participants perceived the recommended lifestyle changes as feasible and achievable gradually.
DISCUSSION: The integrated intervention demonstrated preliminary operational feasibility and acceptability. Individualized future-risk visualization combined with supportive conversational guidance was associated with high self-reported behavioral intention. Controlled and longitudinal studies are required to determine whether these preliminary motivational responses translate into sustained behavior change and improved health outcomes.