Assessment of Multidimensional Health Care Parameters Among Adults in Japan for Developing a Virtual Human Generative Model: Protocol for a Cross-Sectional Study

    March 2023
    Masanobu Hibi, Shun Katada, Aya Tanaka, Kotatsu Bito, Mayumi Ohtsuka, Kei Sugitani, Adeline Muliandi, Nami Yamanaka, Takahiro Hasumura, Yasutoshi Ando, Takashi Fushimi, Teruhisa Fujimatsu, Tomoki Akatsu, Sawako Kawano, Ren Kimura, Shigeki Tsuchiya, Y. Yamamoto, Mai Haneoka, Ken Kushida, Tomoki Hideshima, Eri Shimizu, Jumpei Suzuki, Aya Kirino, Hisashi Tsujimura, Shun Nakamura, Takashi Sakamoto, Yuki Tazoe, Masayuki Yabuki, Shinobu Nagase, Tomohisa Hirano, Reiko Fukuda, Yukari Yamashiro, Yoshinao Nagashima, Nobutoshi Ojima, Motoki Sudo, Naoki Oya, Yoshihiko Minegishi, Koichi Misawa, Nontawat Charoenphakdee, Zhengyan Gao, Kohei Hayashi, Kenta Oono, Yohei Sugawara, Shoichiro Yamaguchi, Takahiro Ono, Hiroshi Maruyama
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    TLDR The study aims to create a model to improve personalized and preventive health care.
    This study aims to create a comprehensive health care data set from 997 adult participants in Japan to develop a Virtual Human Generative Model. By collecting and analyzing a wide range of health parameters, including biochemical, metabolic, bacterial, and lifestyle data, the study seeks to understand the relationships among these variables. The goal is to enable personalized and preventive health care interventions by identifying modifiable risk factors and informing potential health risks. The data and model are expected to reveal how different health statuses are interrelated, ultimately contributing to more effective, empirically-based health interventions.
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