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

Studysummary This study aims to use a comprehensive health data set to develop a statistical model that can improve personalized and preventive health care by understanding relationships between various health parameters in individuals.

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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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