JMIR Research Protocols (Jun 2023)

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

  • Masanobu Hibi,
  • Shun Katada,
  • Aya Kawakami,
  • 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,
  • Yuuki 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,
  • Tamaki 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

DOI
https://doi.org/10.2196/47024
Journal volume & issue
Vol. 12
p. e47024

Abstract

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BackgroundHuman health status can be measured on the basis of many different parameters. Statistical relationships among these different health parameters will enable several possible health care applications and an approximation of the current health status of individuals, which will allow for more personalized and preventive health care by informing the potential risks and developing personalized interventions. Furthermore, a better understanding of the modifiable risk factors related to lifestyle, diet, and physical activity will facilitate the design of optimal treatment approaches for individuals. ObjectiveThis study aims to provide a high-dimensional, cross-sectional data set of comprehensive health care information to construct a combined statistical model as a single joint probability distribution and enable further studies on individual relationships among the multidimensional data obtained. MethodsIn this cross-sectional observational study, data were collected from a population of 1000 adult men and women (aged ≥20 years) matching the age ratio of the typical adult Japanese population. Data include biochemical and metabolic profiles from blood, urine, saliva, and oral glucose tolerance tests; bacterial profiles from feces, facial skin, scalp skin, and saliva; messenger RNA, proteome, and metabolite analyses of facial and scalp skin surface lipids; lifestyle surveys and questionnaires; physical, motor, cognitive, and vascular function analyses; alopecia analysis; and comprehensive analyses of body odor components. Statistical analyses will be performed in 2 modes: one to train a joint probability distribution by combining a commercially available health care data set containing large amounts of relatively low-dimensional data with the cross-sectional data set described in this paper and another to individually investigate the relationships among the variables obtained in this study. ResultsRecruitment for this study started in October 2021 and ended in February 2022, with a total of 997 participants enrolled. The collected data will be used to build a joint probability distribution called a Virtual Human Generative Model. Both the model and the collected data are expected to provide information on the relationships between various health statuses. ConclusionsAs different degrees of health status correlations are expected to differentially affect individual health status, this study will contribute to the development of empirically justified interventions based on the population. International Registered Report Identifier (IRRID)DERR1-10.2196/47024