| Project/Area Number |
19K19437
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 58030:Hygiene and public health-related: excluding laboratory approach
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| Research Institution | Kagoshima University |
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Principal Investigator |
Sloan Robert 鹿児島大学, 医歯学域医学系, 講師 (70765718)
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| Project Period (FY) |
2019-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2022: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2021: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
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| Keywords | fitness / estimated fitness / helath outcomes / diabetes / epidemiology / EHR / public health / primary prevention / cardiovascular fitness / electronic health record / health outcomes / Fitness equation / prediabetes / women / prediction / Estimated fitness / accuracy / fit fat index / fatness / epidemiological / waist / Waist / Fitness / Fatness / Epidemiological / Fit Fat Index |
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| Outline of Research at the Start |
Method. The participant pool totals approximately N=7,000 working Japanese adults (18-64 years old). Regression models will be used to determine the validity of estimated CRF, WHtR and FFI with objective counterpart measures along with the association of health outcomes. Objectives. Determine accurate estimation of CRF, WHtR, and FFI without the need for physical fitness testing. Establish the association of measured and estimated FFI with health outcomes.
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| Outline of Final Research Achievements |
The cost and accessibility of clinical fitness testing can be impractical in practice and research, leading to the need to develop prediction models that generate CRF and WHtR to improve public health practice and research. The primary aim was to establish practical methods to predict eCRF and assess associations with health outcomes. The research methods employed across the five studies primarily involved data collection through comprehensive health examinations. Participants varied from healthy adults and specific subpopulations such as Japanese, Singaporeans, and Americans, with sample sizes ranging from 5293 to 43,356. Key findings include that fitness, not fatness, predicts prediabetes in women, and eCRF is associated with sleep quality in Asians. Five peer-reviewed first-author papers with impact factors ranging from 4.5 to 14.5 were published from the grant. Two international and two domestic conferences were presented.
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| Academic Significance and Societal Importance of the Research Achievements |
This type of eCRF effectively predicts health outcomes and may be used in large-scale epidemiological investigations. It highlights the potential for integrating fitness estimation equations into the population and electronic health records for research primary prevention.
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