Useful Estimation of Fitness and Fatness for Association with Health Outcomes
| 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 |
Granted (Fiscal Year 2022)
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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 | cardiovascular fitness / electronic health record / health outcomes / Fitness equation / prediabetes / women / prediction / epidemiology / Estimated fitness / accuracy / fit fat index / fitness / 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 Annual Research Achievements |
We validated a new algorithm to estimate cardiorespiratory fitness that is typically measured on a clinical treadmill test using commonly available variables in electronic health records in 42,676 adults. The algorithm from this study was then used in a new study to predict abnormal blood glucose. It was found that the algorithm can predict abnormal blood glucose on par with clinical treadmill testing. The algorithm has advantages, such as standardization across EHR systems, decreasing administration time, and allowing practitioners to counsel patients. Three healthcare systems in America are considering the algorithm for implementation.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
In Total, from the grant, we have published five studies in peer reviewed journals (Impact Factors 4.6~14.6) and presented at five conferences (2 domestic and 3 international).
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| Strategy for Future Research Activity |
A series of new studies evaluating various mortality and morbidity outcomes are being planned for the near future.
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Report
(4 results)
Research Products
(12 results)
