Estimation of Health-Related Physical Fitness Using Multiple Linear Regression in Korean Adults: National Fitness Award 2015-2019

Continuous health care and the measurement of health-related physical fitness (HRPF) is necessary for prevention against chronic diseases; however, HRPF measurements including laboratory methods may not be practical for large populations owing to constraints such as time, cost, and the requirement for qualified technicians. This study aimed to develop a multiple linear regression model to estimate the HRPF of Korean adults, using easy-to-measure dependent variables, such as gender, age, body mass index, and percent body fat. The National Fitness Award datasets of South Korea were used in this analysis. The participants were aged 19-64 years, including 319,643 male and 147,600 females. HRPF included hand grip strength (HGS), flexibility (sit and reach), muscular endurance (sit-ups), and cardiorespiratory fitness (estimated VO2max). An estimation multiple linear regression model was developed using the stepwise technique. The outlier data in the multiple regression model was identified and removed when the absolute value of the studentized residual was >= 2. In the regression model, the coefficient of determination for HGS (adjusted R-2: 0.870, P < 0.001), muscular endurance (adjusted R-2: 0.751, P < 0.001), and cardiorespiratory fitness (adjusted R-2: 0.885, P < 0.001) were significantly high. However, the coefficient of determination for flexibility was low (adjusted R-2: 0.298, P < 0.001). Our findings suggest that easy-to-measure dependent variables can predict HGS, muscular endurance, and cardiorespiratory fitness in adults. The prediction equation will allow coaches, athletes, healthcare professionals, researchers, and the general public to better estimate the expected HRPF.

Automated summary

This study aimed to develop a regression model to estimate the HRPF of Korean adults, using parameters such as hand grip strength, flexibility, muscular endurance, and cardiorespiratory fitness. The findings suggest that easy-to-measure parameters can predict physical fitness levels in adults.