Physical fitness is defined as a physiological state of wellbeing in which one can perform daily activities without strain, or that provides the basis for exercise performance. Health-related physical fitness (HRPF) includes components related to a health condition, such as musculoskeletal and cardiorespiratory fitness (CRF; Liguori and American College of Sports Medicine, 2020).

Health-related physical fitness and physical activity (PA) level are often used together, with physical fitness generally considered a more accurate measurement of PA level than self-reported assessments (Williams, 2001). PA involves body movements caused by skeletal muscle contractions that increase energy consumption beyond the basic level (Meredith and Welk, 2010; Liguori and American College of Sports Medicine, 2020). Systematic research on the association between PA and health conditions began six decades ago, and since then, the scientific literature has confirmed the relationship between these two areas (Liguori and American College of Sports Medicine, 2020). Physical fitness was reported to be similar to PA in terms of its association with morbidity and mortality (Blair and Brodney, 1999; Erikssen, 2001). However, physical fitness predicts health outcomes more strongly than PA (Blair et al., 2001; Williams, 2001; Myers et al., 2004). Previous studies have shown at least a 50% decrease in mortality among individuals with a high physical fitness level compared to those with a low physical fitness level (Myers et al., 2004). In addition to serving as a prognostic and diagnostic health indicator in clinical settings, CRF has been used as an indicator of regular exercise (Lin et al., 2015). Warburton et al. reported that the physiological functions of the human body and HRPF continuously decrease with aging, leading to an increased risk for chronic diseases (Warburton et al., 2006). Among the HRPF components, the CRF index’s maximal oxygen uptake decreases by about 3–6% due to aging (Fleg et al., 2005). High levels of HRPF maintained from adulthood can reduce musculoskeletal, cardiovascular, and metabolic diseases such as osteoporosis, sarcopenia, hypertension, and diabetes (Carnethon et al., 2003; Katzmarzyk et al., 2004; Barry et al., 2014; Kim et al., 2019b). The HRPF is an indirect health indicator of the body, and continuous care is important. Therefore, all of the previous study findings establish the need to include HRPF testing in health condition monitoring systems (Ortega et al., 2008). Furthermore, the World Health Organization suggested that regular physical fitness and PA testing should be examined as a public health priority (World Health Organization [WHO], 2010). To prevent chronic diseases, continuous healthcare is necessary, which requires the evaluation of HRPF. However, measurements of HRPF are often not practical or feasible to perform in daily life. Additionally, laboratory methods can accurately measure physical fitness, but may not be a feasible approach for entire populations owing to cost, time constraints, and the need for qualified technicians and sophisticated devices.

The American College of Sports Medicine suggested that physical health is a measurable result of an individual’s PA and exercise habits, which is why many healthcare providers value the accurate and precise measurement of HRPF (Liguori and American College of Sports Medicine, 2020). Common HRPF tests include the isometric hand grip strength (HGS) test for measuring muscle strength (Bäckman et al., 1995), the sit and reach test for flexibility (Mier, 2011), the sit-up test for abdominal muscular endurance (Chen et al., 2020b), and the graded exercise test for cardiorespiratory endurance (Beltz et al., 2016; Kim et al., 2019b). The association between HRPF and health conditions has been established in several studies (Mendes et al., 2016; Chrismas et al., 2019; Chen et al., 2020a). Recently, technological advances in health care and sports science have provided coaches, athletes, healthcare professionals, and researchers with efficient, reliable, and economical means to record health-related and exercise performance data (Seshadri et al., 2017; Aroganam et al., 2019; Kim et al., 2019a; Ray et al., 2019). The connected gains of novel analytical techniques, portable and reliable devices, and comprehensive software programs suggest that research on health promotion will increase in the future (Loncar-Turukalo et al., 2019). Several predictive equations have been developed to estimate HRPF to increase utility for field-based research (Esco et al., 2008; Shenoy et al., 2012; Lopes et al., 2018; Zaccagni et al., 2020). These previous studies generally linked HRPF parameters to laboratory evaluations. However, there were differences in the equation’s estimation reliability due to sample size, the number of independent variables, differences in measurement methods, and statistical analysis methods.

Therefore, our study aimed to develop a multiple linear regression model to predict HRPF parameters (e.g., HGS, flexibility, muscular endurance, and CRF) using easy-to-measure dependent variables [e.g., gender, age, body mass index (BMI), and percent body fat] in Korean adults.

For each multiple regression model developed, the F-test was used to validate the significance of the model. Multiple regression analyses have shown that the regression coefficients for the selected independent variable were statistically significant. Multiple regression analyses for each model included coefficients of determination (R²), adjusted coefficients of determination (adjusted R²), and SEE. The correlations between the dependent variables and HRPF parameters are shown in Table 2.

Over the years, the components of HRPF have been established in various ways in scientific research (Meredith and Welk, 2010). Previous studies describe HRPF as having a multidimensional structure despite the many different definitions (Meredith and Welk, 2010). Some European studies consider HRPF to include body composition, musculoskeletal fitness, CRF, and skill-related fitness (agility, speed, and coordination) (Artero et al., 2011; Ruiz et al., 2011; Secchi et al., 2014). Other studies consider only body composition, CRF, musculoskeletal fitness, and flexibility (Pillsbury et al., 2013); or body composition, CRF, muscle strength, and flexibility as components of HRPF (Castillo-Garzón et al., 2006). However, the American College of Sports Medicine recommends five factors: body composition, flexibility, muscular strength, muscular endurance, and CRF (Liguori and American College of Sports Medicine, 2020). Therefore, multiple regression analysis using the stepwise technique predicted the HRPF parameters (HGS, flexibility, muscular endurance, and CRF) of the American College of Sports Medicine criteria using dependent variables (e.g., gender, age, body mass index, and percent body fat).

Many researchers have conducted studies to evaluate health conditions and exercise performance using HRPF, while assuming that the HRPF parameter is a reliable healthcare index. For healthcare, the development of tools or equipment that can easily measure and evaluate HRPF in daily life will be useful. Previous studies developed equations with relatively small sample sizes or samples with limited age ranges (Esco et al., 2008; Shenoy et al., 2012; Lopes et al., 2018; Zaccagni et al., 2020). This study aimed to develop a multiple regression model for estimating the HRPF parameters in Korean adults using easy-to-measure dependent variables. Before performing multiple regressions to estimate HRPF parameters, it is essential to eliminate outliers because they increase predictive errors. The absolute value of the studentized residual was used to eliminate outliers in this study. The coefficient of determination of the HRPF parameters in the developed multiple regression models was high, except for flexibility. The mean explanatory power of the sit and reach regression model in our study was 29.8%.

The HGS used to evaluate total muscle strength measures the ability of hand muscles to produce force (tension) using a hand dynamometer (Mitsionis et al., 2009). The relevance of HGS measurements continues to grow due to their clinical and epidemiological application for sarcopenia diagnosis, as suggested by the European Working Group (Cruz-Jentoft et al., 2010), or as a nutrition status indication and their association with morbidity and mortality (Norman et al., 2011). HGS has been studied in relation with various anthropometric factors (Alahmari et al., 2017; Eidson et al., 2017; Lopes et al., 2018; Zaccagni et al., 2020). In the current study, the mean explanatory power of the HGS regression model (37.138 − (10.190 × gender_{male = 1; female = 2}) + (0.988 × BMI) − (0.457 × percent body fat) − (0.042 × age)) was 87.0% (adjusted R²). Alahmari et al. (2017) showed that three variables (i.e., age, hand length, and forearm circumference) predicted 42.7% (adjusted R²) of what constitutes the HGS of healthy adult males (aged: 20–74 years; n = 116) in Saudi Arabia. Furthermore, Zaccagni et al. (2020) reported that the independent variables sex, upper arm muscle area, arm fat index, fat mass, and fat free mass accounted for 74.6% (adjusted R²) of the variance of HGS in young adults (aged: 18–30 years; total: n = 544; male: n = 356; female: n = 188). Lopes et al. (2018) showed that 71% (adjusted R²) of the variability in the dominant HGS could be explained by gender, forearm circumference, and hand length (−15.490 + (10.787 × gender_{male = 1; female = 0}) + (0.558 × forearm circumference) + (1.763 × hand length)). In addition, 70% (adjusted R²) of the variability in the nondominant HGS was explained by gender and hand length (−9.887 + (12.832 × gender_{male = 1; female = 0}) + (2.028 × hand length)) in young adult and middle-aged participants (aged: 20–60 years; total: n = 80; male: n = 40; female: n = 40). Our study confirmed that the regression model formulation developed is more accurate and straightforward than the predictive power of previous studies.

The two most important trunk muscle abilities have been presented as trunk muscle strength and muscular endurance in both the athletic and general populations (Granacher et al., 2013). Trunk muscle strength and muscular endurance testing in clinical fields have been important in injury rehabilitation and prevention programs (Jackson et al., 1998; del Pozo-Cruz et al., 2013). Sit-ups test are known to evaluate strength and muscular endurance in the abdomen (Morrow et al., 2015; Liguori and American College of Sports Medicine, 2020). Esco et al. showed that 63.7% (R²) of the variability in sit-ups could be explained by height, push-ups, skinfolds at the thigh, and skinfolds at the subscapularis (1.651 + (0.368 × push-ups) + (0.495 × height) − (0.277 × skinfolds at the thigh) − (0.336 × skinfolds at the subscapularis)) in healthy adults (aged: 18–48 years; total: n = 100; male: n = 40; female: n = 60) (Esco et al., 2008). The sit-ups regression model’s (62.443 − (1.015 × percent body fat) − (0.392 × age) + (0.783 × BMI) − (5.287 × gender_{male = 1; female = 2})) mean explanatory power estimated in our study was 75.1% (adjusted R²).

Cardiorespiratory fitness is an essential component of health and physical fitness, and is affected by the respiratory, cardiovascular, and skeletal muscle systems (Liguori and American College of Sports Medicine, 2020). The gold standard measurement of CRF is VO_2max when performing a maximum graded exercise test (Liguori and American College of Sports Medicine, 2020). However, while VO_2max is the most accurate way to evaluate CRF, testing requires expensive equipment, space to accommodate equipment, and trained personnel. Previous studies developed a method to predict VO_2max without exercise using multiple regression analysis (Bradshaw et al., 2005; Shenoy et al., 2012). The non-exercise regression equations provide convenient estimates of CRFs without performing maximum or submaximal exercise tests (Bradshaw et al., 2005). Shenoy et al. showed that 79.9% (adjusted R²) of the variability in VO_2max could be explained by gender, perceived functional ability, and body surface area (−1.541 + (1.096 × gender_{male = 1; female = 0}) + (0.081 × perceived functional ability) + (1.084 × body surface area)) in healthy young Indian adults (aged: 18–27 years; total: n = 120; male: n = 60; female: n = 60) (Shenoy et al., 2012). Bradshaw et al. (2005) showed that 87% (R²) of the variability in VO_2max could be explained by gender, age, BMI, perceived functional ability, and PA rating (48.073 + (6.178 × gender_{male = 1; female = 0}) – (0.246 × age) – (0.619 × BMI) + (0.712 × perceived functional ability) + (0.671 × PA rating)) in adults (aged: 18–65 years; total: n = 100; male: n = 50; female: n = 50). In our study, the mean explanatory power of the estimated VO_2max regression model (61.068 − (0.197 × percent body fat) − (5.920 × gender_{male = 1; female = 2}) − (0.133 × age) − (0.305 × BMI)) was 88.5% (adjusted R²). Accordingly, we obtained similar or higher regression coefficient than previous studies by using independent variables that are more accessible to measure, and a larger sample size. Therefore, we consider the results of this study straightforward and accurate.

This study had some limitations. The role of HRPF and nutrition in decreasing the progression of chronic diseases is growing more important (Gil et al., 2015). Nutrition was described as a major modifiable behavior, and HRPF has also been defined as an essential health-related indication (Camões and Lopes, 2008). Previous studies have shown that improvements in HRPF and nutritional factors could prevent functional limitations related to aging, lead to healthier and independent aging processes (Strandberg et al., 2017; Wickramasinghe et al., 2020). However, the association with HRPF parameters could not be evaluated because the NFA database did not provide nutrition information. We only included adults between the ages of 19 and 64 in our analysis. Therefore, the multiple regression equation developed in the present study does not apply to older adults. In the future, a multi-regression equation development study will be necessary to predict the functional physical fitness of older adults.

This study demonstrated that the variability of HGS, muscular endurance, and CRF in healthy adults could be explained by gender, age, BMI, and percent body fat. A multi-regression equation could be developed based on these demographic and anthropometric variables. Since this multi-regression equation requires only a simple parameter measurement, it could be time-efficient, inexpensive, and realistic for large groups in clinical practice. The prediction equation will allow coaches, athletes, healthcare professionals, researchers, and the general public to better estimate the expected HRPF in order to improve the data interpretation.

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

The studies involving human participants were reviewed and approved by Institutional Review Board of Kunkuk University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

S-WK, HJ, JL, and H-YP: conception and study design. S-WK, HJ, and H-YP: statistical analysis. H-YP: investigation. S-WK and H-YP: data interpretation and writing–review and editing. S-WK: writing–original draft preparation. KL: supervision. All authors have read and approved the final manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.