# Prediction of Cardiorespiratory Fitness Level of Young Healthy Women Using Non-Exercise Variables
## 4 Discussion
Thus, it can be concluded that non-exercise models have big potential and can be used for CRF estimation. The analysis showed that in a model based solely on age and anthropometric parameters, the following predictor's age, body fat, total body water, and WHtR indicator were significant. The predictors obtained are not random, and their significance on VO\({}_{2}\)max levels is found in earlier studies [53]. CRF is strongly related to the level of individual components of body composition. Low CRF levels in young adults with high body fat may be a factor in the development of cardiovascular disease in middle age and old age [56]. In addition, an important aspect in assessing the risk of cardiovascular disease, among other things, are indicators of body composition, especially those related to fat distribution such as WC, WHR, and WHtR [57]. For models based on age and leisure-time physical activity rates, MLTPAQ\({}_{H}\) activity proved to be the only significant predictor. A cross-sectional study conducted in a group of healthy men and women over a wide age range showed that the level of leisure-time activity positively shaped peak VO\({}_{2}\). The strongest correlation was observed for high-intensity leisure-time activity [58], which is consistent with the obtained model. Physical activity and CRF play an important role in preventing cardiovascular disease and risk factors [59; 60]. Previous studies have also shown that individual CRF levels depend mainly on the level of physical activity [61]. Other studies have also emphasized the importance of lifelong physical activity in enhancing or maintaining CRF [62; 63; 64]. The model obtained from all analyzed variables (age, anthropometric parameters, and leisure-time physical activity level) has two significant predictors: waist circumference and total physical activity level. The significance of the obtained predictors can be found in other scientific studies. Waist circumference is also a predictor of visceral fat and an indicator of central obesity [65]. [PERSON] et al. research [66] confirm the desirability of maintaining a healthy body weight to achieve greater CRF in a healthy population of men and women. Lifestyle changes to increase physical activity, reduce body weight, and improve eating habits are effective in reducing the risk of metabolic syndrome in young adults [67; 68]. A World Health Organization document shows that, worldwide, one in four adults and three in four adolescents (ages 11-17) do not meet the recommended health-promoting dimension of physical activity [69]. Physical activity is important for a healthy lifestyle, weight control, and fitness development [70; 71]. Studies suggest that increasing physical activity intensity or volume impacts CRF. Even moderate-intensity physical activity at a level of 40-55% VO\({}_{2}\)max is sufficient to improve CRF [72; 73]. A significant decline in CRF is observed after the age of 30, and the rate of decline significantly accelerates with decreasing physical activity and weight gain [74]. In addition, lower leisure-time physical activity increases the risk of metabolic syndrome, especially in women over 40 years of age [37; 38]. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Author** & **Method** & **Criteria** \\ \hline Our study & Ridge–FS & RMSE = 6.20 \\ [PERSON] et al. 2005 [51] & OLS & SE = 3.63 \\ [PERSON] et al. 2009 [17] & SVM & SE = 3.53 \\ [PERSON] et al. 2010 [54] & OLS & SE = 3.56 \\ [PERSON] et al. 2017 [55] & OLS & SSE = 5.14 \\ \hline [PERSON] et al. [53] & RBF & RMSE = 4.07 \\ \hline \hline \end{tabular} RMSE—root mean squared error; Ridge–FS—Ridge regression model with input variables calculated by forward regression; SVM—support vector machine; OLS—ordinary least squares regression; RBF— artificial neural network with radial basis function.  
\end{table} 
Table 5: Error comparison of non-exercise predictive models. The limitations of this research are related to the data used for calculating models.