600 meters to VO2max: Predicting Cardiorespiratory Fitness with an Uphill Run - Kübra Stoican; Regina Oeschger (Current Issues in Sport Science)
Introduction Cardiorespiratory fitness (CRF) is used to assess the body's ability to perform daily activities and is a criterion of overall health besides endurance performance. There is an inverse relationship between CRF and heart diseases or mortality due to any potential cause in healthy individuals [1]. Therefore, an estimation of CRF can yield relevant information for healthy people to assess their risk of developing coronary diseases, as well as for athletes to design tailored training programs to optimize their performance. The gold standard for assessing CRF is a direct measurement of maximal oxygen uptake (VO2max) in laboratory settings, which quantifies oxygen consumption mostly during graded maximal exercise tests [2]. In spite of the high accuracy provided by direct measurements, they are time-consuming, costly, and require expertise. At the same time, multiple indirect methods have been developed to estimate VO2max, most of which are field tests. While these tests are more accessible, they often still require technical knowledge or specialized equipment, as most of them are designed primarily for athletes and administered by experts. Therefore, performing these tests and interpreting the results can be challenging for recreational individuals. As technology has been advancing, wearable devices are increasingly used for physical activity monitoring with proper accuracy [3, 4]. This advancement further provided access to data for specialists, enabling the development and comparison of field tests and predictive models. Subsequently, various studies have shown that deep learning models outperform linear regression models in predicting VO2max values [5]. Previous studies also showed that, to accurately predict VO2max in a laboratory or through indirect tests, individuals need to reach their maximal effort rather than performing submaximal exercises [6]. In this study, we present an approach to predict VO2max from data collected during a maximal effort test on a 600-meter uphill course, using both linear regression and machine learning models.