Factors Associated with Cardiorespiratory Fitness in a Swiss Working Population. (2nd July 2019)

• Record Type: Journal Article
• Authors: Kind, Sara; Brighenti-Zogg, Stefanie; Mundwiler, Jonas; Schüpbach, Ulla; Leuppi, Jörg D.; Miedinger, David; Dieterle, Thomas
• Other Names: Willems Mark Academic Editor.
• Is Part Of: Journal of sports medicine. Volume 2019(2019)
• Publication Date: 2019-07-02
• Subjects: Sports medicine -- Periodicals; 617.102705
• DOI: 10.1155/2019/5317961
• Languages: English
• ISSNs: 2356-7651

Abstract

Abstract : Background . Good cardiorespiratory fitness (highV O 2 m a x ) has beneficial effects on morbidity and mortality. Therefore, a tool to estimateV O 2 m a x in daily clinical practice is of great value for preventing chronic diseases in healthy adults. This study aimed at exploring the cardiometabolic profile in a representative Swiss working population. Based on these insights, a regression model was derived revealing factors associated withV O 2 m a x . Methods . Cross-sectional data of 337 healthy and full-time employed adults recruited in the Basel region, Switzerland, were collected. Anthropometric measurements to compute body mass index (BMI) and waist circumference (WC) were performed. A 20-meter shuttle run test was conducted to determine individualV O 2 m a x . Heart rate (HR) was measured at rest, during maximal exertion, and two minutes after exercise. Systolic (SBP) and diastolic blood pressure (DBP) were assessed at rest and after exercise. A multiple linear regression model was built to identify a set of nonexercise predictor variables ofV O 2 m a x . Results . Complete data of 303 individuals (63% male) aged 18 to 61 years (mean 33 ± 12 years) were considered for analysis. The regression model (adjusted R 2 = 0.647, SE = 5.3) identified sex ( β = -0.699, p < 0.001), WC ( β = -0.403, p < 0.001), difference of maximal to resting HR ( β = 0.234, p < 0.001), smoking ( β = -0.171, p < 0.001), and age ( β = -0.131, p < 0.01) as the most important factors associated withV O 2 m a x, while BMI, SBP, and DBP did not contribute to the regression model. Conclusions . This study introduced a simple model to evaluateV O 2 m a x based on nonexercise parameters as part of daily clinical routine without needing a time-consuming, cost-intense, and physically demanding direct assessment ofV O 2 m a x . Knowledge aboutV O 2 m a x may help identifying individuals at increased cardiovascular risk and may provide the basis for health counselling and tailoring preventive measures.