Factors Associated with Cardiorespiratory Fitness in a Swiss Working Population.

Sara Kind,Stefanie Brighenti-Zogg,David Miedinger,Ulla Schüpbach,Jörg D Leuppi,Thomas Dieterle,Jonas Mundwiler

doi:10.1155/2019/5317961

Abstract

Background Good cardiorespiratory fitness (high VO2max) has beneficial effects on morbidity and mortality. Therefore, a tool to estimate VO2max 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 with VO2max. 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 individual VO2max. 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 of VO2max. 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 R2 = 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 with VO2max, while BMI, SBP, and DBP did not contribute to the regression model. Conclusions This study introduced a simple model to evaluate VO2max based on nonexercise parameters as part of daily clinical routine without needing a time-consuming, cost-intense, and physically demanding direct assessment of VO2max. Knowledge about VO2max may help identifying individuals at increased cardiovascular risk and may provide the basis for health counselling and tailoring preventive measures.

Highlights

Physical inactivity is rising on a global scale, yielding dramatic consequences for the general health of the population and representing a huge burden to the healthcare systems [1]
In order to identify the most important factors associated with VO2max, a forward-stepwise multiple linear regression analysis was performed including age, sex, smoking status, metabolic parameters (BMI, waist circumference (WC)), vital signs (HR, SBP, diastolic blood pressure (DBP), double product), and physical activity level (MVPA) as independent variables based on previous research
Multiple linear regression analysis identified sex, WC, difference of maximal to resting Heart rate (HR), smoking, and age as the most important factors associated with cardiorespiratory fitness (VO2max), while SBP and DBP did not correlate with VO2max

Summary

Introduction

Physical inactivity is rising on a global scale, yielding dramatic consequences for the general health of the population and representing a huge burden to the healthcare systems [1]. Previous studies indicated that cardiorespiratory fitness measured as maximal oxygen uptake (VO2max) is more closely correlated with CV risk factors than physical activity [2]. 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 with VO2max. The regression model (adjusted R2 = 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 with VO2max, while BMI, SBP, and DBP did not contribute to the regression model. Knowledge about VO2max may help identifying individuals at increased cardiovascular risk and may provide the basis for health counselling and tailoring preventive measures

Objectives

Methods

Conclusion