Cargando…
Factors Associated with Cardiorespiratory Fitness in a Swiss Working Population
BACKGROUND: Good cardiorespiratory fitness (high VO(2max)) has beneficial effects on morbidity and mortality. Therefore, a tool to estimate VO(2max) in daily clinical practice is of great value for preventing chronic diseases in healthy adults. This study aimed at exploring the cardiometabolic profi...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6633967/ https://www.ncbi.nlm.nih.gov/pubmed/31355290 http://dx.doi.org/10.1155/2019/5317961 |
Sumario: | BACKGROUND: Good cardiorespiratory fitness (high VO(2max)) has beneficial effects on morbidity and mortality. Therefore, a tool to estimate VO(2max) 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 VO(2max). 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 VO(2max). 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 VO(2max). 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 with VO(2max), while BMI, SBP, and DBP did not contribute to the regression model. CONCLUSIONS: This study introduced a simple model to evaluate VO(2max) based on nonexercise parameters as part of daily clinical routine without needing a time-consuming, cost-intense, and physically demanding direct assessment of VO(2max). Knowledge about VO(2max) may help identifying individuals at increased cardiovascular risk and may provide the basis for health counselling and tailoring preventive measures. |
---|