Prediction of Cardiorespiratory Fitness Level of Young Healthy Women Using Non-Exercise Variables

Author:

Zadarko Emilian1ORCID,Przednowek Karolina H.1ORCID,Barabasz Zbigniew2ORCID,Zadarko-Domaradzka Maria1ORCID,Nizioł-Babiarz Edyta2ORCID,Hulewicz Tomasz1,Niewczas-Czarna Klaudia1,Huzarski Maciej1,Iskra Janusz3,Gouveia Élvio Rúbio4ORCID,Przednowek Krzysztof1ORCID

Affiliation:

1. Institute of Physical Culture Sciences, Medical College of Rzeszow University, 35-959 Rzeszów, Poland

2. The Institute of Health and Economy, State University of Applied Sciences in Krosno, 38-400 Krosno, Poland

3. Faculty of Physical Education and Physiotherapy, Opole University of Technology, 45-758 Opole, Poland

4. Department of Physical Education and Sport, University of Madeira, 9020-105 Funchal, Portugal

Abstract

Cardiorespiratory fitness (CRF) is considered an important indicator of health in children and adults. The main contribution of this paper is an analysis of cardiorespiratory fitness predictive models among a population of healthy and young women, using the non-exercise variables. The study was conducted on a group of 154 healthy women (aged 20.3 ± 1.2) from selected academic centers in Poland. The VO2max was measured using a Cosmed K4b2 portable analyzer during a 20 m shuttle test. In addition, selected anthropomotor parameters including body composition components were measured for each subject. The participants’ leisure-time physical activity was assessed using the Minnesota Leisure-Time Physical Activity Questionnaire. The Ridge regression was the most accurate model for estimating VO2max from anthropometric parameters. The most accurate model based on the level of leisure-time physical activity was calculated using stepwise regression for which the prediction error was at the level of 6.68 (mL·kg−1·min−1). The best model calculated from all non-exercise variables (age, anthropometric parameters, and leisure-time physical activity) had only two predictors: waist circumference and total physical activity, and had a prediction error equal to 6.20 (mL·kg−1·min−1).

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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