Abstract
Purpose To develop accurate and practical prediction models of maximal oxygen consumption (VO2max) using the Young Men’s Christian Association (YMCA)-step test in South Korean adults. Methods In total, 568 adults (20–66 years) were included in this study. To develop and cross-validate prediction models of VO2max, the total sample was divided into 80% training and 20% testing using a simple random sampling method. VO2max was measured using the maximal-graded exercise treadmill test. Sex, age, 1-min recovery heart rate, body weight, and height were measured as potential predictors. Each test was conducted within a 2-to 3-day interval, ensuring sufficient rest. Preliminary prediction models were developed from training datasets, which were cross-validated using regression analyses and/or repeated-measures analysis of variance. The accuracy of prediction models was evaluated using R2, standard error of estimate (SEE), and mean difference (MD) against a criterion-measured VO2max. Results The average age and VO2max were 43.5 ± 12.9 years and 39.1 ± 7.5 ml/kg/min, respectively. For model development, three practical models with acceptable accuracy were developed (R2 = 0.56–0.61; SEE = 4.74–5.01). For model crossvalidation, significant relationships between the criterion-measured and predicted VO2max were observed in all three models (R2 = 0.56–0.61; SEE = 4.62–4.88). The difference between criterion-measured and predicted VO2max was not significant in the models (MD =− 0.03 to − 0.14). Conclusions The prediction models included 3–5 variables as significant predictors of VO2max and had acceptable accuracy in a large sample of South Korean adults. The selected models provide a simple and practical method to estimate VO2max using the YMCA-step test for South Korean adults.
| Original language | English |
|---|---|
| Pages (from-to) | 1245-1252 |
| Number of pages | 8 |
| Journal | European Journal of Applied Physiology |
| Volume | 119 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Cross-validation
- Prediction model
- Submaximal exercise tests
- VOmax
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