PurposeTo further develop the Ekblom Bak-test prediction equation for estimation of VO2max from submaximal cycle ergometry.MethodsThe model group (117 men and 100 women, aged 48.3 ± 15.7 and 46.1 ± 16.8 years, VO2max 46.6 ± 11.1 and 40.4 ± 9.6 mL kg−1 min−1, respectively) and the cross-validation group (60 men and 55 women, aged 40.6 ± 17.1 and 41.6 ± 16.7 years, VO2max 49.0 ± 12.1 and 43.2 ± 8.9 mL min−1 kg−1, respectively) performed 4 min of cycling on a standard work rate (30 W) directly followed by 4 min on a higher work rate. Heart rate (HR) at each work rate was recorded. Thereafter, participants completed a graded maximal treadmill test for direct measurement of oxygen uptake. The new prediction equation was cross-validated and accuracy compared with the original Ekblom Bak equation as well as by the Åstrand test method.ResultsThe final sex-specific regression models included age, change in HR per-unit change in power (ΔHR/ΔPO), the difference in work rates (ΔPO), and HR at standard work rate as independent variables. The adjusted R2 for the final models were 0.86 in men and 0.83 in women. The coefficient of variation (CV) was 8.7 % and SEE 0.28 L min−1. The corresponding CV and SEE values for the EB-test2012 and the Åstrand tests were 10.9 and 18.1 % and 0.35 and 0.48 L min−1, respectively.ConclusionThe new EB-test prediction equation provides an easy administered and valid estimation of VO2max for a wide variety of ages (20–86 years) and fitness levels (19–76 mL kg−1 min−1).

Maximal oxygen uptake (VO2max) is an important, independent predictor of cardiovascular health and mortality. Despite this, it is rarely measured in clinical practice. The aim of this study was to create and evaluate a submaximal cycle ergometry test based on change in heart rate (HR) between a lower standard work rate and an individually chosen higher work rate. In a mixed population (n = 143) with regard to sex (55% women), age (21-65 years), and activity status (inactive to highly active), a model included change in HR per unit change in power, sex, and age for the best estimate of VO2max. The association between estimated and observed VO2max for the mixed sample was r = 0.91, standard error of estimate = 0.302 L/min, and mean measured VO2max = 3.23 L/min. The corresponding coefficient of variation was 9.3%, a significantly improved precision compared with one of the most commonly used submaximal exercise tests, the Åstrand test, which in the present study was estimated to be 18.1%. Test-retest reliability analysis over 1 week revealed no mean difference in the estimated VO2max (-0.02 L/min, 95% confidence interval: -0.07-0.03). The new test is low-risk, easily administered, and valid for a wide capacity range, and is therefore suitable in situations as health evaluations in the general population.

BackgroundPrevious studies on the relation between lifestyle and the metabolic syndrome lack one or several aspects of the physical activity pattern in the analyses or cardiorespiratory fitness. Likewise, both uni- and triaxial accelerometry have been used, though, the predictive validity of these two modes has not been compared.ObjectivesThe aims of the present study were firstly to investigate the independent relation between cardiorespiratory fitness and physical activity pattern to the metabolic syndrome (MetS) and secondly to examine the predictive validity of uni- and triaxial accelerometry, respectively.MethodsData was extracted from the SCAPIS pilot study (n=930, mean age 57.7 yrs). Physical activity pattern was assessed by accelerometry. Cardiorespiratory fitness was estimated using cycle ergometry. MetS was defined per the Adult Treatment Panel III from the National Cholesterol Education Program definition.ResultsTime spent sedentary (OR: 2.38, 95% CI: 1.54-4.24 for T3 vs T1), in light intensity (OR: 0.50, 95% CI: 0.28-0.90) and in moderate-to-vigorous activity (OR: 0.33, 95% CI: 0.18-0.61), as well as cardiorespiratory fitness (OR: 0.24, 95% CI:0.12-0.48), were all independently related to the prevalence of MetS after adjustment for potential confounders, fitness and/or the other aspects of the physical activity pattern. In addition, we found that triaxial analyses were more discriminant, with ORs farther away from the reference group and additional significant ORs.ConclusionThe finding that several aspects of the physical activity pattern reveal independent relations to the MetS makes new possible targets for behaviour change of interest, focusing on both exercise and everyday life. When assessing the risk status of a patient, it is advised that triaxial accelerometry is used.