Abstract
19 Numerous submaximal exercise tests are available to address the needs of a variety of different situations. However, test designs do not permit computerized automation and are limited by a small number of data points for prediction. The purpose of this study was to create a fully automated cardiorespiratory fitness test. Forty-two subjects (age: 37.8 ± 10.8, % body fat: 22.7 ± 8.2) completed a maximal treadmill test to volitional fatigue and two submaximal ramp tests to 80% of age predicted maximal heart rate (80%PMHR) on an electrically braked cycle ergometer on separate days. Tests were ordered and randomly assigned to prevent test order bias. HR, watts and expired gases were assessed each breath. Paired t-tests revealed no significant differences between the two submaximal cycle ergometer tests (P > 0.05). Maximal watts predicted from HR regressed on watts from bike test 1 and calculated VO2max from maximal watts each correlated significantly with actual VO2max (r = 0.95, P < 0.05). Predictions of VO2max from multiple linear regression including watts at 80%PMHR, body weight and % body fat explained 92% (P<0.05) of the variation in VO2max. When BMI replaced % body fat and age was included in the model, 90% (P< 0.05) of the variance in VO2max was explained. These analyses reveal that HR regressed on watts during a ramp test can effectively predict VO2max, and that a model including demographic information and the workrate that elicits 80%HR can accurately predict VO2max. The simple test design (HR vs. watts) allows test automation while maintaining prediction accuracy. Supported by a grant from the U.S. Airforce AF-F4162297M5165, MedicalGraphics.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call