Predicting VO_2maxin College-Aged Participants Using Cycle Ergometry and Perceived Functional Ability

Abstract

The purpose of this study was to develop a multiple linear regression model to predict treadmill VO 2max scores using both exercise and non-exercise data. One hundred five college-aged participants (53 male, 52 female) successfully completed a submaximal cycle ergometer test and a maximal graded exercise test on a motorized treadmill. The submaximal cycle protocol required participants to achieve a steady-state heart rate equal to at least 70% of age-predicted maximum heart rate (220-age), while the maximal treadmill graded exercise test required participants to exercise to volitional fatigue. Relevant submaximal cycle ergometer test data included a mean (±SD) ending steady-state heart rate and ending workrate equal to 164.2 ± 13.0 bpm and 115.3 ± 27.0 watts, respectively. Relevant non-exercise data included a mean (±SD) body mass (kg), perceived functional ability score, and physical activity rating score of 74.2 ± 15.1, 15.7 ± 4.3, and 4.7 ± 2.1, respectively. Multiple linear regression was used to generate the following prediction of (R = .91, standard error of estimates (SEE) = 3.36 ml·kg −1 ·min −1 ): VO 2max = 54.513 + 9.752 (gender, 1 = male, 0 = female) -.297 (body mass, kg) + .739 (perceived functional ability, 2-26) + .077 (work rate, watts) -.072 (steady-state heart rate). Each predictor variable was statistically significant (p < .05) with beta weights for gender, body mass, perceived functional ability, exercise workrate, and steady-state heart rate equal to . 594, -.544, .388, .305, and -.116, respectively. The predicted residual sums of squares (PRESS) statistics reflected minimal shrinkage (R PRESS = .90, SEE PRESS = 3.56 ml·kg −1 ·min −1 ) for the multiple linear regression model. In summary, the submaximal cycle ergometer protocol and accompanying prediction model yield relatively accurate VO 2max estimates in healthy college-aged participants using both exercise and non-exercise data.

Estimating VO2max Using a Personalized Step Test

“…Although age is generally inversely related to CRF, because this study involved a relatively homogenous sample of college students (18-29 years of age), age was not statistically significant in the prediction of CRF. Consistent with other research Nielson, et al, 2010) the results of this study found gender to be a significant predictor of CRF. The male participants (52.1 ± 6.5 mLkg -1 min -1 ) in this study had average VO 2 max values that were 19.5% greater than that of their female (43.6 ± 6.4 mLkg -1 min -1 ) counterparts.…”

“…The relationship between self-reported physical activity and CRF has previously been reported (Siconolfi, Lasater, Snow & Carleton, 1985) and regression models that include only non-exercise data (e.g., age, gender, body weight, body mass index, and self-reported physical activity) appeared in the 1990's Ainsworth, Richardson, Jacobs, & Leon,1992;Heil, Freedson, Ahlquist, Price, & Rippe, 1995;. More recent studies have combined exercise data and non-exercise data to predict CRF Nielson, George, Vehrs, Hager, & Webb, 2010). No other studies have combined exercise test data and self-reported physical activity or fitness levels to predict CRF from a step test.…”

“…The rank order of the beta weights of the independent variables included in the regression equation to predict CRF in this study indicate that there is a greater contribution from the PFA score (0.315) than body mass (kg; -0.256), final step rate (steps/min; 0.248), and recovery HR (bpm; -0.238). This concurs with previous studies that ranked the beta weight of PFA higher than all other variables (Bradshaw et.al., 2005), third after gender and body mass (Nielson et al, 2010), and fourth after treadmill speed, age, and gender . When the PFA variable was removed from the full model, the R value decreased from 0.91 to 0.87, the variance explained by the regression model decreased by 8.6% (from 82.8% to 75.7%), and the SEE increased by 11.9% (from 3.43 mLkg -1 min -1 to 3.84 mLkg -1 min -1 ).…”

Comparison of Cardio-respiratory Fitness and Anthropometric Characteristics between Teenage Swimmers and Non-swimmers

“…This requires minimal space and is easy to transport. Cycle ergometer exercise is a no weight-bearing activity that is usually well tolerated by individuals with orthopedic or other physical limitations and the heart rate (HR), blood pressure, and electrocardiographic data are also easily collected during the test protocol [17].…”

Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances

“…Nielson et al 32 built a MLR model for predicting treadmill VO 2 max scores using both exercise and nonexercise variables. For the study, 105 college-aged participants (53 males and 52 females) successfully accomplished a submaximal cycle ergometer test and a maximal GXT on a motorized treadmill.…”