Accuracy of VO2(max) prediction equations in older adults.
OBJECTIVE
We explored the accuracy and bias of prediction equations (ACSM and Foster) in older, deconditioned men and women. We also examined the predictors of VO2(max) to further understand which variables affect respiratory fitness in the elderly.


METHODS
One hundred seventy-one community dwelling, men (72.6 +/- 4.8 yr) and women (71.0 +/- 5.1 yr) screened in a clinical trial were retrospectively examined. VO2(max) was measured using a standardized protocol with gas exchange measured. Measured VO2 (max) values were compared with prediction equations via mean difference analyses, and bias was explored using Bland-Altman analyses. Regression analysis determined significant predictors of measured VO2 (max). Alpha was P<or= 0.05.


RESULTS
In men and women, Foster, 21.6 +/- 5.7 and 17.9 +/- 5.1 mL.kg (-1).min (-1), respectively, was not significantly different from measured VO2 (max), 21.7 +/- 4.8 and 17.3 +/- 4.0, respectively. ACSM overestimated VO2 (max) in men and women, 26.3 +/- 8.2 and 20.9 +/- 7.3, respectively. By using Bland-Altman plots, ACSM showed significant overestimation bias in more fit women (r = 0.29), whereas Foster showed no estimation bias in either gender. Significant predictors of VO2 (max) were gender, BMI, age, treadmill grade, and speed, with an equation R(2) of 0.70. A measure of current activity levels did not make it into the final model ( P= 0.0505) but is worthy of future consideration using more sensitive measures than ours.


CONCLUSIONS
ACSM is not appropriate for use when treadmill testing older adults. We believe the Foster equation's VO2 (max) prediction accuracy is acceptable, showing no bias along a continuum of aerobic capacity.
Peterson
Matthew J
MJ
Department of Veterans Affairs Medical Center, Durham, NC 27705, USA. peter076@mc.duke.edu
Pieper
Carl F
CF
Morey
Miram C
MC
D000368:Aged; D005080:Exercise Test; D005260:Female; D006801:Humans; D008297:Male; D010101:Oxygen Consumption; D011237:Predictive Value of Tests; D012044:Regression Analysis