Share this article on:

Evaluation of the American College of Sports Medicine Submaximal Treadmill Running Test for Predicting Vo2max

Marsh, Clare E

The Journal of Strength & Conditioning Research: February 2012 - Volume 26 - Issue 2 - p 548-554
doi: 10.1519/JSC.0b013e318220d9a8
Original Research

Marsh, CE. Evaluation of the ACSM submaximal treadmill running test for predicting V̇o2max. J Strength Cond Res 26(2): 548–554, 2012—The purpose of this study was to assess the validity of the American College of Sports Medicine's (ACSM's) submaximal treadmill running test in predicting V̇o2max. Twenty-one moderately well-trained men aged 18–34 years performed 1 maximal treadmill test to determine maximal oxygen uptake (M V̇o2max) and 2 submaximal treadmill tests using 4 stages of continuous submaximal exercise. Estimated V̇o2max was predicted by extrapolation to age-predicted maximal heart rate (HRmax) and calculated in 2 ways: using data from all submaximal stages between 110 b·min−1 and 85% HRmax (P V̇o2max-All), and using data from the last 2 stages only (P V̇o2max-2). The measured V̇o2max was overestimated by 3% on average for the group but was not significantly different to predicted V̇o2max (1-way analysis of variance [ANOVA] p = 0.695; M V̇o2max = 53.01 ± 5.38; P V̇o2max-All = 54.27 ± 7.16; P V̇o2max-2 = 54.99 ± 7.69 ml·kg−1·min−1), although M V̇o2max was not overestimated in all the participants—it was underestimated in 30% of observations. Pearson's correlation, standard error of estimate (SEE), and total error (E) between measured and predicted V̇o2max were r = 0.646, 4.35, 4.08 ml·kg−1·min−1 (P V̇o2max-All) and r = 0.642, 4.21, 3.98 ml·kg−1·min−1 (P V̇o2max-2) indicating that the accuracy in prediction (error) was very similar whether using P V̇o2max-All or P V̇o2max-2, with up to 70% of the participants predicted scores within 1 SEE (∼4 ml·kg−1·min−1) of M V̇o2max. In conclusion, the ACSM equation provides a reasonably good estimation of V̇o2max with no difference in predictive accuracy between P V̇o2max-2 and P V̇o2max-All, and hence, either approach may be equally useful in tracking an individual's aerobic fitness over time. However, if a precise knowledge of V̇o2max is required, then it is recommended that this be measured directly.

School of Health Sport and Rehabilitation Sciences, Directorate of Sport, Exercise and Physiotherapy, University of Salford, Salford, United Kingdom

Address correspondence to Clare Marsh,

Back to Top | Article Outline


The most accurate measure of maximal oxygen uptake (V̇o2max) involves the direct measurement of expired gases, but because of the potential risks associated with maximal exercise, the necessary technical expertise, and the participant motivation required, indirect methods have been developed as a safer and more convenient technique (2). These tests predict V̇o2max from submaximal exercise and are based on the relationship between heart rate (HR) and oxygen uptake (V̇o2) (HR–V̇o2 relationship). V̇o2max is predicted by the linear extrapolation of the slope of submaximal V̇o2 (or work rate [WR]) vs. HR values to an estimated maximal heart rate (HRmax). However, many predictive submaximal tests using HR measurements are considered to have limited accuracy because the procedures are based on several assumptions: that V̇o2 for a given WR is the same for everyone (2), that the relationship between HR and V̇o2 is linear up to maximal WRs, and that all participants reach a similar age-related HRmax (7,8).

The American College of Sports Medicine's (ACSM) metabolic equations (2) are commonly used to predict V̇o2. A number of previous studies have evaluated the accuracy of the ACSM equations for cycle ergometry (12,19,20), which were found to overestimate V̇o2max. Whilst cycle ergometry has traditionally been used as the primary exercise modality for submaximal exercise testing (2), running is a popular mode of exercise providing rationale for the requirement of a valid treadmill running test to predict V̇o2max that could be used as an alternative to cycle ergometry. An example of such a test is the ACSM submaximal treadmill running test that requires the use of the ACSM prediction equation for treadmill running. This test is protocol specific intended for estimation during steady state exercise, using at least 2 exercise stages, and above a minimum velocity. Unfortunately, most of the previous studies that have evaluated its accuracy (11,21,23,25,31) did not adhere to the specific exercise protocol but instead used either 1 minute nonsteady state stages (11,23), running speeds below the recommended velocity constraints (11,23), the use of a running track instead of a treadmill (25), or just one single running speed (21). Hence, although there was some agreement that V̇o2max was overestimated by these studies, it is difficult to draw conclusions regarding the accuracy of the treadmill test because of the methodological limitations. It appears that there have been no published studies that have reported the validity of V̇o2max estimates from the ACSM treadmill running test using the intended specific exercise protocol and, therefore, this provides rationale for its reevaluation but with adherence to the recommended exercise protocol.

In addition, predicted V̇o2max can be calculated in 2 ways using the ACSM equation for treadmill running, both involving extrapolation to HRmax. As outlined by ACSM (2), the test requires extrapolation of the submaximal HR–WR slope (including HRs between 110 b·min−1 and 85% HRmax obtained during the test) to age-predicted estimated HRmax, from which maximal WR is estimated and incorporated into the ACSM treadmill running equation to predict V̇o2max (2). Alternatively, Eston et al. (10) and Heyward (14) use the ACSM treadmill equation first to calculate ‘submaximal’ V̇o2 for the last 2 consecutive exercise stages of the test, followed by extrapolation of the submaximal HR–V̇o2 slope to age-predicted estimated HRmax. No previous studies have compared predicted V̇o2max using these 2 methods providing rationale for a comparison of this nature.

Hence, the focus of this study is to evaluate the accuracy of the ACSM treadmill running test in predicting V̇o2max by testing the hypothesis that it will overestimate measured V̇o2max and also to test the hypothesis that both methods used to calculate predicted V̇o2max will yield a similar value.

Back to Top | Article Outline


Experimental Approach to the Problem

After initial familiarization to the testing protocol, the participants performed 3 treadmill tests to measure and predict V̇o2max. The order of tests were implemented in a randomized manner to eliminate any biases introduced by order of administration. V̇o2max during treadmill running was measured once and estimated V̇o2max using the ACSM treadmill running test was assessed twice to check the repeatability. Predicted V̇o2max was calculated using the ACSM equation for treadmill running in 2 ways, both requiring the use of HR/WR data from the submaximal treadmill tests and both involving extrapolation to HRmax. The predicted and measured V̇o2max were compared to assess the accuracy of the ACSM treadmill running test in predicting V̇o2max.

Back to Top | Article Outline


Twenty-one moderately well-trained men (mean ± SD: age 21.33 ± 3.85 years, height 177.73 ± 6.86 cm, body mass 75.23 ± 6.98 kg, V̇o2max 53.01 ± 5.38 ml·kg−1·min−1 [range 45.36–60.79 ml·kg−1·min−1]) of mixed recreational training background participated in the study. The experimental protocol was approved by the Faculty Ethics Committee following the principles outlined by the World Medical Assembly Declaration of Helsinki. All the participants provided written informed consent following detailed verbal and written explanation of the aims, procedures, and any risks involved in the investigation, and they completed a health history questionnaire including details on training activity levels before participation. Before the testing sessions, the subjects were advised about the importance of maintaining their normal nutritional intake, particularly carbohydrate consumption, and to remain well hydrated. The participants were asked to avoid eating food for 2 hours before testing, to avoid caffeine on the day of testing, and to avoid heavy exercise or excess alcohol consumption for 24 hours before participation.

Back to Top | Article Outline


The participants reported to the laboratory for testing on 3 occasions. The participants underwent a brisk 3-minute walk at zero gradient as a warm-up before each test. A minimum of 48 hours elapsed between tests, and all tests were completed within a 2-week period and were performed at the same time of the day for each participant.

Back to Top | Article Outline

Maximal Exercise Testing

All the participants performed a progressive maximal exercise test to exhaustion on a motorized treadmill (Woodway Ergo ELG55, Weil am Rhein, Germany). A graded continuous ramp protocol using speed increments of 0.4–0.5 mph·min−1 at 0% gradient was designed for each participant. The initial running speed and size of the increment was based on fitness and ability of the participant. Expired gas was measured and analyzed continuously using a Metamax 3B portable online gas analyzer (Cortex, Biophysik GmbH, Leipzig, Germany) interfaced with a data acquisition system after calibration for gas concentrations using standard reference gases and for gas volume using a 3-L gas syringe (Hans Rudolph). Data averaging was performed after data collection using 30-second averages. The HR was measured using an HR monitor (Polar Sport Tester, Polar Electro, Kempele, Finland). All the tests lasted no more than 8–12 minutes (5,35). The test was terminated when the participant reached volitional exhaustion, could not keep pace with treadmill, and was unable to continue despite vigorous encouragement. Peak V̇o2 obtained during the treadmill test was taken as V̇o2max. Although primary and secondary criteria (15) were used to assist in establishing achievement of V̇o2max, the secondary criteria were used with caution because they have been rejected as a means of verifying V̇o2max in ramp exercise tests (28).

Back to Top | Article Outline

Submaximal Exercise Testing

The participants reported to the laboratory to perform the ACSM multistage submaximal exercise test. The ACSM protocol requires HR to be measured at 2 or more 3-minute submaximal stages of continuous exercise that raise the HR to >110 b·min−1 during the running test, and terminating when 85% of age-predicted HRmax (85% HRmax) was attained as described in the ACSM Guidelines (2). During this test, the participants performed 4 stages; treadmill speed was increased between stages while maintaining a constant gradient of zero, and this protocol was repeated on a separate occasion as a repeatability study. A zero gradient was used because no gradient was used during V̇o2max tests. The range of treadmill speed for incremental testing was 5 mph (134 m·min−1) to 9 mph (241.2 m·min−1), and the increments in speed varied between the participants according to fitness. Each stage lasted for a minimum of 3 minutes until steady state HR was achieved, the criteria for which was a plateau in HR of ±2 b·min−1 between 2 consecutive minutes of each stage.

Back to Top | Article Outline

Prediction of V̇o2max

Heart rate and running speed data were then used to calculate predicted V̇o2max in 2 ways using 1 or more of the equations below:

(a) Extrapolation of estimated maximal speed using all data points between 110 b·min−1 and 85% HRmax as outlined in the ACSM guidelines (2) (P V̇o2max-All). Calculation of predicted V̇o2max requires the HR values for each steady-state stage to be plotted against speed. The line generated from the plotted points is then extrapolated to age-predicted maximal HR (220·− age) and a perpendicular line dropped to the x-axis to estimate speed that would have been achieved if the participant had worked to maximum. In this study, linear regression was used for this purpose to ensure the most accurate estimation of maximal speed. Maximal V̇o2 was then predicted by incorporating the extrapolated maximal speed into the ACSM treadmill running equation (equation 1).

(b) Use of HR and speed data from the ‘last 2’ steady-state stages within 85% HRmax only (P V̇o2max-2) as outlined by Eston et al. (10) and Heyward (14). Submaximal V̇o2 (ml·kg−1·min−1) was calculated using the ACSM treadmill running equation (equation 1) for each of the last 2 stages of the running test. Two further equations were then used to calculate predicted V̇o2max: the ratio of the difference between the 2 submaximal V̇o2 and corresponding change in HR was used to calculate slope (b) of the V̇o2 regression line (equation 2; stages 1 and 2 refer to the last 2 submaximal stages of the test); slope b was subsequently used to predict V̇o2max (equation 3) by extrapolation of the V̇o2:HR values from stage 2 (the last completed stage) to estimated maximal HR (220 − age).

ACSM equation for treadmill running used to calculate V̇o2 (ml·kg−1·min−1) (2):

Calculation of slope V̇o2 regression line (b):

Calculation of V̇o2max (ml·kg−1·min−1):

Back to Top | Article Outline

Statistical Analyses

Statistical analysis was performed with the Statistical Package for Social Sciences (SPSS), version 16.0. Comparisons between the predictions of V̇o2max from the 2 submaximal tests (repeatability) were made using paired t-tests, and a intraclass correlation coefficient was calculated to assess reliability. Measured and predicted V̇o2max are presented as mean ± SD for the group and were compared using a 1-way ANOVA. In addition, Pearson's Product moment correlation coefficient was calculated to show the relationship between predicted and measured V̇o2max. The standard error of estimate (S EE) and total error (E) were calculated to quantify the error of prediction of the estimated V̇o2max, and also for age-predicted HRmax. A paired t-test was used to test for difference between estimated and observed HRmax. All statistical tests were performed at a significance level of p ≤ 0.05.

Back to Top | Article Outline


Test-retest reliability between the 2 predicted V̇o2max trials was good, the data were highly positively correlated (r = 0.922, p < 0.05) and not significantly different (p = 0.417). The mean ± SD of measured and predicted V̇o2max for the group are summarized in Table 1 along with correlation coefficients, SEE and E values. Mean predicted V̇o2max values were slightly higher, although not significantly different (p = 0.695) (Figure 1) from the measured V̇o2max, overestimating measured V̇o2max by on average 3% for the group. Pearson's correlation coefficients showed a significant (p < 0.05) positive relationship between the measured and the predicted V̇o2max, with similar r values for both P V̇o2max-2 and P V̇o2max-All. In addition, SEE and E were computed, which were also very similar for both predictive methods.

Table 1

Table 1

Figure 1

Figure 1

A significant difference (p = 0.003) was also found between age-adjusted estimated HRmax and observed HRmax (199 ± 4 vs. 192 ± 9 b·min−1, respectively) with 76.5% of the participants having a lower observed HRmax compared with estimated HRmax. The SEE and E of age-predicted HRmax was 8.6 and 8.1 b·min−1, respectively.

Back to Top | Article Outline


The group results demonstrate that mean predicted V̇o2max using the ACSM treadmill test slightly overestimates the measured V̇o2max, and the error in accuracy of the predicted value indicated by the SEE and E were very similar for each of the 2 methods (P V̇o2max-2 and P V̇o2max-All). From an equation accuracy perspective, SEE and E are more useful in indicating the accuracy of a prediction equation than mean differences because unless deviations from the mean are all primarily positive or negative, they may have a canceling effect hence minimizing the mean difference (19,20). For example, although predicted V̇o2max was reported to slightly overestimate the measured V̇o2max for the group and this trend of overestimation occurred in 70% of the participants for both P V̇o2max-2 and P V̇o2max-All, it should be noted that M V̇o2max was underestimated in the remaining 30% of the participants. The SEE, on the other hand, indicates the accuracy of a predicted score, as does E, which takes into account the total deviations of the predicted values from the actual scores and so indicates the dispersion of scores around a regression line. A smaller SEE and E indicates a greater accuracy of prediction (3). Hence, because the SEE and E values were extremely similar for P V̇o2max-2 and P V̇o2max-All (SEE = 4.21–4.35; E = 3.98–4.08 ml·kg−1·min−1) with up to 70% of the predicted scores within 1 SEE of M V̇o2max, one can conclude that either approach can be used to predict V̇o2max with a similar accuracy.

When evaluating the magnitude of the SEE and E in this study, it is helpful to compare the errors with those of other studies that have also evaluated the ACSM treadmill running equation. The results of this study are in agreement with those of other studies in that V̇o2max is overestimated, but this study reports improved predictive accuracy over most of these studies. The SEE values for predicted V̇o2max in this study of 4.21–4.35 ml·kg−1·min−1 (P V̇o2max-2 and P V̇o2max-All) translate to an error of slightly >1 metabolic equivalent, which is better than the SEE reported in most other previous studies. For example, Foster et al. (11) found an SEE of 4.8 ml·kg−1·min−1 in an older cardiac patient population, and Mier and Gibson (23) reported an even higher SEE of 5.98 ml·kg−1·min−1; both the studies did not adhere to the exercise protocol stipulated by ACSM and used 1-minute stages of non–steady-state exercise – failure to achieve steady-state HR may have contributed to a greater overestimation in these studies. The only study reporting a better predictive accuracy was Morrow et al. (25) with an SEE of 3.7 ml·kg−1·min−1, but their data are not truly comparable because the ACSM protocol was performed on a running track instead of on a treadmill and at altitude. Unfortunately, because the total error was not calculated in any of the studies that evaluated the ACSM treadmill equation, a comparison of E values cannot be included.

However, despite there being no significant difference between predicted and measured V̇o2max for the group, and the relatively low SEE compared to other studies, a number of participants demonstrated considerable variance between predicted and measured V̇o2max, with 30% of predicted scores varying by >1 SEE of M V̇o2max. Because the procedures for estimating V̇o2max from the HR response during submaximal exercise tests are based on several assumptions, the focus of the ensuing discussion is to consider the implications of these assumptions on the predictive accuracy of the ACSM treadmill test in estimating V̇o2max.

When estimating V̇o2max using HR measurements, 1 of the assumptions of such procedures is that the aerobic demand (V̇o2) for a given submaximal WR is the same for everyone (2). However, variability does exist in measured V̇o2 for a given submaximal WR among different individuals (interindividual variability), with an SEE as high as 7% (2). Factors that may affect the V̇o2 during running (running economy) include training status, technique of movement, gender, age, and body mass. Trained runners have been shown to be more economical than untrained runners (24), men may be more economical than women (4), and adult athletes may be more economical than young athletes (6). Using the example of training status, the evaluation of submaximal aerobic demand during treadmill running (24) in elite, highly trained, and moderately trained runners and nonrunners (V̇o2max = 75.6, 70.5, 59.2, 51.4 ml·kg−1·min−1, respectively) revealed that V̇o2 varied significantly (p < 0.05) as a function of training—untrained runners were less economical than the 3 running groups and moderately trained and trained runners were less economical than elite runners. Hence, the assumption that V̇o2 for a given work load is the same for everyone, as assumed by the ACSM equation (equation 1), may influence the accuracy to which V̇o2max can be predicted for different individuals. For example, Morrow et al. (25) evaluated the ACSM equation using well-trained elite male runners (V̇o2max = 68.2 ml·kg−1·min−1), whereas this study used subjects with a lower training status (V̇o2max = 53.01 ml·kg−1·min−1), but the potentially better running economy of the elite male runners (25) compared with less trained runners as suggested by Morgan et al. (24) is not accounted for and may account for the slight variation in predictive accuracy between studies.

A further assumption made when estimating V̇o2max using HR measurements is that the HR– V̇o2 relationship is linear up to and including maximal WRs. Davies (8), however, showed that the HR–V̇o2 relationship is linear over most of its working range, but at near maximal WRs, the relationship changes, and the curve becomes nonlinear. Furthermore, Davies (8) reported that intraindividual HR variation fluctuates at different submaximal WRs influencing the linearity of the HR–V̇o2 relationship. The ACSM submaximal treadmill test stipulates that HR remains between 110 b·min−1 and 85% of age-predicted HRmax for at least 2 consecutive stages. The rationale for these recommendations is that, presumably, the HR–V̇o2 relationship remains linear and also that a steady-state HR is achievable within that HR range. Despite the linear HR–V̇o2 relationship during submaximal exercise, for any given level of submaximal work, the HR can vary independently of V̇o2 because of factors such as emotional state, hydration state, ambient temperature and humidity, body temperature and heat stress, time after previous meal and mechanical efficiency (1,13,22,30). These changes are often unaccompanied by concomitant changes in HRmax or the true V̇o2max; therefore, any intraindividual variation in submaximal HR measurements subsequently used in equations to calculate predicted V̇o2max may lead to errors in its calculation because a nonlinear increase in the HR–WR relationship directly influences the slope of the V̇o2 regression. Davies (8) reported that predicting V̇o2max using relatively low workloads may produce less accurate estimates because intraindividual variation in HR is 8% higher at lower workloads but is reduced to approximately 2% at higher exercise HRs of above 165 b·min−1, significantly improving the accuracy of predicting V̇o2max. Lamberts et al. (18) and Lamberts and Lambert (17) also reported that the lowest variation in HR occurred at higher HRs (∼85–90% HRmax), this may be because faster running speeds are more economical (22). Although the use of HR >165 b·min−1 suggested by Davies (8) is high and contradicts ACSM recommendations because it would exceed 85% age-predicted HRmax in individuals over the age of 26 years (85% of 194 b·min−1 = 165 b·min−1), it would seem reasonable to recommend that one works at higher HRs where intraindividual variation in HR is potentially lower but not so high that the HR deviates away from linearity and fails to achieve a steady state—both of which can contribute to inaccurate predictions of V̇o2max.

With respect to this study, it was noted that P V̇o2max-All and P V̇o2max-2 resulted in a similar mean predicted V̇o2max. For P V̇o2max-All, data from all stages between 110 b·min−1 and 85% HRmax were included in the HR–WR regression line, but one could argue that HR at the lower WRs may be subject to greater intraindividual variation and may instil inaccuracy into this method. However, fitting a regression line through all data points should eliminate anomalies that may occur in the data because of intraindividual HR variation at lower WRs, and furthermore, it would seem intuitive that the validity of the test would be enhanced by using more exercise stages. P V̇o2max-2, on the other hand, only uses the HR–WR data from 2 exercise stages from the exercise test and could be open to error because any deviation in HR away from linearity between the 2 exercise stages such as an unusually large or small increase in an individual's HR will profoundly affect the regression slope and subsequent calculation of predicted V̇o2max. However, because P V̇o2max-2 uses data from the last 2 stages within 85% HRmax, HR should be at a level where intraindividual HR variation is thought to be low (8). Two additional points that relate to the assumed linear relationship between HR and V̇o2 are that where the HR response to exercise is abnormal (tachycardia or bradycardia) this can lead to serious underestimation or overestimation of V̇o2max. Also, predicting V̇o2max by extrapolation of the submaximal HR–V̇o2 slope to estimated maximal HR does not allow for the nonlinear nature of curve toward maximal exercise, which may also result in predictive errors (30).

A further assumption that is made when estimating V̇o2max using the HR measurements is that all participants of a similar age attain a comparable HRmax (220 − age) (8). This assumption can lead to a large amount of error because considerable interindividual variability exists in the maximal HR achieved by people of the same age, the SD of age related HRmax is 10–12 b·min−1 (2,34). The traditional 220 − age equation has been reported to have a tendency to overestimate HRmax in many younger individuals (34). This trend was shown in the present study in which a significant difference (p = 0.003) was found between age-adjusted estimated HRmax and observed HRmax with >75% of the participants having a lower observed HRmax compared with estimated HRmax. The consequence of using estimated HRmax that is higher than the observed HRmax is an inflated predicted V̇o2max, and this is likely to be a major contributory factor to the overestimation of V̇o2max using the ACSM treadmill test. Interestingly, the use of age-predicted HRmax has recently been rejected as valid when verifying the achievement of V̇o2max during maximal exercise testing (28). It is possible, therefore, that the accuracy of predicted V̇o2max may be improved when observed HRmax is used, but it is appreciated that in most cases, a knowledge of HRmax would not be available to those using the ACSM treadmill running equations.

In conclusion, the ACSM treadmill running test slightly overestimates the measured V̇o2max and the accuracy of predicted V̇o2max was reasonably good and similar whether using data from all stages within 110 b·min−1 and 85% HRmax or just the data from the last 2 stages within 85% HRmax. Although 70% of predicted values were within 1 SEE of measured V̇o2max, nearly a third of individuals did show a larger variation between predicted and measured V̇o2max. This highlights that the use of HR as a independent variable influences the accuracy of predicted V̇o2max, and this is most likely because of the error incurred by the assumed linear relationship between HR and V̇o2, and extrapolation to estimated HRmax.

It is appreciated that a limitation of this study is the low external validity because of the relatively small sample size and the homogenous nature of the group that were tested. The results, therefore, cannot be generalized to the wider population but only to a specific group of young men. Future research needs to include participants from a wider age range, varying levels of fitness, and both genders. Although the ACSM treadmill ‘walking’ equation has been validated in healthy older men and women (age range 65–90) (27) with the conclusion that it tended to overestimate V̇o2max, particularly as aerobic fitness levels increased, the ACSM treadmill ‘running’ equation has not be validated in this group, because for many elderly participants, walking is more appropriate. However, future validation of the ACSM treadmill running equation research should include men and women between the age of 25 and 60 years.

Furthermore, Taylor et al. (33) observed that raising the grade while keeping the speed constant was the most satisfactory method of increasing work load to attain V̇o2max on a treadmill and, in fact, V̇o2max has been shown to be higher during uphill compared with during level running (26) by approximately 2 ml·kg−1·min−1 (9,32). This study used level running during the assessment of V̇o2max, but because maximal running speed may have been the limiting factor during the maximal test instead of the cardiopulmonary capacity for some individuals, it may have been better to have used an alternative treadmill protocol that used running speeds well within the individual's running capacity, and used treadmill inclination to increase WR. This may have achieved a measured V̇o2max slightly closer to predicted V̇o2max. It was noted by Kasch et al. (16), however, that there was no significant difference in V̇o2max achieved during horizontal and inclined treadmill running. Finally, the reliability of peak V̇o2 during the maximal test could have been tested by the use of a supramaximal constant WR performed immediately after the incremental test (29).

Back to Top | Article Outline

Practical Applications

From a practical viewpoint, the ACSM equation provides a reasonable estimation of V̇o2max, overestimating measured V̇o2max by approximately 3% on average for the group, although V̇o2max was not overestimated in all individuals—it was underestimated in about a third of individuals. However, up to 70% of the participants achieved a predicted V̇o2max that was actually within just over 4 ml·kg−1·min−1 of measured V̇o2max, which is fairly close. Because the accuracy of the predicted value was very similar for both methods used (P V̇o2max-2 and P V̇o2max-All), it is recommended that the approach used to calculate predicted V̇o2max be down to personal preference although there are a few considerations that need to be taken into account when adopting an approach. When using P V̇o2max-2, it is important that the HR measurements used are taken from the latter exercise stages of the test (but within 85% of HRmax), where the HR is less subject to intraindividual variation that is more likely to occur at low WRs. When using P V̇o2max-All, it is essential that care is taken when plotting the HR against WR to ensure the most accurate extrapolation to maximal WR that is subsequently incorporated into the ACSM equation, careless data plotting will without doubt lead to an inaccurate prediction. With respect to who and how the use of the ACSM treadmill test is appropriate, it would serve as a useful tool in tracking changes in aerobic fitness in the same participant over a period of time and could be used by fitness professionals, gym instructors, and potentially coaches although it may not be suitable where a large number of players need to be assessed unless there are multiple treadmills to hand. Importantly, however, the ACSM treadmill test is not appropriate if the HR response is abnormal, perhaps because of medication, because predicted V̇o2max will be severely overestimated or underestimated. If a precise measure of V̇o2max is required though, as is necessary for some athletic groups, then this should be measured directly.

Back to Top | Article Outline


Research relating to this manuscript was not funded by a grant support. The author would like to thank all the participants who volunteered to participate in this study. The author has no conflict of interest to declare.

Back to Top | Article Outline


1. Achten, J and Jeukendrup, AE. Heart rate monitoring: Applications and limitations. Sports Med 33: 517–538, 2003.
2. American College of Sports Medicine. Guidelines for Exercise Testing and Prescription. London, United Kingdom: Lippincott Williams & Wilkins, 2006.
3. Berg, KE and Latin, RW. Essentials of Research Methods in Health, Physical Education, Exercise Science, and Recreation. London, United Kingdom: Lippincott Williams & Wilkins, 2008.
4. Bransford, DR and Howley, ET. Oxygen cost of running in trained and untrained men and women. Med Sci Sports 9: 41–44, 1977.
5. Buchfuhrer, MJ, Hansen, JE, Robinson, TE, Sue, DY, Wasserman, K, and Whipp, BJ. Optimizing the exercise protocol for cardiopulmonary assessment. J Appl Physiol 55: 1558–1564, 1983.
6. Bunc, V and Heller, J. Energy cost of running in young and adult female athletes. Ergonomics 37: 167–174, 1994.
7. Davies, CTM. Maximum oxygen uptake: Prediction from cardiac frequency during submaximal exercise. J Physiol 189: P77–P78, 1967.
8. Davies, CTM. Limitations to the prediction of maximum oxygen intake from cardiac frequency measurements. J Appl Physiol 24: 700–706, 1968.
9. Draper, SB, Wood, DM, and Fallowfield, JL. The effect of test protocol on V̇o2peak and the incidence of a V̇o2plateau. J Sports Sci 17: 31, 1999.
10. Eston, R, Williams, JG, and Faulkner, J. Control of exercise intensity using heart rate, perceived exertion and other non-invasive procedures. In: Kinanthropometry and Exercise Physiology Laboratory Manual: Test, Procedures and Data. (Vol. 2). Physiology. R. Eston and T. Reilly, eds. London, United Kingdom: Routledge, 2009. pp. 237–270.
11. Foster, C, Crowe, AJ, Daines, E, Dumit, M, Green, MA, Lettau, S, Thompson, NN, and Weymier, J. Predicting functional capacity during treadmill testing independent of exercise protocol. Med Sci Sports Exerc 28: 752–756, 1996.
12. Greiwe, JS, Kaminsky, LA, Whaley, MH, and Dwyer, GB. Evaluation of the ACSM submaximal ergometer test for estimating V̇o2max. Med Sci Sports Exerc 27: 1315–1320, 1995.
13. Hamilton, MT, Gonzalez-Alonso, J, Montain, SJ, and Coyle, EF. Fluid replacement and glucose infusion during exercise prevent cardiovascular drift. J Appl Physiol 71: 871–877, 1991.
14. Heyward, VH. Advanced Fitness Assessment and Exercise Prescription. Leeds, United Kingdom: Human Kinetics, 2002.
15. Howley, ET, Bassett, JR, and Welch, HG. Criteria for maximal oxygen uptake: Review and commentary. Med Sci Sports Exerc 27: 1292–1301, 1995.
16. Kasch, FW, Wallace, JP, Huhn, RR, Krogh, LA, and Hurl, PM. V̇o2max during horizontal and inclined treadmill running. J Appl Physiol 40: 982–983, 1976.
17. Lamberts, RP and Lambert, MI. Day-to-day variation in heart rate at different levels of submaximal exertion: Implications for monitoring training. J Strength Cond Res 23: 1005–1010, 2009.
18. Lamberts, RP, Lemmink, KA, Durandt, JJ, and Lambert, MI. Variation in heart rate during submaximal exercise: Implications for monitoring training. J Strength Cond Res 18: 641–645, 2004.
19. Lang, PB, Latin, RW, Berg, KE, and Mellion, MB. The accuracy of the ACSM cycle ergometry equation. Med Sci Sports Exerc 24: 272–276, 1992.
20. Latin, RW, Berg, KE, Smith, P, Tolle, R, and Woodby-Brown, S. Validation of a cycle ergometry equation for predicting steady-rate V̇o2. Med Sci Sports Exerc 25: 970–974, 1993.
21. Latin, RW and Elias, BA. Predictions of maximum oxygen uptake from treadmill walking and running. J Sports Med Phys Fitness 33: 34–39, 1993.
22. McArdle, WD, Katch, FI, and Katch, VL. Exercise Physiology, Energy, Nutrition and Human Performance. London, United Kingdom: Lippincott Williams & Wilkins, 2001.
23. Mier, CM and Gibson, AL. Evaluation of a treadmill test for predicting the aerobic capacity of firefighters. Occup Med 54: 373–378, 2004.
24. Morgan, DW, Bransford, DR, Costill, DL, Daniels, JT, Howley, ET, and Krahenbuhl, GS. Variation in the aerobic demand of running among trained and untrained subjects. Med Sci Sports Exerc 27: 404–409, 1995.
25. Morrow, JR, Van Handel, PJ, Bradley, PW, and Kearney JT. The validity of the ACSM energy requirement equation with well-trained elite athletes at altitude. Med Sci Sports Exerc 20: S1, 1988.
26. Paavolainen, L, Nummela, A, and Rusko, H. Muscle power factors and V̇o2max as determinants of horizontal and uphill running performance. Scand J Med Sci Sports 10: 286–291, 2000.
27. Peterson, MJ, Pieper, CF, and Morey, MC. Accuracy of V̇o2max prediction equations in older adults. Med Sci Sports Exerc 35: 145–149, 2003.
28. Poole, DC, Wilkerson, DP, and Jones, AM. Validity of criteria for establishing maximal O2 uptake during ramp exercise tests. Eur J Appl Physiol 102: 403–410, 2008.
29. Rossiter, HB, Kowalchuk, JM, and Whipp, BJ. A test to establish maximum O2 uptake despite no plateau in the O2 uptake response to ramp incremental exercise. J Appl Physiol 100: 764–700, 2006.
30. Rowell, LB, Taylor, HL, and Yang W. Limitations to prediction of maximal oxygen intake. J Appl Physiol 19: 919–927, 1964.
31. Sherman, NW, Pivarnik, JM, Jackson, AS, and Ross, RM. The effect of estimating the energy cost of running on exercise prescription. Med Sci Sports Exerc 20: S2, 1988.
32. St Clair Gibson, A, Lambert, MI, Hawley, JA, Broomhead, S, and Noakes, TD. Measurement of maximal oxygen uptake from two different laboratory protocols in runners and squash players. Med Sci Sports Exerc 31: 1226–1229, 1999.
33. Taylor, HL, Buskirk, E, and Henschel, A. Maximal oxygen intake as an objective measure of cardio-respiratory performance. J Appl Physiol 8: 73–80, 1955.
34. Whaley, MH, Kaminsky, LA, Dwyer, GB, Getchell, LH, and Norton, JA. Predictors of over- and underachievement of age-predicted maximal heart rate. Med Sci Sports Exerc 24: 1173–1179, 1992.
35. Yoon, B, Kravitz, L, and Robergs, R. V̇o2max, protocol duration, and the V̇o2 plateau. Med Sci Sports Exerc 39: 1186–1192, 2007.

maximal heart rate; extrapolation; intraindividual variation; regression slope

Copyright © 2012 by the National Strength & Conditioning Association.