Submitted 4 June 2020
Accepted 4 January 2021
Published 15 March 2021
Corresponding author
Hung-Ting Chen,
simondr@mcu.mail.edu.tw
Academic editor
Celine Gallagher
Additional Information and
Declarations can be found on
page 13
DOI 10.7717/peerj.10831
Copyright
2021 Chung et al.
Distributed under
Creative Commons CC-BY 4.0
OPEN ACCESS
Predicting maximal oxygen uptake from
a 3-minute progressive knee-ups and step
test
Yu-Chun Chung1, Ching-Yu Huang2, Huey-June Wu3, Nai-Wen Kan1,
Chin-Shan Ho4, Chi-Chang Huang4 and Hung-Ting Chen5
1 Center of General Education, Taipei Medical University, Taipei, Taiwan
2 Service Systems Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan
3 Department of Combat Sports and Chinese Martial Arts, Chinese Culture University, Taipei, Taiwan
4 Graduate Institute of Sports Science, National Taiwan Sport University, Taoyuan, Taiwan
5 Physical Education Office, Ming Chuan University, Taipei, Taiwan
ABSTRACT
Background. Cardiorespiratory fitness assessment is crucial for diagnosing health
risks and assessing interventions. Direct measurement of maximum oxygen uptake
( ˙VO2 max) yields more objective and accurate results, but it is practical only in a
laboratory setting. We therefore investigated whether a 3-min progressive knee-up and
step (3MPKS) test can be used to estimate peak oxygen uptake in these settings.
Method. The data of 166 healthy adult participants were analyzed. We conducted a
˙VO2 max test and a subsequent 3MPKS exercise test, in a balanced order, a week later.
In a multivariate regression model, sex; age; relative ˙VO2 max; body mass index (BMI);
body fat percentage (BF); resting heart rate (HR0); and heart rates at the beginning
as well as at the first, second, third, and fourth minutes (denoted by HR0, HR1, HR2,
HR3, and HR4, respectively) during a step test were used as predictors. Moreover, R2
and standard error of estimate (SEE) were used to evaluate the accuracy of various body
composition models in predicting ˙VO2max.
Results. The predicted and actual ˙VO2 max values were significantly correlated (BF%
model: R2 = 0.624, SEE = 4.982; BMI model: R2 = 0.567, SEE = 5.153). The BF% model
yielded more accurate predictions, and the model predictors were sex, age, BF%, HR0,
1HR3−HR0, and 1HR3−HR4.
Conclusion. In our study, involving Taiwanese adults, we constructed and verified a
model to predict ˙VO2 max, which indicates cardiorespiratory fitness. This model had
the predictors sex, age, body composition, and heart rate changes during a step test. Our
3MPKS test has the potential to be widely used in epidemiological research to measure
˙VO2 max and other health-related parameters.
Subjects Anatomy and Physiology, Cardiology, Hematology, Kinesiology, Respiratory Medicine
Keywords Aerobic ability, 3-min Harvard step test, Cardiovascular function, Field tests
INTRODUCTION
In 2016, the American Heart Association launched a series of publications promoting the
clinical evaluation of cardiorespiratory fitness (CRF) with the overall aim of improving the
prevention and treatment of cardiovascular disease (CVD; Ross et al., 2016). Furthermore,
How to cite this article Chung Y-C, Huang C-Y, Wu H-J, Kan N-W, Ho C-S, Huang C-C, Chen H-T. 2021. Predicting maximal oxygen
uptake from a 3-minute progressive knee-ups and step test. PeerJ 9:e10831 http://doi.org/10.7717/peerj.10831
the association urged the US federal government to compile a registered CRF database
(Kaminsky et al., 2013); this highlights the importance of CRF. CRF is generally defined as
the integrated ability to transport oxygen from the atmosphere to the mitochondria for
physical activity. Notably, CRF involves the respiratory, circulatory, and neuromuscular
systems and has a clear and direct relationship with the functions of various systems.
Individuals with weak CRF have an up to 70% all-cause mortality rate and 56%
cardiovascular mortality rate (Kodama et al., 2009). Similarly, every 1-MET increase in
athletic ability reduces all-cause mortality and cardiovascular mortality rates by 15% and
13%, respectively (Kodama et al., 2009). Numerous studies have suggested that CRF and
CVD are related to all-cause mortality and cancer mortality (Blair et al., 1989; Laukkanen
et al., 2004; Sui, LaMonte & Blair, 2007; Sawada et al., 2014; Sui, LaMonte & Blair, 2007).
A recent meta-analysis reported CRF to be a predictor of the risk of sudden cardiac death
(Jiménez-Pavón, Lavie & Blair, 2019). Therefore, CRF assessment is crucial for diagnosing
health risks and assessing interventions.
CRF can be measured using the respiratory data of exercising participants. Specifically,
these data are used to calculate maximal oxygen uptake ( ˙VO2 max), the gold standard
for CRF measurement; in the measurement, participants either run on a treadmill
or use an ergometer at an exercise intensity that increases progressively until a given
maximum is reached. Although submaximal exercise models and nonexercise models
(without an exercise test) are alternatives for estimating ˙VO2 max in measuring CRF
(Abut, Akay & George, 2016), the direct measurement of ˙VO2 max yields more objective
and accurate results. However, such measurement is inconvenient because it requires
expensive equipment and well-trained experimenters. In addition, participants perceive
such measurement tests to be exhausting, time-consuming, and relatively risky and are thus
less willing to participate. Accordingly, researchers have developed various submaximal
exercise tests to indirectly estimate ˙VO2 max; moreover, retrospective studies conducted by
the American Heart Association have demonstrated that CRF indicators, whether directly
measured or indirectly estimated, are robust indicators of health (Ross et al., 2016).
Submaximal exercise is a common method for estimating ˙VO2 max, particularly in
epidemiological research and large-scale physical fitness testing that involve numerous
participants. The field tests in these measurement procedures include running, shuttle
running, and the step test, with the step test being the most common method for
evaluating cardiovascular function (Grant, Joseph & Campagna, 1999). In particular, the
YMCA step test is widely used to predict ˙VO2 max (Beutner et al., 2015). Currently, the
Sports Administration of Taiwan’s Ministry of Education uses the 3-min Harvard step
test for its National Physical Fitness and Cardiovascular Test. Specifically, three heart rate
measurements are used to calculate the step-up index. However, previous studies have
reported considerable differences in the validity of using the step test index to evaluate
˙VO2 max, with the corresponding correlation coefficient (R) being 0.35–0.94 (Buckley et
al., 2004; Chang & Lin, 1995; Mazic et al., 2001; Su, Lin & Hsieh, 2006; Chang & Lin, 1995;
Yoopat, Vanwonterghem & Louhevaara, 2002). Furthermore, step tests require the use of
step-up boxes, and the overall test time must be at least 6 min to allow for heart rate
recovery. Participants who are less physically fit or who have knee conditions may find it
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difficult to complete the test and may also fall in the process of going up and down the
stairs. A team of Japanese researchers developed a new 3-min walking test (Cao et al., 2013).
Specifically, their main evaluation criteria comprised participant characteristics such as age,
sex, and BMI as well as participants’ RPE during exercise. These criteria were determined to
be effective predictors of ˙VO2 max, and participants thought that this method was quicker
and easier.
Tests of general CRF are crucial to the clinical evaluation of CVD. Additionally,
the advantages and disadvantages, such as venue size, participant willingness, and the
instruments, of various past field tests should be considered during the formulation of
new methods, as done in the present study. Accordingly, we conducted the present study
with the aim of developing a rapid, convenient, and low-risk model that can predict ˙VO2
max in Taiwanese adults. Additionally, our model accords with the principle that physical
exercise ought to be progressive. We investigated the feasibility of using a 3-min progressive
knee-ups and step (3MPKS) test to predict ˙VO2 max.
MATERIALS AND METHODS
Participants
Prospective participants were excluded if they (1) had cardiovascular, pulmonary, or
metabolic diseases; (2) had neurological, muscular, or skeletal disorders that affected their
athletic ability; (3) had other health conditions that made them unsuited for moderate or
intense exercise; or (4) were taking medications that could affect the outcome of this study.
In total, among 200 participants recruited for this experiment, 166 completed the test.
The data of the 166 participants were included in the analysis (age: 20–64 years; 65 men,
101 women). Among the 34 participants excluded, one participant withdrew from the
experiment after experiencing suspected symptoms of arrhythmia during exercise; 11 were
excluded because they failed to complete the step test within the requisite time (3 min); 12
were excluded because they could not attain the requisite step frequency and knee height
for 20 consecutive seconds; nine were excluded because they had missing or improperly
measured heart rate data; and one was excluded for having a ‘‘0’’ in their heart rate data.
All participants signed an informed consent form after understanding their rights, the
risks when participating in this study, and the purpose and method of our research. Our
research plan was approved by the Institutional Review Boards (IRBs) of the Industrial
Technology Research Institute and of Taipei Medical University (IRB No: N201808055).
Participant characteristics are detailed in Table 1.
Procedure
The anthropometric and body composition measures were height, weight, and body fat
percentage (BF%). BF% was measured using bioelectrical impedance analysis (InBody 720,
Biospace, USA; McLester et al., 2020), and body mass index (BMI, in kg/m2) was calculated
as the quotient that is weight (in kilograms) divided by the squared height (in meters).
We conducted two exercise tests in a counterbalanced design. The second test was
conducted exactly 1 week after the first and at the same time of the day to ensure that the
participants recovered adequately from the first exercise. The participants underwent 5–10
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Table 1
Participant characteristics.
Total
Training dataset
Testing dataset
Sample size(n)
166
124
42
Age(years)
41.9 ± 9.6
42.2 ± 9.4
40.8 ± 10.2
Male (n)
65
44
21
Height
164.83 ± 8.35
164.33 ± 8.07
166.30 ± 9.06
Weight
65.63 ± 13.60
65.22 ± 14.08
66.85 ± 12.15
Body fat (%)
27.81 ± 7.92
27.79 ± 7.65
27.86 ± 8.75
˙VO2 max (ml kg−1 min−1)
34.45 ± 8.69
34.06 ± 8.14
35.61 ± 10.15
HR0
86.04 ± 12.78
86.04 ± 12.99
86.02 ± 12.29
1HR3- HR0
71.00 ± 13.24
71.10 ± 13.41
70.69 ± 12.87
1HR3-HR4
14.64 ± 13.72
14.14 ± 13.94
16.65 ± 14.09
Notes.
Data are presented as mean ± standard deviation.
HR0, heart rate at the beginning; 1HR3-HR0, difference between third minute heart rate and beginning heart rate; 1HR3-
HR4, difference between third minute and fourth minute heart rates.
min of dynamic warm-up prior to both exercise tests; to mitigate extraneous influence on
the results, the participants were also asked not to engage in moderate or intense exercise
48 h before both exercise tests.
To measure the ˙VO2 max of the participants, we used a bicycle ergometer (839E,
Monark, Varberg, Sweden) for a maximal graded exercise test. After participants sat still
for 2 min, they sat on the stationary bicycle and started cycling at the speed of 70 ± 10
rpm. The participants began the exercise with a 2-min warm-up at 25 W loading, where
the loading was increased by 15 W every 2 min. The testing was terminated when the
participants could no longer continue the exercise due to bradypnea or fatigue, although
the bicycle speed was maintained at 70 rpm. Subsequently, the participants rested for 3 min
at a loading of 0 W (no resistance). Throughout the exercise testing, the participants wore a
watch to monitor their heart rate and a mask to monitor their breathing. Breath-by-breath
analysis was conducted on the participant data through a cardiopulmonary testing system
(MetaMax 3B, Cortex, Germany). ˙VO2 max was defined as the maximum average oxygen
uptake for 20 consecutive seconds. To ensure that every participant reached ˙VO2 max, we
defined ˙VO2 max as being reached if two of the three following conditions were met: (1)
˙VO2 plateaus with increases in work rate; (2) the maximum respiratory exchange ratio
is ≥1.10; and (3) 90% of the expected maximal heart rate, obtained by subtracting the
participant’s age from 220, is reached (American College of Sports Medicine, 2009). Nearly
all participants satisfied the criteria for an acceptable ˙VO2 max, with only one participant
excluded from the ˙VO2 max test due to suspected symptoms of arrhythmia observed in
the step test.
3MPKS test
Prior to the 3MPKS test, the participants wore a sports watch with heart rate (Polar V800,
USA) and stride sensors (Polar S3 BlueTooth Stride Sensor, USA). The heart rate sensor
was placed at the center of each participant’s chest using a heart rate belt (Polar H10), and
the step sensor was fixed on a pair of shoes, with shoelaces, to monitor their heartbeat
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Figure 1
3MPKS heart rate model and corresponding step frequency.
Full-size
DOI: 10.7717/peerj.10831/fig-1
and number of steps taken. After the devices were worn, we measured the midpoint of
the line connecting the anterior epicondyle to the midpoint of the sacrum. We marked
the midpoint on the wall using colored tape as a reference for the height at which the
knee should be lifted to when stepping. After the test started, the participants followed the
appropriate rhythm and were required to lift their knee to the marked height at each step.
The participants began the test at a pace of 80 spm (steps per minute), which increased
by 16 spm every 30 s in six stages. The participants walked in stages 1 to 4 and had to
perform stationary running in stages 5 and 6 (Fig. 1). We stopped the exercise if the
participants could not achieve the requisite knee height or rhythm for 30 s. For their safety,
the participants were asked to relax at a step rate of 80 spm in the first 30 s before resting
in a standing position. We recorded the participants’ heart rate during the exercise, at the
end of the exercise, and 1 min after the end of the exercise. Thirty-four participants were
excluded because (1) their heart rate data were missing, (2) their heart rate was 0, (3) they
did not maintain the requisite step frequency or knee height for 20 consecutive seconds,
(4) they failed to complete the step test within the requisite duration, and (5) they were
suspected of having heart arrhythmia. Potential predictor variables for the results of the
3MPKS test were based on per-second heart rate data collected during the test. The data
included heart rate at the beginning as well as at the first, second, third, and fourth minutes,
denoted by HR0, HR1, HR2, HR3, and HR4, respectively, and were used for subsequent
analysis.
Statistical analyses
To construct and subsequently evaluate a model for estimating relative oxygen uptake, we
divided the full sample set (n = 166) into a 75% training sample set (n = 124) and 25% test
sample set through simple random sampling. We analyzed the descriptive statistics for the
main parameters, for the whole sample, and for the two subsamples.
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Development of prediction model
Using Pearson correlation coefficients, we examined the relationship between the predicted
and actual relative oxygen uptakes. Multiple regression analysis was used to construct a
method for selecting which variables to include in the model for predicting relative oxygen
uptake. Through a backward-selection regression approach, the initial model included
all possible predictors, including sex (men = 1, women = 0), age, BMI, BF%, HR0, HR1,
HR2, HR3, HR4, △HR0 − HR1, △HR1 − HR2, △HR2 − HR3, △HR3 − HR0, and
△HR3 − HR4. Additionally, we constructed a BMI model and BF% model to predict body
composition. The goodness of fit and precision of the regression equations were evaluated
using the multiple coefficient of determination (R2), absolute standard error of estimate
(SEE), and relative SEE (%SEE).
To construct an accurate regression model, the regression assumptions were verified.
We conducted a Kolmogorov–Smirnov test to examine the normality of the residuals, and
we calculated the variation inflation factor (VIF) to check for multicollinearity.
All statistical analyses were performed using SPSS version 20 (IBM, USA). Statistical
significance was indicated by an alpha level of 0.05.
RESULTS
The 166 participants had an average age of 41.9 ±9.6 years (range: 22–64 years), and 40%
of them were men. Their mean relative oxygen uptake was 34.45 ±8.69 mL/kg/min. The
training sample and test sample did not differ significantly with respect to their parameter
values (p > 0.05) Table 1.
The test–retest reliability of the 3MPKS test, as evaluated in our laboratory, was excellent:
the intraclass correlation coefficient (ICC) was 0.88 (95% confidence interval [CI]: 0.77–
0.94), and 60 Taiwanese adults tested 1 week apart participated in this evaluation. In
general, good, moderate, and poor reliability levels are indicated by ICC values of >0.75,
0.5–0.75, and <0.5, respectively.
According to the correlation matrix, ˙VO2 max had the strongest correlation with BF%
among all variables (R = −0.662; training data set, n = 124). In addition, ˙VO2 max was
significantly correlated with the heart rate parameters (HR0, HR2, HR3, and HR4), whose
data were collected in the step test. ˙VO2 max was most and least correlated with HR4 (R =
−0.442) and HR3 (R = −0.289), respectively. Despite the high correlation between ˙VO2
max and the heart rate parameters at different stages, the heart rates of the participants
were expected to increase continuously from the first to third minutes of stepping, if
performed properly. An individual’s heart rate typically reaches its peak immediately after
exercise, and it either decreases at 1 min after exercise or does not decrease at all depending
on whether the individual recovers quickly or poorly. Because heart rate is dynamic, to
establish a regression model, we used combinations of heart rate parameters and adopted
the difference between predicted and measured heart rate data at each stage as inputs
(Table 2).
The results of our other cross-validation analyses are presented in terms of CE (Constant
error) values. The absolute CE values for subgroups stratified by sex and age were <1.00
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Table 2
Correlation between ˙VO2max and features in training dataset (n = 124).
˙VO2max
Sex
Age
BMI
BF%
HR0
HR1
HR2
HR3
Sex
0.597**
Age
−0.342**
−0.114
BMI
−0.083
0.334**
−0.160
BF%
−0.662**
−0.491**
0.109
0.448
HR0
−0.317**
−0.242*
−0.101
−0.058
0.227*
HR1
−0.344**
−0.033
−0.283*
−0.039
0.274*
0.69**
HR2
−0.357**
−0.312**
−0.093
−0.005
0.308**
0.592**
0.899**
HR3
−0.289*
−0.254*
−0.21*
0
0.248*
0.525**
0.725**
0.8**
HR4
−0.442**
−0.42**
−0.13
−0.063
0.334**
0.564**
0.57**
0.629**
0.702**
Notes.
BF%, body fat percentage.
**Correlation coefficient is significant(p < 0.001).
*Correlation coefficient is significant(p < 0.05).
for the two models (both in training and testing data sets, n = 124 and 42). Regarding the
subgroups stratified by ˙VO2 max, the CE values were negative in low-fitness, middle-fitness
subgroups in training data set and low-fitness in testing data set. On the other hand, the
CE values were positive in high-fitness in all two data sets (Table 3).
Figures 2 and 3 present the Bland–Altman plots produced by the BF% and BMI models
based on the testing data set (n = 42). As evident in the plots, the differences between the
predicted and measured data were within an acceptable range. The mean error of the BF%
model was −0.36 mL/kg/min (95% CI [−12.38–11.98]). For the BMI model, the mean
error was 0.4 mL/kg/min (95% CI [−12.35–13.58]). In the BF% and BMI models, the
errors for three and two participants, respectively, fell outside the 95% CI.
We constructed a model to predict relative oxygen uptake by using multiple regression
analysis. The parameters selected for the BF% model were sex, age, BF%, HR0, 1HR3 −
HR0, and 1HR3 − HR4; R2 = 0.624 and SEE = 4.982 (training data set, n = 124) (Fig. 4).
The parameters selected for the BMI model were sex, age, BMI, initial heart rate, 1HR3 −
HR0, and 1HR3 − HR4; R2 = 0.567 and SEE = 5.153 (training data set, n = 124) (Fig. 5).
We used BF% as a predictor of body composition; it is more accurate relative to BMI, which
is calculated using only height and weight (Table 4). Table 4 presents the cross-validation
results for the predicted residual sum of squares (PRESS) statistics (R2p = 0.64 and SEE
p = 4.84), which demonstrated minimal shrinkage in the accuracy of the regression model.
All regression assumptions were satisfied in our ˙VO2 max prediction models. Specifically,
the Kolmogorov–Smirnov test indicated normality in the residuals (p > 0.05). No
pattern was determined in the scatter plot between the residuals and predicted ˙VO2
max. Multicollinearity was absent among the predictor variables: the VIF ranges for the
BF% and BMI models were 1.09–1.49 and 1.10–1.40, respectively; multicollinearity is
absent if VIF ≤ 10 (O’brien, 2007).
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Table 3
Measured versus predicted ˙VO2max constant error (CE) and standard deviations (SD) for
subgroups of the training dataset and testing dataset.
Subgroup
n(%)
BF% model(%)
BMI model(kg m−2)
CE
SD
CE
SD
Training set(n = 124)
Sex
Female
80(64.5)
−0.01
3.95
0.01
4.45
Male
44(35.5)
−0.02
6.23
0.01
5.99
Age
<40 years
48(38.7)
−0.34
4.72
−0.49
4.94
40–50 years
44(35.5)
0.21
5.35
0.22
5.24
≥50 years
32(25.8)
0.17
4.46
0.47
4.95
˙VO2max
<29 ml/kg/min
34(27.4)
−2.77
3.19
−3.13
3.66
29–38 ml/kg/min
56(45.2)
−0.22
4.46
−0.22
4.60
≥38 ml/kg/min
34(27.4)
3.09
5.18
3.51
4.75
Testing set(n = 42)
Sex
Female
21(50)
−0.15
5.85
−0.89
5.84
Male
21(50)
0.87
6.82
0.08
7.62
Age
<43 years
24(57.1)
0.67
6.22
−0.07
6.8
≥43 years
18(42.9)
−0.05
6.55
−0.85
6.79
˙VO2max
<35 ml/kg/min
22(52.4)
−2.88
4.95
−3.86
5.31
≥35 ml/kg/min
20(47.6)
3.93
5.73
3.39
6.11
Figure 2
Bland Altman plot, including limits of agreement, for predicted and measured ˙VO2 max
(ml/kg/min) of BF% model by testing dataset (n = 42). Black line mean difference. Dashed line±1.96
×SD.
Full-size
DOI: 10.7717/peerj.10831/fig-2
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Figure 3
Bland Altman plot, including limits of agreement, for predicted and measured ˙VO2 max
(ml/kg/min) of BMI model by testing dataset (n = 42). Black line mean difference. Dashed line±1.96
×SD.
Full-size
DOI: 10.7717/peerj.10831/fig-3
Figure 4
BF% model for testing test (n = 42).
Full-size
DOI: 10.7717/peerj.10831/fig-4
DISCUSSION
This study developed a practical and easy-to-use model for predicting ˙VO2 max in
Taiwanese people. We recruited 166 Taiwanese adults and constructed and then evaluated
a prediction model. Our results suggest that age, sex, and BF% as well as heart rate during
the step test are excellent predictors of ˙VO2 max. We also developed a novel 3MPKS test.
Nes et al. (2011) conducted large-scale ˙VO2 max tests on 4,260 participants. They
developed a nonexercise model and determined four variables (age, waist circumference,
physical activity, and resting heart rate) to be excellent predictors of ˙VO2 max; for their
model, R2 was 0.61 and SEE was 5.70 mL/kg/min for men, and R2 was 0.56 and SEE was 5.14
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Figure 5
BMI model for testing (n = 42).
Full-size
DOI: 10.7717/peerj.10831/fig-5
Table 4
Estimation of ˙VO2 max through multiple regression model (n = 124).
BF% model (%)
BMI model (kg m−2)
˙VO2max
(ml kg−1 min −1)
Coefficients
β
p value
Coefficients
β
p value
Constant
72.334
.000
82.387
.000
Sex
(0=women, 1=men)
4.366
0.258
.000
9.338
0.551
.000
Age(yr)
−0.261
−0.302
.000
−0.327
−0.378
.000
Body composition
−0.448
−0.421
.000
−0.718
−0.346
.000
HR0
−0.134
−0.214
.001
−0.171
−0.273
.001
1HR3- HR0
−0.082
−0.136
.041
−0.099
−0.163
.017
1HR3-HR4
0.073
0.124
.048
0.081
0.139
.032
R2
0.624
0.567
SEE
4.982
5.153
SEE%
14.46
14.96
PRESS
2904.186
3107.325
SEEp
4.840
5.006
R2p
0.644
0.619
Notes.
BMI, body mass index; BF%, body fat percentage; β, standardized regression weights; SEE, standard error of estimate;
SEE%, SEE / mean of measured ˙VO2 max ×100.; PRESS, predicted residual error sum of squares; SEEp, PRESS standard er-
ror of estimate; R2p, PRESS squared multiple correlation coefficient.
mL/kg/min for women. Jackson et al. (2012) conducted a 27-year study that examined the
˙VO2 max of 11,365 people and used variables such as age, sex, BMI, waist circumference,
resting heart rate, physical activity, and smoking habits to estimate CRF; for their model,
R was 0.78–0.81 and SEE was 5.3–5.6 mL/kg/min. Although the nonexercise model is
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an excellent predictor of ˙VO2 max, its SEE is generally higher than those of submaximal
exercise models; compared with nonexercise models, our developed BF% model had better
predictive performance and a lower standard error of estimate (R2 = 0.624 and SEE =
4.982). Abut, Akay & George (2016) reported that (1) when perceived functional ability
(PFA) was used as the sole predictor of ˙VO2 max, an R value of 0.73 and a higher RMSE
of 6.08 mL/kg/min could be obtained; (2) when submaximal ending speed (SM-ES) of
a treadmill was used as the sole predictor, the R value increased to 0.82 and the RMSE
was relatively low at 4.99 mL/kg/min; and (3) when both PFA and SM-ES were used as
predictors, the R value was 0.89 and RMSE was 4.14 mL/kg/min. These findings indicate
that predicted values of ˙VO2 max that are based only on participant self-reports are likely
to deviate from their measured values. Although predictive performance is ostensibly
improved when motion is added to the prediction model, the cost of exercise tests due to
the use of this method restricts its application in large-scale tests.
Several studies have developed simple models involving submaximal motion. Lee et al.
(2019) investigated 568 adults and used sex, age, height, and weight and inverse recovery
heart rate during a YMCA step test to predict ˙VO2 max; for their model, R was 0.78 and
SEE was 4.74 mL/kg/min. The duration of their exercise test plus recovery time was only
4 min, and they used exercise-induced heart rate as a predictor; their results are similar to
ours. Their study provided a simple and practical method for simultaneously estimating
CRF in many Korean adults. Cao et al. (2013) used age, sex, and physical composition as
well as stepping distance over a 3-min period to develop a set of prediction methods. They
determined that BF% (a measure of body composition) was a better predictor than BMI
(R2 = 0.83 vs. 0.80, SEE = 4.565 vs. 5.037 mL/kg/min). In contrast to our method, their
method has the considerable advantages of a shorter testing time of 3 min and the fact that
participants need not wear a heart rate monitor. However, their test is limited by its need
for a 20-m open space. Similarly, we found that sex, age, and BF% as well as heart rate
during the 3MPKS test yielded the best prediction performance (R = 0.79, SEE = 4.982
mL/kg/min). Because BMI is based on only height and weight and may not accurately
represent the body characteristics of participants, BMI is a less accurate predictor than
BF%.
Most submaximal exercise models proposed by previous studies involve a fixed-height
step test. However, the height and leg length of participants when standing may affect
their physiological response in the step test (Culpepper & Francis, 1987). Relative to their
European counterparts, Asian adults have shorter heights and leg lengths when standing
(Stanfield et al., 2012). Therefore, differences in heart rate and oxygen consumption
potentially affect the model’s prediction. The 3MPKS test employs the knee-ups and step
test to measure the physical fitness and cardiopulmonary endurance of older adults (Rikli
& Jones, 2001). In the test, participants must execute tasks at various knee heights based
on their thigh length, and individualized exercise testing goals are provided. Moreover,
most field tests involve average speed tests, such as step tests and running. In running
tests specifically, if the distance is used as the capacity index but the speed or frequency of
exercise is not progressively increased, participants may exercise intensely at the beginning
of the test (i.e., run at a higher speed). However, due to the lack of appropriate speed
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allocation, decremental loading occurs in participants as their physical strength decreases.
The difficulty of diagnosing potential heart diseases in advance increases the risk of sudden
death during running tests. To the best of our knowledge, research has not been conducted
on the ethics of running tests. Most previous studies have investigated the rate of sudden
death among athletes in long-distance competitions. However, cases of sudden cardiac
death occur frequently worldwide during running tests, and the principle that physical
activities ought to be progressive must be adhered to in physical fitness tests. Our research
method used body composition and heart rate as variables. The advantages of the 3MPKS
test are that it does not require a step-up box and is not subject to venue restrictions.
These make the 3MPKS test accord with the principle that physical activities ought to be
progressive, thus making it safer.
Considering the immediacy of heart rate measurement and that of confounding factors,
we used a chest-worn heart rate monitor in the experiment. Although the requirement
of heart rate monitoring constitutes a disadvantage for the 3MPKS test, it is ameliorated
by the prevalence of low-cost wearable devices. More comfortable than the chest-worn
heart rate belt, products that combine running clothes with heart rate belts have also
appeared on the market. Research has also suggested a high correlation between the heart
rate measurements of various types of optical devices and chest-worn heart rate belts (Stahl
et al., 2016). Therefore, when conducting a large-scale cardiorespiratory general test, the
use of easily wearable optical heart rate monitors can be considered. The whole-range
monitoring of heart rate can also considerably improve test safety in a field study. Notably,
through whole-range monitoring, we found that one research participant was likely to
have an unknown heart disease. We then terminated the experiment for the participant
and recommended that the participant seek medical treatment. This example illustrates a
side benefit of CRF tests.
In our research model, heart rate during stepping at each stage was used as the main
variable. Therefore, the test may be unsuitable for individuals who have psychological
sensitivity or dysautonomia or who are taking medication. Furthermore, because our
participants were adults between 20 and 64 years old, it was unclear whether our 3MKPS
test is appropriate as a physical fitness and cardiorespiratory test for students (7–23 years
old) and older adults (≥65 years old). Future research must include samples with greater
diversity in age and ethnicity to assess whether our 3MKPS test can be applied to the wider
global population.
CONCLUSION
This study, involving Taiwanese adults, constructed and verified a model for predicting
˙VO2 max, which is used to measure CRF. This model comprises the predictors sex, age,
and body composition as well as heart rate changes during a step test. Our 3MKPS test has
three advantages: it has a short testing time of 4 min, it has no venue limitations, and it does
not require a step box. Furthermore, measurements can be taken for many participants
simultaneously by asking them to wear a heart rate monitor and move according to a
beat. Our model can also be applied to large-scale epidemiological research. In future
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applications, the model can be combined with smartwatches or used to develop health and
well-being apps, helping users to track their ˙VO2 max. Future research can further explore
the correlation between various diseases and ˙VO2 max, as predicted using our simple and
reliable method for measuring CRF.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This research was supported by Research Grants from Taipei Medical University (no.
TMU105-AE1-B06) and the Sports Administration, Ministry of Education, R.O.C. for the
Comprehensive Research for the Industrial Technology Research Institute’s Technology
Fitness Program (no. J4653H1A20). The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Research Grants from Taipei Medical University: no. TMU105-AE1-B06.
Sports Administration, Ministry of Education, R.O.C. for the Comprehensive Research
for the Industrial Technology Research Institute’s Technology Fitness Program: no.
J4653H1A20.
Competing Interests
The authors declare there are no competing interests.
Author Contributions
• Yu-Chun Chung conceived and designed the experiments, performed the experiments,
analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the
paper, and approved the final draft.
• Ching-Yu Huang conceived and designed the experiments, analyzed the data, authored
or reviewed drafts of the paper, and approved the final draft.
• Huey-June Wu conceived and designed the experiments, performed the experiments,
authored or reviewed drafts of the paper, and approved the final draft.
• Nai-Wen Kan and Chi-Chang Huang performed the experiments, authored or reviewed
drafts of the paper, and approved the final draft.
• Chin-Shan Ho performed the experiments, analyzed the data, authored or reviewed
drafts of the paper, and approved the final draft.
• Hung-Ting Chen conceived and designed the experiments, analyzed the data, prepared
figures and/or tables, authored or reviewed drafts of the paper, and approved the final
draft.
Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
Institutional Review Boards (IRBs) of the Industrial Technology Research Institute and
of Taipei Medical University (IRB No: N201808055).
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Data Availability
The following information was supplied regarding data availability:
Raw measurements are available in the Supplemental Files.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.10831#supplemental-information.
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Predicting maximal oxygen uptake from a 3-minute progressive knee-ups and step test.
Chung, Yu-Chun,Huang, Ching-Yu,Wu, Huey-June,Kan, Nai-Wen,Ho, Chin-Shan,Huang, Chi-Chang,Chen, Hung-Ting