Jurov and Demšar BMC Pediatrics(2024) 24:550
https://doi.org/10.1186/s12887-024-05013-5

BMC Pediatrics

RESEARCH

Open Access

Factors affecting maximal oxygen uptake
in prepubertal children: a systematic review
and meta-analysis
Iva Jurov1* and Jure Demšar2,3
Abstract
In pre-pubertal children the factors affecting maximal oxygen uptake have yet to be fully understood. Therefore,
the purpose of this analysis is to present cardiorespiratory fitness in prepubertal boys and girls and to determine
if there are any differences based on sex, exercise testing modality or if maximal or peak oxygen consumption
metrics are used. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were
followed. For statistical analysis, multilevel models grounded in Bayesian principles were used. Selected studies
obtained: maximal effort during the test, peak or maximal oxygen consumption (V̇O2) values, boys and girls (sexspecific groups only) age under 11, cycle ergometry or treadmill, pre-intervention or no intervention data. In
boys using cycle ergometry, 118 studies were included in the analysis, in boys using treadmill 115, in girls using
cycle ergometry 83 and in girls using treadmill testing 95 study entries were included. As children get older, theircardiorespiratory fitness increases (P ≈ 100%). Studies with participants having smaller body mass have higher V̇O2
relative to body mass values (P ≈ 100%). Boys have higher V̇O2 values than girls (P ≈ 100%). Studies using treadmill
reported higher values than those using cycle ergometer (P ≈ 100%). Regarding the influence of measurement
method (max vs. peak) on V̇O2 values we did not find significant differences. In conclusion, we present reference
values for cardiorespiratory fitness in prepubertal boys and girls using cycle ergometry or treadmill. Prepubertalboys have higher cardiorespiratory fitness than girls and using treadmill testing might be a preferred method
to cycle ergometry, especially in older children. Maximal or peak oxygen consumption metrics might be usedinterchangeably in prepubertal children.
Keywords Exercise test, Physical fitness, Physiology, Sports medicine, Child health*Correspondence:
Iva Jurov
iva.jurov@gmail.com
1
Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
2
Faculty of Computer and Information Science, University of Ljubljana,
Ljubljana, Slovenia
3
Department of Psychology, Faculty of Arts, University of Ljubljana,
Ljubljana, Slovenia© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you
give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified thelicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to thematerial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation
or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by-nc-nd/4.0/.

Jurov and Demšar BMC Pediatrics(2024) 24:550

BackgroundPhysiological, anatomical and psychological transformations due to growth, maturation and development affectthe physiology and the physiological response to exercisethat occur during childhood [1]. Cardiorespiratory fitness (CRF) is traditionally recognized as an important
health marker in adults, but its importance is gaining
more attention in children as well [2]. CRF in children isassociated with traditional and emerging cardiovascularrisk factors, with total and abdominal adiposity, mental
health, and also with a higher academic performance [3].
CRF reference values have been explored in youths [4],however, for prepubertal children less is known and there
are no clear measured values for CRF that could be usedin clinical setting. One of the emerging normative data
on CRF in children was presented in a paper by Griffith
et al., who analyzed 2025 pediatric CRF in children aged
6–18 years with no underlying heart disease [5]. They
reported that CRF was generally higher in males whencompared with females in the same age groups. Burstein
et al. also included prepubertal children in their research(patients age 6 to 22) with aim to observe changes in CRF
during COVID-19) pandemic [6]. Blais et al. reviewed
pediatric CPET references values published between
1980 and 2014 and concluded that high-quality reference values with adequate adjustment for confounding
variables are needed in order to optimize CPET’s specificity and sensitivity to detect abnormal cardiopulmonary
response to exercise [7]. Most of studies in prepubertal
children focus on a wider age group and only include
them or represent CRF as part of a different aim of thestudy. A novel contribution would be to represent CRF in
prepubertal children specifically and to evaluate any gender differences based on reported values in the literature.
Traditionally, standardized cardiopulmonary exercise
testing (CPET) is used to determine CRF. CPET has been
gaining importance as a method of functional assessment worldwide in cardiology and also for exercise guidance and other disease progress monitoring. In anotherwords, CPET is not only used in setting a diagnosis but
also for determining prognosis, for monitoring therapies or interventions and for safe exercise participation[8, 9]. As well as in adults, in children test performance
will depend on exercise equipment and usually treadmill
or cycle ergometry are used [10]. Using cycle ergometryin adults typically results in lower CRF values than using
a treadmill where upper body movement increases oxygen consumption and this has also been demonstrated
in children [11, 12]. In general, using treadmill for CPET
is advised especially for children under 7, as children
around 3 years of age first learn to walk and then run ona treadmill [13]. In addition, using treadmill is usually thepreferred way in individuals as it a more familiar activity [10]. Another advantage of treadmill is that using a

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cycle ergometer requires a constant pedaling rate, whichis harder to achieve in younger children. This is particularly an issue with a mechanically braked ergometer.
Also, leg fatigue seems to be more limiting on a bicycle
than on a treadmill [14]. In contrast to cycle ergometry,
speed on treadmill can be precisely controlled. Treadmillis also more suitable for children below 125 cm in stature, since cycle ergometers are not generally adjustable
for small children [15]. Treadmill testing is, however, lesssafe and is not portable. Clear differences in CRF basedon equipment used still need to be explored in prepubertal children.
In addition, there are no clear exercise termination
criteria for CPET in children. Achieving maximal oxygen consumption (V̇O2max) during CPET requires measuring a plateau during maximal exertion. The use of
secondary criteria to verify a maximal effort in young
people during ramp cycling exercise may result in theacceptance of submaximal V̇O2max. In children, a “true”
V̇O2max could be confirmed using a supramaximal testing as recommended by Barker et al. [16]. Another metric of determining maximal exertion is the peak oxygen
consumption (V̇O2peak), which is the highest V̇O2 value
during CPET. V̇O2max and V̇O2peak are often used interchangeably. In adults, some studies showed that thesemeasures are indistinguishable in well-motivated adults[17, 18]. In youth, there is still no consensus whether
V̇O2peak is indeed a maximal index of an individual’s aerobic fitness [19]. In prepubertal children, there is no dataon possible differences that exist in V̇O2max and V̇O2peak.
Despite almost 80 years of reporting CRF of youth,
maximal values in prepubertal children are still notunderstood completely [19]. The aim of this meta-analysis is set to examine CRF in prepubertal boys and girls
and to evaluate any gender differences based on reported
value in the literature. Secondly, the aim was to compare
CRF values based on equipment used – treadmill or cycle
ergometry. Another purpose of the study is to develop as
web-based program for assessing probability associatedwith a given CRF by sex and mode of exercise. Finally,V̇O2max and V̇O2peak methods were compared to find if
any of them present advantage in CRF measurement.

Materials and methods
DesignThe Consideration of Population, Intervention, Comparator, Outcomes and Study design (PICOS) framework
was used:
Population

Included participants had to fit the following criteria: (1)studies had to have a mean age of 11 years old or less (2)did not have any cardiovascular disease, pulmonary diseases (with the exception of asthma), morbid obesity,

Jurov and Demšar BMC Pediatrics(2024) 24:550

developmental disabilities, muscular dystrophies, andwere otherwise healthy, (3) were not injured. Overweightparticipants were included in the analysis. Children withasthma were also included as the literature shows they
have comparable physical activity levels with children
without asthma [20].
Intervention

Regardless of the intervention seen in many of the original articles, only pre-intervention data were used.
Dependent variableV̇O2max and V̇O2peak metrics were compared. In addition, we compared studies based on the year and location(USA versus non-USA countries).
Outcomes

The main outcomes were V̇O2max and V̇O2peak metrics
measured with treadmill or cycle ergometer incremental
testing. The only values included were those that werereported in mLkg−1min−1, meaning they were reported
relative to body mass. Using relative CRF removes theimpact of mass as the absolute value is divided by it.
Study design

Our analyses facilitated data from studies that matched
the following criteria:
1. A paper representing the study was published in apeer-reviewed journal.
2. Within the paper we were able to find the following
data:a. Mean V̇O2max or V̇O2peak.
b. The standard deviation of V̇O2max or V̇O2peak.
c. Mean age of the participants in the study.
These measures resulted with article data on children(girls and boys) that used various testing methods formeasuring CRF. Next, all articles reporting CRF values ofboth sexes combined were excluded from further analysis. The aim was to create sex specific values, that could
be later compared.
In addition, analysis excluded articles with graphicalresults only, field studies, or other non-standardized protocols and types of incremental tests.
A systematic electronic literature search was conducted in PubMed database until 2019 using key searchwords ((children) AND ((oxygen consumption) OR (aerobic power) OR (peak oxygen consumption) OR (V̇O2)
OR (V̇O2max) OR (V̇O2peak)). During the first search,
potential articles included boys and girls, that performed
incremental tests with cycle ergometry and treadmills.
All potential articles up to 2019 were independently hand

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searched by two researchers. In 2022, the same criteria
were used to conduct an additional search from 1. January 2019 to 31. March 2022 (Fig. 1). PRISMA guidelines
were used. Data on prepubertal boys using cycle ergometry have been published and presented with different
analysis elsewhere [21]. In this meta-analysis, the focus
was to explore any differences in aerobic power based onsex, exercise testing modality or if maximal or peak oxygen consumption metrics are used.
A flow diagram that visualizes the study selection process can be found in Fig. 1.
Statistical analysis

Bayesian statistics was used to analyze the results. Bayesian methodology offers a modern, more flexible, andintuitive alternative to frequentists statistics [22–25]. Allanalyses were conducted through Stan – a state-of-theart platform for executing modern Bayesian statisticalanalyses [24]. In this work we were mainly interested in
how various variables are correlated with measurementsof VO2 relative to body mass. Thus, our response (dependent) variable was the VO2 measurement, while the following variables were explanatory (independent):– 	 mean age of study participants,– 	 mean body mass of study participants,– 	 year of the study,– 	 measurement metric (peak or max methodology),– 	 country of participants (USA or other),– 	 sex (male or female),– 	 test modality (cycle or treadmill).
To analyze how VO2 correlates with mentioned explanatory variables, we used Bayesian multiple linear
regression:y | α, β, σ ∼ Normal (α + βX, σ ) ,β ∼ Cauchy (0, 2.5) .
We put the standard weakly informative prior on the
regression’s beta coefficients ( β ) [24], we did not put
any prior on the intercept ( α ) meaning that the default,
non-informative prior was used in this case. X denotes amatrix containing values of explanatory variables, y is avector containing values for the response variable and σ
is the variance parameter.
In the second analysis, where we analyzed how age
along with sex and test modality influences the probability of measuring the highest (max) or lowest (min) VO2,
we fit the above model independently for each of the sex/
test modality combinations (boys on a cycle, boys ona treadmill, girls on a cycle, girls on a treadmill) where
age was the only explanatory variable. We then used theJurov and Demšar BMC Pediatrics(2024) 24:550

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Fig. 1 PRISMA flow chart of study selection process for articles reporting on CRF in prepubertal children

model to calculate mean VO2 values as the age of participants changes and compared those to get the reported
probabilities.
In all analyses, we used the posterior parameters of ourmodels to estimate certainty of our claims. With Bayesianstatistics we can directly quantify the probability (P) of aparticular research question, which arguably gives us themost direct and intuitive measure of how certain we areabout a claim we are making [22, 23, 25]. In this work weare reporting P as a percentage, meaning that the value of
100 represents the highest possible certainty, labeled asP ≈ 100%. As we work with probabilities in percentages,

100 - P represents the probability that the opposite of aparticular claim is true. We used Monte Carlo standard
error (MCSE) measure to quantify uncertainty. MCSE
is simply standard error adjusted for autocorrelationof samples that are generated by algorithms for Bayesian statistics. For all practical purposes MCSE should
be interpreted the same as standard error. Since in allcases the uncertainty was very small/insignificant (< 1%)
we decided to omit it in order to simplify the text andincrease the readability of our results and analyses.

Jurov and Demšar BMC Pediatrics(2024) 24:550

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Table 1 Cardiorespiratory fitness values (mlmin− 1kg− 1) based on studies for children under 11 years old (mean) (M = male, F = female)MFage (years)
cycle ergometer
treadmillcycle ergometer
treadmill

4–5
44.3
49.0
40.4
44.8

5–6
44.6
49.3
40.7
44.6

6–7
45.0
49.6
41.0
44.5

7–8
45.4
49.9
41.3
44.3

8–9
45.8
50.2
41.6
44.1

9–10
46.1
50.4
41.9
44.0

10–11
46.5
50.7
42.2
43.8

SD ( σ )
6.99
6.01
5.81
6.65

Columns 2–8 denote the mean for a given age, while the last column denotes the standard deviation (σ in our statistical model) around that mean

Table 2 Probabilities for each age group that a given combination of the test modality and the sex of children yields the highest
cardiorespiratory fitnessMF

age (years)
cycle ergometer
treadmillcycle ergometer
treadmill

4–5
13%
80.9%
2%
4.1%

5–6
9.1%
89.3%
0.8%
0.9%

EthicsThis is a review of available data in databases. Institutional Ethics Committee has confirmed that no ethicalapproval is required.

Results
In boys using treadmill, 115 data-points from 82 studies
fit all our criteria (Table S1, Additional file 1). 118 datapoints from XY studies were found for boys using cycle
ergometry [21]. In prepubertal girls, 83 data-points from56 studies fit inclusion criteria using cycle ergometry(Table S2, Additional file 1) and 95 data-points from 59
studies fit the criteria for the treadmill (Table S3, Additional file 1). Note that a single study can be included inseveral cases as sometimes a single study can have a single data point (e.g., the study measured only girls using
a treadmill), while other times it might have several data
points (e.g., the study measured both boys and girls, or itmeasured both with a treadmill and a cycle ergometer).
Summary CRF values based on studies used in our work
are presented in Table 1.
Table 2 shows the probability that a given combination of test modality and sex is the highest for a given
age group. This table shows that with children in theage group 6–7 and older, we are very confident that thehighest cardiorespiratory fitness measurements will beacquired with boys using the treadmill (P = 95.5% for
the 6–7 age group, P = 99.1% for the 7–8 age group, andP ≈ 100% for all older age groups). For the two youngest
age groups (4–5 and 5–6) it is still most likely that boys
using a treadmill will yield the highest cardiorespiratory
fitness measurements, however the probabilities here arelower (80.9% and 89.3%, respectively).
Influence of study parameters on measured V̇O2

Our results suggest that studies containing older participants have higher V̇O2 relative to body mass values(P ≈ 100%). The opposite holds for weight. Studies with

6–7
4.4%
95.5%
0%
0.1%

7–8
0.9%
99.1%
0%
0%

8–9
0%
100%
0%
0%

9–10
0%
100%
0%
0%

10–11
0%
100%
0%
0%

participants having smaller body mass have higher V̇O2
relative to body mass values (P ≈ 100%). Studies containing boys have higher V̇O2 values than those with girls(P ≈ 100%). Similar holds for studies made on a treadmill in comparison to those made on a cycle ergometer (P ≈ 100%). Our data also suggest that measured
V̇O2 seems to be declining through years (P = 94.3%).
This might suggest that kids in the past were fitter thankids nowadays in this aspect given the assumption that.
Regarding the influence of the measurement method(max vs. peak) on V̇O2 values we did not find significant
differences. Data suggests that V̇O2max measurements arehigher than V̇O2peak measurements, however, our certainty in this claim is relatively low (P = 83.1%), meaningthat based on these results we cannot claim with certainty that that V̇O2max values are different than V̇O2peakvalues. Similar conclusion can be made for location of
the study, here USA children might have lower V̇O2
compared to other countries of the world, but again theprobability that this is true is not that high (P = 83.7%).
Figure 2 visualizes these findings.
Comparison of V̇O2 measurements with age

In the second part of our analysis, we were interested
in analyzing how sex and type of incremental test (cycle
ergometer/treadmill) device (test modality) influence theprobability of measuring the highest/lowest (max/min)V̇O2 as the age of participants increases. For the youngest
age group (4 years), the probability of getting the highest
V̇O2 measurement is the highest with boys on a treadmill(P = 77.5%), followed by boys on a cycle (P = 12%), girls ona treadmill (P = 7.2%), and girls on a cycle (P = 3.2%). For
the oldest age group (11 years), we are extremely confident (P ≈ 100%) that studies with boys on a treadmill will
give the highest V̇O2 measurements. Through all age
groups it is the most likely that boys on a treadmill will
generate the highest V̇O2 values.

Jurov and Demšar BMC Pediatrics(2024) 24:550

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Fig. 2 A visualization of how study parameters influence measured V̇O2. Visualized are distribution of beta coefficients from the Bayesian multiple linear
regression model. We are extremely confident that age, weight, sex and test modality (cycle or treadmill) have a significant impact on V̇O2. It seems likeV̇O2 is higher in more recent studies, but the confidence in this claim is lower. Based on our data and analysis, we cannot confidently claim that country(USA or other) and measurement method (max or peak) have a significant influence on measured V̇O2

For the youngest age group (4 years), the probability of getting the lowest V̇O2 measurement is the highest with girls on a cycle (P = 69.1%), followed by boys ona cycle (P = 20.6%), girls on a treadmill (P = 10.2%), andboys on a treadmill (P = 0.2%). For the oldest age group(11 years), girls on cycle will again most likely give thelowest V̇O2 measurements (P = 80.3%), followed by girls
on a treadmill (P = 19.7%). The other two groups are
extremely unlikely to generate the lowest V̇O2 measurement (P ≈ 0%). Through all age groups it is the most likelythat girls on a cycle will generate the lowest V̇O2 values.
These findings are visualized on Fig. 3.
Furthermore, we executed a similar analysis, where
we grouped the results by test modality and investigatedwhich how probabilities of highest measurements varywith age and sex. With the youngest kids on the cycle,
we are more likely to get the highest measurement if
our study is composed of males (P = 75.4%), this probability grows with age and is at 99.7% in the oldest age
group. For studies using the treadmill, this difference iseven more prominent, with youngest kids, males areagain more likely to give highest V̇O2 measurements(P = 90.9%), this probability grows to approximately 100%in the oldest age group. See Fig. 4 for a visualization ofthis analysis.

We repeated this analysis, but this time we grouped theresults by sex and analyzed how probabilities of highest
measurements vary with age and test modality. With theyoungest boys we are more likely to get the highest measurement if our study is executed on a treadmill (P = 87%),
this probability grows with age and is 99.9% in the oldest
age group. With the youngest girls, we are more likely to
get the highest measurement if our study is executed on atreadmill (P = 82.3%), this probability initially grows withage and then seems to decline a bit with teenagers. In ouroldest group, treadmill will give higher measurements
than cycle with a probability of 80.4%. To get additional
insight whether this is just an anomaly in our data or atrue trend, we would need to gather more data and also
expand the data so it would encompass older populations from 11 years. This part of our study is visualized
on Fig. 5.
A web application for calculating V̇O2 percentiles

As a part of this study, we also developed a web application where anyone can enter information about a V̇O2
measurement on a child. The application will then use
statistical models based on the data gathered for the purposes of this study to calculate on which percentile thekid lies given the provided data. Note that this application is also capable of working with non-normalized V̇O2

Jurov and Demšar BMC Pediatrics(2024) 24:550

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Fig. 3 Probabilities that a certain combination of sex and test modality give the highest (max) or lowest (min) V̇O2 measurement per age groups. In all
age groups the probability that studies with boys on a treadmill will give the largest V̇O2 measurement is the highest. Analogously, probabilities that girls
on a cycle will give the lowest V̇O2 measurement is the lowest in all age groups

Fig. 4 Probabilities that a study will give the highest V̇O2 measurement per age group and sex. This visualization shows that over all ages groups it ismore likely that male studies will have higher V̇O2 than female studies. In younger populations there is a non-significant chance that a female study might
give the highest V̇O2 (24.6% on the cycle and 9.1% on the treadmill). These probabilities decline with age, with older populations male studies will almost
always generate higher V̇O2 measurements than female onesJurov and Demšar BMC Pediatrics(2024) 24:550

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Fig. 5 Probabilities that a study will give the highest V̇O2 measurement per age group and test modality. This visualization shows that over all ages groups
it is more likely that cycle studies will have lower V̇O2 than treadmill studies. In younger populations there is a non-significant chance that a cycle study
might give the highest VO2 (17.7% for female studies and 13% for male studies). These seem to decline with age, more so in the case of male studies

measurements, we gathered that data as well, but decided
to omit it from this study for brevity’s sake as it is notthat useful. The web application is publicly accessible athttps://demsarjure.shinyapps.io/vo2max/.

Discussion
The results of this meta-analysis show that age is associated with changes in CRF in prepubertal children andolder children seem to have higher values. This is in contrast with youth where CRF seems to be relatively stable.
It has already been shown that during the pediatric years,
CRF is consistent with growth of heart, lungs, and muscle mass, with values greater in boys than in girls [26]. As
commented by Robinson [27], youngest boys are more
likely to be unwilling to work after it ceases to be funcompared to older boys. It could be that younger children
were not as motivated in studies included. Regardless ofthe factors influencing age impact, we suggest age should
be considered in clinical practices when a child’s CRF
is compared to normative values – as both sex and age
seem to have an important impact. Understanding why
prepubertal boys have higher CRF than girls, is, however,more difficult. Armstrong summarizes that the difference
between boys and girls during puberty can be approximately 35% in CRF. Higher muscle mass in boys probably
accounts for much of the sex difference and greater blood
haemoglobin concentration could also have an impact.

However, it is still not clear how why boys’ CRF is higher
than girls’ CRF before puberty [28] when physiological differences are not as prominent as in later years. To
be able to understand the impact of sex on CRF before
puberty, more research needs to be done.
Based on the analysis, children can achieve greater
CRF on a treadmill compared to cycle ergometry. Thisis consistent with literature in adults and wider paediatric population. The main reason for the higher CRF values in children could be that treadmill exercise utilises
the whole body whereas cycle ergometry is restricted to
the lower body – thus more oxygen is needed to sustain
exercise intensity and performance [28–30]. The issues
surrounding protocol selection, equipment, and methods use go beyond the scope of this meta-analysis, butdifferences in cycle ergometry vs. treadmill testing areclear. We would like to suggest treadmill be the preferred
equipment used during incremental tests in this age
group.
The assessment and interpretation of V̇O2max andV̇O2peak are the most researched concepts in exercise
physiology, but still controversial, especially during
growth and maturation [28]. One issue in comparing
V̇O2max and V̇O2peak in adults and children is certainly
which secondary criteria were used, such as respiratoryexchange ratio, % of maximal heart rate, blood lactate
level, perceived exertion, to confirm the maximal values.

Jurov and Demšar BMC Pediatrics(2024) 24:550

Ideally, we would have all or most of these values, but
methodology used in the literature varies greatly. Nevertheless, V̇O2max and V̇O2peak in adults are frequently usedinterchangeably, but is has not been clear if the same ispossible in prepubertal children. Our results support thatnone of these two metrics have an advantage in prepubertal children. Although reported V̇O2max were higher,
the probability of significant difference between them is
not high. A possible explanation for similar values is prepubertal childrens underdeveloped anaerobic potential
and so when they reach oxidative exercise capacity theycannot sustain exercise long enough for a peak to becomepresent (due to a lack of ability to tolerate lactate production). Reaching a plateau in children, which is necessaryfor V̇O2max determination, is not always possible [29].
This could be due to required effort and high motivation that is harder to achieve in children. The possibilityof using V̇O2peak is thus very important. With our results
we support the hypothesis that V̇O2peak and V̇O2max caninterchangeably be used in prepubertal children. This isalso in line with findings in youth [30] and adults [31]. In
addition to V̇O2peak being a simpler metric, this finding
could aid in faster data harmonization that is required forbetter understanding of CRF in prepubertal children. We
believe this is an important finding as there is an urgent
need to harmonize methodology to enable meaningful
cross-study comparisons of CRF in children and youth[19]. Finally, our results suggest that it is highly advisable
to use V̇O2max whenever possible, even for prepubertal
children. However, when it is not possible to measureV̇O2max, we might also get some meaningful results withV̇O2peak.
In the results we also observed that papers publishedin more recent years report on lower CRF values. This
indicates that children might be less fit than in the past,
which is in line with conclusions from Tomkinson andOlds who concluded that there has been a decline in aerobic fitness test performances of Australasian children
and adolescents in recent decades based on different
field-running tests [32]. Also, more studies are found inyouth where CRF is declining in the last 6 decades a well[33]. Since CRF is a predictor of a number of health indicators, this suggests poorer CRF in prepubertal children
could lead to worse health in their future [2]. Hypoactive
children are often at greater risk of preventable healthproblems, such as obesity and cardiometabolic diseases[34, 35]. Obese and overweight children already havepoorer health outcomes in their future, but the findingthat CRF is also poorer among prepuberal children is anadditional reason for concern.
Finally, we also looked into data from USA and “nonUSA” children since the most studies were performed in
the USA. USA children might have lower V̇O2 comparedto other countries of the world, but again the probability

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that this is true is not that high (P = 83.7%). Based on this
it is not possible to draw any relevant conclusions on differences in USA vs. non-USA CRF.

Conclusions
The aim of this analysis is to report on measured CRF
values in the literature for prepubertal children. By
reporting these normative values in the literature, we aim
to raise awareness among clinicians, researchers, sport
practitioners, and other experts in the field of the importance of declining aerobic power otherwise healthy prepubertal children. Reported values can be used in clinicalsetting to determine aerobic power, for setting diagnosis,
to monitor disease progression and to evaluate intervention effects.
Limitations

CRF measurement and reporting depend on various factors. Among them are also the test used and its protocol[36]. There is a pressing need for protocol standardization and data harmonization, but at this point no universal protocol exists for testing prepubertal children.
Protocol nuances used by individual laboratories makesorting them into logical categories very difficult. In thispaper, more protocols were included. The only criteria for inclusion were that the test was incremental andthat standardized equipment was used /cycle ergometeror treadmill). In addition, criteria for determining maximal exertion are not universal [37]. These criteria usually
include: RER (≥ 1.0), no change in V̇O2 with increasing
workload (i.e. a plateau in V̇O2), visible signs of exhaustion, and attainment of age-predicted heart rate, or somepercentage of it [38, 39], but are not identical in all studies involved.
Lastly, children with physical and mental disabilities were not included in this research as their disability
might significantly affect CRF values.
Perspective section

Despite almost 80 years of reporting CRF of youth,
maximal values in prepubertal children are still notcompletely understood [19]. Cardiorespiratory fitness
in prepubertal boys is probably higher than in girls, but
no reference values exist based on measured values. It
is also not clear if test modality (using treadmill or cycle
ergometer) significantly affects VO2 values in prepubertal
children. This analysis provides normative values basedon existing measured VO2 values in the literature. The
reference values can be used in clinical practice and we
also provide a web application (https://demsarjure.shinyapps.io/vo2max/) with which VO2 from a child can becompared to normative values. The assumptions that ourmodels make are valid for ages 4–11 (ages in our dataset)but might not hold for older or younger children. It is notJurov and Demšar BMC Pediatrics(2024) 24:550

advisable to use the tool for children younger than 4 andolder than 11. Based on studies up to 2022, there are no
significant differences in using maximal or peak oxygen
consumption metrics (V̇O2max or V̇O2peak). This is why wepropose that physicians, clinicians and other personnel
who are working with prepubertal children in incremental testing can use VO2max or V̇O2peak interchangeably.
This is especially valuable since achieving VO2max in thisage group can be challenging. Finally, based on thesefindings in prepubertal children both V̇O2max and V̇O2peak
could be used in future research.
Abbreviationsβ	beta coefficients
CPET	cardiopulmonary exercise testing
CRF	Cardiorespiratory fitness
MCSE	Monte Carlo standard error measure
PICOS	Population, Intervention, Comparator, Outcomes and Study designV̇O2max	Maximal oxygen consumption
V̇O2peak	peak oxygen consumption

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12887-024-05013-5.
Supplementary Material 1
Supplementary Material 2
Acknowledgements
We thank TM for his help in preparing this paper.
Author contributions
All authors contributed to the study conception and design. Material
preparation, data collection and first draft were performed by IJ. JD completed
statistical analysis. All authors completed the final draft and commented on
previous versions of the manuscript. All authors read and approved the final
manuscript.
Funding
This research was partially funded by the following grants: Cognitive control
beyond executive function (Slovenian Research and Innovation Agency,
J5-4590), Physiological mechanisms of neurological disorders and diseases(Slovenian Research and Innovation Agency, P3-0338), and Cloud-Based Big
Neuroimaging Data Resource for Harmonized Research on NeuropsychiatricSymptoms in Alzheimer’s Disease (NIH, RF1 AG078304).
Data availability
Data is provided within the manuscript or supplementary information files.

Declarations
Ethics approval and consent to participate
This is a review of available data in databases. Institutional Ethics Committee
has confirmed that no ethical approval is required.
Consent for publication
Non required.
Competing interests
The authors declare no competing interests.
Received: 23 January 2024 / Accepted: 14 August 2024

Page 10 of 11

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