Vol.:(0123456789)
1 3
European Journal of Applied Physiology (2019) 119:1865–1874 
https://doi.org/10.1007/s00421-019-04175-w
ORIGINAL ARTICLE
Quantification of aerobic determinants of performance 
in post‑pubertal adolescent middle‑distance runners
Richard C. Blagrove1  · Glyn Howatson2,3 · Charles R. Pedlar4,5,6 · Philip R. Hayes2
Received: 26 March 2019 / Accepted: 7 June 2019 / Published online: 17 June 2019 
© The Author(s) 2019
Abstract
Purpose The use of oxygen cost ( ̇Oaero) parameters to predict endurance performance has recently been criticized. Instead, it 
is suggested that aerobic energy cost ( ̇Eaero ) provides greater validity; however, a comparison of these quantification methods 
has not previously been made.
Methods Fifty-six male (n = 34) and female (n = 22) competitive adolescent (17 ± 1 years) middle-distance runners par-
ticipated in a sub-maximal and maximal incremental treadmill test. Running economy (RE) was measured at the speed 
corresponding to lactate turnpoint, and the three speeds prior. Maximal oxygen uptake ( ̇VO2max), speed at ̇VO2max and 
fraction of ̇VO2max utilized across a range of intensities, and speeds from 0.8, 1.5 and 3 km races were also quantified. RE 
and fractional utilization were calculated in units of ̇Oaero and ̇Eaero.
Results Multiple linear regression models demonstrated no discernible difference in the predictive capability of RE, frac-
tional utilization and ̇VO2max when expressed as ̇Oaero or ̇Eaero in both sexes. When plotted as a function of running speed, ̇O
aero displayed a stepwise decrease (F = 11.59, p < 0.001) whereas ̇Eaero exhibited a curvilinear response (F = 4.74, p = 0.015). 
Differences were also evident in the slopes plotted for % ̇VO2max and % ̇Eaeromax against running speed (F = 5.38, p = 0.021).
Conclusions Quantifying aerobic determinants of performance in units of ̇Eaero provides no greater validity compared to ̇O
aero-based measurement. Although both ̇Eaero and ̇Oaero are sensitive to changes in speed, ̇Eaero provides the more valid reflec-
tion of the underlying metabolic cost of running. Physiologists should also be aware of the potential differences between 
expression of aerobic running intensity based upon % ̇VO2max compared to % ̇Eaeromax.
Keywords Running economy · Maximal oxygen uptake · Fractional utilization · Youth
Abbreviations
ANCOVA  Analysis of co-variance
ANOVA 
 Analysis of variance
CI 
 Confidence interval
̇Eaero 
 Aerobic energy cost
̇Eaeromax 
 Maximal aerobic energy expenditure
LTP 
 Lactate turnpoint
MDC 
 Minimal detectable change
̇Oaero 
 Oxygen cost
RE 
 Running economy
RER 
 Respiratory exchange ratio
SD 
 Standard deviation
sLT 
 Speed corresponding to lactate threshold
sLTP 
 Speed corresponding to lactate turnpoint
s ̇VO2max 
 Speed at ̇VO2max
̇VO2 
 Oxygen uptake
̇VO2max 
 Maximal oxygen uptake
Communicated by Jean-René Lacour.
This article is submitted as part of the Topical Collection on 
‘Energetics of Human Locomotion’.
 * Richard C. Blagrove 
 
R.C.Blagrove@lboro.ac.uk
1 
School of Sport, Exercise and Health Sciences, 
Loughborough University, Epinal Way, Loughborough, 
Leicestershire, UK
2 
Department of Sport, Exercise and Rehabilitation, 
Northumbria University, Newcastle-upon-Tyne, UK
3 
Water Research Group, Northwest University, Potchefstroom, 
South Africa
4 
School of Sport, Health and Applied Science, St Mary’s 
University, Twickenham, UK
5 
Orreco Ltd, National University of Ireland Business 
Innovation Centre, Galway, Ireland
6 
Division of Surgery and Interventional Science, University 
College London, London, UK
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European Journal of Applied Physiology (2019) 119:1865–1874
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Introduction
Distance running performance is largely dependent upon 
aerobic factors, including maximal oxygen uptake ( ̇V
O2max), running economy (RE) and the fraction of ̇V
O2max utilized over a given distance (Bassett and Howley 
2000; Brandon 1995). Although the variability in distance 
running performance can largely be explained by ̇VO2max 
in heterogeneous groups of runners, RE and fractional uti-
lization are better capable of predicting performance in 
runners homogenous for ̇VO2max (Conley and Krahenbuhl 
1980). Specifically, in middle-distance events, a model that 
included ̇VO2max and RE, was capable of explaining 96% 
of the variance in performance in highly trained 800-m 
and 1500-m runners (Ingham et al. 2008). It has recently 
been suggested that expressing physiological parameters 
in terms of aerobic energy cost ( ̇Eaero) provides greater 
validity for quantifying exercise intensity compared to 
traditional oxygen cost ( ̇Oaero)-based measurements (Beck 
et al. 2018); however, these claims have not yet been fully 
examined with experimental data.
The expression of aerobic factors in units of ̇Oaero is 
limited because this measure does not account for differ-
ences in substrate utilization, which can vary substantially 
between runners operating at the same oxygen uptake ( ̇V
O2) (Brooks and Mercier 1994; Fletcher et al. 2009). It has 
been suggested that RE should, therefore, be quantified 
as Ėaero, which provides a more accurate reflection of the 
metabolic cost of exercise (Shaw et al. 2014). Previous 
reports have confirmed that ̇Eaero provides a more sensitive 
measure of RE compared to ̇Oaero across range of intensi-
ties in highly trained runners (Fletcher et al. 2009; Shaw 
et al. 2014); however, this has not yet been established in 
lesser trained populations of runners, such as adolescents. 
̇Eaero appears to provide a more reliable measurement of 
RE compared to ̇Oaero in high-performing adolescent run-
ners (Blagrove et al. 2017); however validity-related issues 
associated with these measures have not previously been 
scrutinized in this age group.
The physiological determinants of performance for 
adolescents are similar to those of adult runners. A num-
ber of investigations have confirmed that ̇VO2max has a 
moderate–good correlation (r = 0.5–0.9) with performance 
over 1.5 km (Abe et al. 1998; Almarwaey et al. 2003), 
3 km (Abe et al. 1998; Mahon et al. 1996; Unnithan et al. 
1995), and 5 km (Abe et al. 1998; Cole et al. 2006; Cun-
ningham 1990) in young (10–18 years) groups of runners. 
Measures of RE quantified in units of ̇VO2 also appear to 
be related to middle-distance performance (Almarwaey 
et al. 2003; Mayers and Gutin 1979; Unnithan et al. 1995). 
Additionally, speed at ̇VO2max (s ̇VO2max) (Abe et al. 1998; 
Almarwaey et al. 2003; Cole et al. 2006; Cunningham 
1990) and fractional utilization calculated in ̇VO2 terms 
have also been shown to significantly correlate with dis-
tance running performance in adolescents (Mahon et al. 
1996; Unnithan et al. 1995). Despite these findings, for ̇E
aero to possess greater criterion validity compared to ̇Oaero, 
it should be capable of predicting performance times with 
greater accuracy. This direct comparative analysis of two 
different approaches to quantifying aerobic-based determi-
nants of performance has not previously been performed 
and is important for establishing validity of these metrics. 
Moreover, the method used to partition groups of young 
participants for differences in body size for variables 
such as ̇VO2max and RE is also likely to influence findings 
(Eisenmann et al. 2001). Previous studies have normalized 
to body mass as a simple ratio (Abe et al. 1998; Almar-
waey et al. 2003; Mahon et al. 1996; Unnithan et al. 1995); 
however, this is unlikely to appropriately partition out the 
confounding influence of body size (Loftin et al. 2016).
It has been proposed that fractional utilization expressed as 
the ratio between ̇Eaero and maximal aerobic energy expendi-
ture ( ̇Eaeromax) at lower intensities (respiratory exchange ratio 
(RER) < 1.0) provides a numerically lower relative aerobic 
intensity compared to fractional utilization quantified as % ̇V
O2max (Beck et al. 2018). This has important implications for 
prescription of aerobic exercise intensity and for quantifying 
the physiological outcomes to training or nutritional interven-
tions. Although this difference has been established in elite 
race walkers (Beck et al. 2018), no papers have attempted to 
compare these two approaches for other exercise modalities 
and sub-elite populations. Moreover, small differences in the 
predictive power of physiological determinants (expressed in 
̇Eaero or ̇Oaero terms) on performance times may provide greater 
deterministic accuracy when combined as part of a multiple-
factor regression model.
Consequently, the primary purpose of this study was to 
examine the relationship between physiological variables, 
quantified as both ̇Eaero and ̇Oaero, and race performances in a 
group of competitive post-pubertal adolescent middle-distance 
runners. The secondary aims were to investigate the influence 
of running speed on RE quantified as both ̇Oaero and ̇Eaero, and 
examine whether expressing relative aerobic intensity as % ̇V
O2max and % ̇Eaeromax produces a different slope of values 
across a range of speeds. It was hypothesized that ̇Eaero would 
provide a more valid means of expressing important aerobic 
performance determinants compared to ̇Oaero.
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Methods
Participants
Following institutional level ethical approval and in accord-
ance with the Helsinki declaration, 56 competitive male 
(n = 34) and female (n = 22) middle-distance (0.8–3 km) 
runners (15–18 years) volunteered to take part in this study. 
Participant descriptive statistics are displayed in Table 1. All 
participants possessed at least 2 years of distance running 
training and racing experience, were familiar with treadmill 
running and considered middle-distance running to be their 
main sport. Participants were informed of the requirements 
and risks associated with the study and thereafter signed 
consent to participate was obtained from a parent or guard-
ian, or the participant themselves if > 18 years.
Procedure
All trials were conducted in the same laboratory under simi-
lar environmental conditions (temperature 16–20 °C; relative 
humidity, 29–54%; barometric pressure, 746–773 mmHg). 
Participants were instructed to avoid strenuous exercise in 
the 48 h preceding the trial, and arrive at least 2 h post-
prandial. Upon arrival at the laboratory, stature and sitting 
height were measured with a stadiometer (SECA GmbH & 
Co., Hamburg, Germany) to the nearest 0.01 m, and matu-
rity offset was predicted for each participant using published 
formulae (Moore et al. 2015). Body mass was recorded 
with digital scales (MPMS-230, Marsden Weighing Group, 
Oxfordshire, UK) to the nearest 0.1 kg.
All exercise testing was performed on the same motor-
ised treadmill (HP Cosmos Pulsar 4.0, Cosmos Sports & 
Medical GmbH, Munich, Germany). Throughout the test-
ing, participants breathed through a low-dead space mask 
to monitor expired air via an open-circuit metabolic cart 
(Oxycon Pro, Erich Jaeger GmbH, Germany). Before test-
ing, gas analysers were calibrated with known gas concen-
trations (16%  O2; 5%  CO2) and ventilation measurement 
with a 3-L syringe. Participants completed a standard-
ized warm-up involving a 5-min run at 2 km h−1 slower 
than the pre-determined start speed for their exercise test. 
Each test involved a sub-maximal discontinuous incre-
mental test followed by an incremental continuous test 
to volitional exhaustion. The sub-maximal test involved 
5–7 × 3-min stages interspersed with 30-s rest periods for 
extraction of a 20 µL capillary blood sample. The sam-
ple was immediately haemolysed in a micro-test tube and 
tested for blood lactate (Biosen C-Line, EKF Diagnostic, 
Ebendorfer Chaussee 3, Germany). The start speed of the 
test was determined using participants’ best race times 
and published recommendations (Jones 2006). Speed 
was increased by 1 km h−1 every stage until lactate turn-
point (LTP) had been surpassed, which was defined as the 
speed before a rise of > 1 mMol L−1 compared to the pre-
vious stage. The gradient of the treadmill remained at 1% 
throughout the sub-maximal test (Jones and Doust 1996).
Following a 5-min passive recovery, participants ran 
continuously at the speed corresponding to their LTP 
(sLTP). At the end of each minute, the treadmill gradi-
ent was increased by 1% until volitional exhaustion was 
reached (typically 6–8 min).
Physiological measures
Sub‑maximal measures
Prior to analysis of expired gases, data were filtered to 
remove any values that were deemed to represent errant 
breaths (Lamarra et al. 1987). The absence of a ̇VO2 slow 
component was verified by calculating the difference 
between the first 30 s of the final minute and the last 30 s. A 
difference less than the minimal detectable change (MDC), 
calculated as standard error of the mean × 1.96 × 
√
 2, con-
firmed a ̇VO2 steady state had been achieved. The final 60 s 
of each submaximal stage was averaged for ̇VO2, volume 
of expired  CO2 and RER. Updated non-protein quotient 
equations (Peronnet and Massicotte 1991) and RER val-
ues were used to estimate ̇Eaero at each speed. Values for 
the sLTP and the three speeds prior (sLTP − 1 km.h−1, 
sLTP − 2 km.h−1, sLTP − 3 km.h−1) were used as the 
measure of RE, and quantified as both ̇Oaero and ̇Eaero. For 
each of the four submaximal speeds, the intensity relative 
to each participants ̇VO2max or ̇Eaeromax was calculated 
and expressed as a percentage. Fractional utilization at 
the speed corresponding to lactate threshold (sLT) was 
also quantified. sLT was defined as the final speed prior 
to an initial rise (≥ 0.2 mmol L−1) of blood lactate from 
baseline, which is greater than the typical error of meas-
urement at this speed in a similar cohort (10).
Table 1  Descriptive characteristics of the study participants 
̇VO2max maximal oxygen uptake, sLT speed at lactate threshold, s ̇VO
2max speed at ̇VO2max
Measure
Males (n = 34)
Females (n = 22)
Age (year)
17 ± 1
17 ± 1
Stature (m)
1.76 ± 0.06
1.69 ± 0.06
Body mass (kg)
62.5 ± 6.4
52.7 ± 5.8
̇VO2max (ml kg−1 min−1)
70.1 ± 7.2
61.1 ± 6.4
sLT (km h−1)
13.4 ± 1.5
11.7 ± 1.3
s ̇VO2max (km h−1)
19.2 ± 1.5
17.0 ± 1.5
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European Journal of Applied Physiology (2019) 119:1865–1874
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Maximal measures
The highest average ̇VO2 attained within a 30-s period dur-
ing the maximal test was defined as a participant’s ̇VO2max. 
Confirmation that ̇VO2max had been attained was identified 
using an objective procedure (Midgley et al. 2009). A pre-
dicted ̇VO2max was calculated using the linear regression 
line obtained from the ̇VO2 data between + 2 min following 
the start of the test and − 2 min prior to exhaustion. A pla-
teau was confirmed if the difference between the predicted 
and recorded ̇VO2max values was greater than 0.5 times the 
regression gradient. ̇Eaeromax was obtained by multiply-
ing the ̇VO2max value (expressed in mL min−1) by 21.745 
joules. s ̇VO2max was identified by substitution of values into 
the linear regression equation representing the ̇VO2–speed 
relationship from the sub-maximal running assessment.
Allometric scaling
It is well recognised that when expressing variables relative 
to body size, the use of ratio scaling is inappropriate (Tan-
ner 1949). Consequently, when comparing youth perform-
ers or individuals of different sexes, allometric expression 
of variables is more appropriate (Curran-Everett 2013). To 
obtain allometrically scaled exponents for the population 
under investigation, body mass and ̇VO2 data were log trans-
formed and linear regression lines compared for males and 
females using an analysis of co-variance (ANCOVA) model. 
Results revealed homogeneity of regression for the slopes of 
all variables, thus a common scaling exponent was derived 
on the logarithmic transformed data sets. The appropriate-
ness of the power function was confirmed using an absence 
of relationships derived from the linear regression correla-
tions between body mass and ̇VO2 scaled values.
Performance measures
Participant’s best times over 0.8, 1.5 and 3 km during com-
petitive track races, within 60 days (41 ± 17 days) of labora-
tory testing, were converted to running speed as an index of 
performance.
Statistical analysis
Data were analysed with IBM SPSS Statistics (v24) and 
values are displayed as mean ± standard deviation (SD). A 
p value of < 0.05 was used to denote statistical significance. 
Normality in distribution of the dependent variables was 
assessed using the Shapiro–Wilk statistic and homogene-
ity of variance with Levene’s test. Data from a number of 
race distances did not conform to this assumption, thus run-
ning speeds were log-transformed prior to further analysis. 
Normality associated with the standardized residual errors 
was assessed using probability plots and confirmed objec-
tively using the standard residual statistic. Homoscedastic-
ity was assessed using scatterplots of the residual errors 
and predicted values. Several variables displayed multi-
collinearity, defined as an r value > 0.7. Speed at LTP and 
s ̇VO2max were, therefore, analysed as separate independent 
variables with one-tailed Pearson correlation tests. For each 
race distance, multiple linear regression models were used 
to examine the combined influence of predictors expressed 
in terms of the ̇Oaero measures, and predictors quantified as 
̇Eaero. Zero-order correlation statistics were used to interpret 
the relationship with each variable in the model. To compare 
the correlation statistics for ̇Oaero-related measures against 
those expressed as ̇Eaero, a 95% confidence interval (CI) 
was calculated for each result. Correlation coefficients were 
interpreted as ≤ 0.30 negligible correlation, 0.31–0.50 low 
correlation, 0.51–0.70 moderate correlation, 0.71–0.90 high 
correlation, > 0.90 very high correlation (Hinkle et al. 2003).
A one-way repeated measures analysis of variance 
(ANOVA) was performed to evaluate the differences 
between ̇Oaero and ̇Eaero across four relative running speeds. 
Differences between % ̇VO2max and % ̇Eaeromax were 
assessed using a two-way (measure × speed) ANOVA and 
the differences between individual relative speeds was 
analysed using a one-way ANOVA. Bonferroni post hoc 
adjustments were used to detect any significant differences 
between individual speeds or measures.
Results
Performance times for males and females are shown in 
Table 2. Allometric scaling revealed exponents that approxi-
mated three-quarters for ̇VO2 at each speed [sLTP: b = 0.77 
(95% CI 0.54–0.99), sLTP − 1 km h−1: b = 0.77 (95% CI 
0.54–0.99), sLTP − 2 km h−1: b = 0.78 (95% CI 0.56–0.99), 
sLTP − 3 km h−1: b = 0.84 (95% CI 0.64–1.05)] and ̇VO2max 
[b = 0.74 (95% CI 0.48–1.00)]. Applying this power function 
(b = 0.75) revealed an absence of any significant relationship 
between body mass and scaled ̇VO2 across the intensities 
assessed (r ≤ 0.14, p ≥ 0.36).
Table 2 shows a high level of similarity between the 
correlation coefficients for the two methods used to quan-
tify aerobic energy expenditure. Multiple-regression 
analysis revealed that the independent variables of mean 
RE, fractional utilization at sLT, and ̇VO2max, accounted 
for > 80% and > 70% of the variance in 3  km perfor-
mance in males and females, respectively (p < 0.001). 
These three variables were also significant predictors of 
0.8 km (p < 0.01) and 1.5 km (p < 0.001) performance in 
males, but were poor predictors of 1.5 km performance in 
females. s ̇VO2max and sLTP tended to correlate strongly 
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Table 2  Performance time (mean ± standard deviation), coefficients (with 95% CI) of multiple regression, and Pearson correlations for physiological variables and running performance speed in 
males and females
̇Oaero aerobic oxygen cost, ̇Eaero aerobic energy cost, ̇VO2max maximal oxygen uptake, ̇Eaeromax maximal aerobic energy expenditure, s ̇VO2max speed at ̇VO2max, sLTP speed at lactate turn 
point
a p < 0.05, bp < 0.01, cp < 0.001
A Variables used in multiple regression analysis
Distance
Time (s)
Multiple 
regression 
adjusted r2
Mean running  economyA
Fractional utilization at  sLTA
̇VO2maxA
s ̇VO2max
sLTP
̇Oaero
̇Eaero
̇Oaero
̇Eaero
%̇V̇O2max
% ̇Eaeromax
Males
 0.8 km (n = 21)
120.8 ± 8.8
0.40b 0.39b − 0.53a (− 0.78 to 
− 0.13)
− 0.52a (− 0.78 to 
− 0.12)
0.19 (− 0.26 to 
0.57)
0.21 (− 0.25 to 
0.59)
0.14 (− 0.31 to 
0.55)
0.45 (− 0.01 to 
0.72)
0.60b (0.20 to 0.81)
 1.5 km (n = 34)
250.8 ± 17.9 0.56c 0.57c − 0.33 (− 0.60 to 
0.01)
− 0.39a (− 0.64 to 
− 0.06)
− 0.03 (− 0.31 to 
0.36)
− 0.04 (− 0.29 to 
0.38)
0.55b (0.27 to 
0.75)
0.75c (0.55 to 
0.87)
0.78c (0.60 to 0.89)
 3 km (n = 21)
539.5 ± 43.5 0.84c 0.85c − 0.60b (− 0.82 to 
− 0.23)
− 0.63b (− 0.83 to 
− 0.27)
− 0.15 (− 0.52 to 
0.33)
− 0.14 (− 0.50 to 
0.34)
0.77c (0.52 to 
0.91)
0.93c (0.77 to 
0.97)
0.90c (0.77 to 0.96)
Females
 0.8 km (n = 16)
136.6 ± 3.7
0.58b 0.44a − 0.66b (− 0.87 to 
− 0.25)
− 0.40 (− 0.74 to 
0.13)
0.02 (− 0.49 to 
0.50)
0.07 (− 0.43 to 
0.55)
0.52a (0.04 to 
0.81)
− 0.01 (− 0.50 to 
0.49)
0.22 (− 0.31 to 
0.64)
 1.5 km (n = 22)
281.4 ± 11.8 0.10
0.11
− 0.34 (− 0.68 to 
0.08)
− 0.37 (− 0.69 to 
0.05)
− 0.29 (− 0.64 to 
0.15)
− 0.33 (− 0.66 to 
0.11)
0.36 (− 0.08 to 
0.68)
0.42 (0 to 0.72)
0.55a (0.17 to 0.79)
 3 km (n = 16)
622.0 ± 36.3 0.79c 0.73c − 0.57a (− 0.83 to 
− 0.11)
− 0.59a (− 0.84 to 
− 0.14)
− 0.64b (− 0.86 to 
− 0.22)
− 0.66b (− 0.87 to 
− 0.25)
0.77c (0.45 to 
0.92)
0.84c (0.59 to 
0.94)
0.85c (0.62 to 0.95)
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with performance over longer distances but the relation-
ships were weaker for 0.8 km in both sexes (Table 2).
ANOVA revealed a significant decrease in ̇Oaero as run-
ning speed increased (F = 11.59, p < 0.001, Fig. 1). Post 
hoc analysis revealed significant differences between ̇Oaero 
at a number of individual speeds (Fig. 1) and two other 
comparisons (sLTP vs sLTP − 1 km  h−1, sLTP − 1 km  h−1 
vs sLTP − 2 km  h−1) approached significance (p = 0.07). 
A significant effect of running speed was also noted for 
̇Eaero (F = 4.74, p = 0.015, Fig. 2). Post hoc inspection 
identified a difference between sLTP and sLTP − 1 km 
 h−1 (p = 0.02); however, the difference between sLTP and 
sLTP − 2 km  h−1 was close to the threshold of significance 
(p = 0.06).
A significant main effect between the slopes of the lines 
was detected for % ̇VO2max and % ̇Eaeromax when plotted 
against relative running speed (F = 5.38, p = 0.021); how-
ever, there was an absence of an interaction effect (meas-
ure × speed; F = 0.29, p = 0.834). One-way ANOVA analysis 
was also not able to locate any difference between measures 
at each relative speed.
Discussion
The primary aim of this study was to examine the relation-
ship between race performances and several important aero-
bic variables quantified as both ̇Eaero and ̇Oaero in adolescent 
Fig. 1  Oxygen cost ( ̇Oaero) 
for speed at lactate turnpoint 
(LTP) and the three speeds 
prior (n = 56). aSignificantly 
different from speed at LTP 
(p < 0.01), bsignificantly differ-
ent from speed at LTP-1 km  h−1 
(p = 0.01)
Fig. 2  Aerobic energy cost ( ̇E
aero) for speed lactate turnpoint 
(LTP) and the three speeds prior 
(n = 56). aSignificantly different 
from speed at LTP (p = 0.02)
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1 3
middle-distance runners. Results indicate that ̇Eaero does not 
provide a greater level of criterion validity compared to ̇O
aero-based measurements in this age group for the middle-
distance running events. The study also aimed to investigate 
the validity of ̇Oaero and ̇Eaero as a means of quantifying RE. 
Results showed differences in the manner ̇Oaero and ̇Eaero 
change with increasing running speed, with ̇Oaero displaying 
a decrease and ̇Eaero a curvilinear response. A further find-
ing was that the relationship between relative running speed 
and the fraction of ̇VO2max or ̇Eaeromax that is accessed 
also appears to differ, with the difference being greater at 
lower intensities. These findings provide new insight into the 
ongoing debate surrounding the most appropriate method of 
expressing aerobic fitness parameters, which are typically 
used to evaluate performance, health status and monitor 
improvement.
Results of the multiple regression analysis show that 
using ̇Eaero to quantify RE and fractional utilization, instead 
of traditional ̇Oaero, provides no additional value in the pre-
diction of middle-distance running performance in adoles-
cents. To alter the strength of the relationship between ̇E
aero-based determinants and performance, a high-level of 
inter-individual variability in substrate utilization is required. 
This was not apparent as coefficient of variation (SD/mean) 
for the RER values at each relative speed was ~ 4%. The 
participants used in this study possessed somewhat homog-
enous physiological characteristics (Table 1), thus future 
research could investigate a more heterogeneous sample 
of runners, who are likely to differ more substantially in 
terms of their consumption of substrates at the same rela-
tive speeds. Similarly, a relatively small range of running 
speeds (sLTP to sLTP − 3 km  h−1) was examined in the 
present study and measurement stages were relatively short 
(3 min), which resulted in mean RER values > 0.9. A larger 
range of speeds and longer sampling duration would pro-
duce lower RER values (Van Loon et al. 2001) and may 
have generated more substantial between-participant vari-
ability as maximal rates of lipid oxidation rates are known 
to occur at ~ 65% ̇VO2max but is dependent upon training 
status (Achten and Jeukendrup 2004). This would, there-
fore, alter the predictive power of variables quantified in ̇E
aero units. Nevertheless, it is also questionable that slower 
running speeds would correlate well with middle-distance 
performance given the large discrepancy between low-inten-
sity running and middle-distance race speed. Although ̇E
aero also accounts for the energy yield associated with work 
performed during sub-maximal exercise compared to ̇Oaero 
(Shaw et al. 2014), there are numerous other factors that also 
govern these parameters, including use of stretch-shortening 
cycle mechanisms, muscle activation in the musculotendi-
nous unit, running kinematics and anthropometric factors 
(Barnes and Kilding 2015). Thus, it may also be the case 
that the additional metabolic insight, which ̇Eaero provides, 
is insufficient to alter the predictive capacity of these aerobic 
parameters on performance. Within study designs that assess 
participants at more than one point in time, expressing RE as 
̇Eaero, rather than ̇Oaero, is likely to provide the most scientifi-
cally robust metric (Blagrove et al. 2017; Shaw et al. 2014).
To the author’s knowledge, this is the first study to apply 
a multiple regression model to middle-distance performance 
in adolescent runners, using acknowledged aerobic deter-
minants of performance (Bassett and Howley 2000; Ing-
ham et al. 2008). Results showed that a high level (~ 80%, 
p < 0.001) of inter-individual variability in male and female 
3 km performance could be explained by RE, fractional 
utilization at sLT and ̇VO2max (Table 2). Moreover, ~ 40% 
(p < 0.01) of the 0.8 km performance variability and 56% 
(p < 0.001) of male 1.5 km performance could be explained 
using these same variables. Surprisingly, this regression 
model could only predict a small (10%) amount of the vari-
ability in female 1.5 km performance. This may be due to the 
homogeneity of the performance times in the female (5%) 
compared to the male sample (7%) over 1.5 km.
Previous studies have shown the importance of ̇VO2max 
for middle-distance (1.5 km and 3 km) performance in 
children and adolescent (Abe et al. 1998; Mahon et al. 
1996; Unnithan et al. 1995), which is largely confirmed by 
the results of this investigation (r = 0.55–0.77, p < 0.01). 
Based upon the non-overlap of the 95% CI with the cor-
relation coefficients, it is also apparent that ̇VO2max 
becomes more important as a determinant of performance 
as race duration increases, which is in agreement with pre-
vious findings in adolescent (Almarwaey et al. 2003) and 
adult runners (Ingham et al. 2008; Padilla et al. 1992). It 
is likely that this pattern in results reflects the increasing 
proportion of ̇VO2max that is attained as race duration 
increases in middle-distance events (Brandon 1995). This 
is also the case for s ̇VO2max and sLTP, both of which 
show high (r > 0.84, p < 0.001) correlations with 3 km per-
formance in males and females but weaker correlations 
at the shorter distances (Table 2). RE, as an independent 
factor, is not thought to be important for middle-distance 
running performance (Ingham et al. 2008) despite sev-
eral studies observing significant relationships in young 
runners (Almarwaey et al. 2003; Mayers and Gutin 1979; 
Unnithan et al. 1995). When RE was expressed as ̇Eaero, 
it generally showed low–moderate negative relationships 
(r = − 0.37 to − 0.63) with performance, which did not dif-
fer across race distances (Table 2). Relationships were sig-
nificant for male participants across all distances (p < 0.05) 
and females only at 3 km (r = − 0.57, p < 0.05), which is 
in agreement with the previous findings (Almarwaey et al. 
2003). In adolescent middle-distance running, it, therefore, 
appears that RE influences race performance, but explains 
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European Journal of Applied Physiology (2019) 119:1865–1874
1 3
a relatively small proportion of inter-individual variability. 
It is possible that participants who have a low ̇VO2max 
compensate by possessing better RE (Cunningham 1990). 
This may explain the low relationship (r = 0.14) between 
̇VO2max and 0.8 km performance in males but moderate 
relationship (r = − 0.52, p < 0.05) between ̇Eaero and per-
formance over this distance.
Results demonstrate that the RE–speed relationship 
differed depending upon the strategy used to quantify 
RE. When expressed as ̇Oaero, running became less meta-
bolically expensive as a function of speed (F = 11.59, 
p < 0.001, Fig. 1), which is in agreement with previous 
findings (Iaia et al. 2009) but in contrast to others who 
have shown no change (Fletcher et al. 2009; Shaw et al. 
2014) or an increase (Fletcher et al. 2013) in ̇Oaero as speed 
increases. This discrepancy between findings is likely due 
to the range of speeds examined in each study and the 
training status of participants. Similar to the study by Iaia 
et al. (2009), the speeds selected in the present study rep-
resent the upper end of the range over which RE can be 
measured with high validity (≤ LTP, RER < 1.0), whereas 
others have utilized a lower range of relative intensities 
(Fletcher et al. 2009; Shaw et al. 2014). Furthermore, pre-
vious studies used highly trained runners (Fletcher et al. 
2009; Shaw et al. 2014), who were assessed at faster abso-
lute speeds compared to the young runners recruited in the 
present study. When quantified as ̇Oaero (per unit distance), 
a faster range of absolute speeds tends to produce a flatter 
relationship compared to oxygen cost at slower absolute 
speeds.
Conversely, when RE was quantified as ̇Eaero, a sub-
tle ‘U-shaped’ profile was apparent across the range of 
speeds (Fig. 3), with a significant difference noted between 
sLTP and sLTP − 1 km  h−1 (p = 0.02) and a near-sig-
nificant difference between sLTP and sLTP − 2 km  h−1 
(p = 0.06). A curvilinear relationship between ̇Eaero and 
speed has been observed in a number of studies (Black 
et al. 2018; Rathkey and Wall‐Scheffler 2017; Steudel-
Numbers and Wall-Scheffler 2009; Willcockson and 
Wall-Scheffler 2012), with the nadir representing the 
most economical running speed. The least energetically 
expensive speed (at sLTP − 2 km h−1) in the present study 
was 13.4 ± 1.7 km h−1, which is similar to the 13 km h−1 
(Black et al. 2018) and 12.6 km h−1 (Steudel-Numbers 
and Wall-Scheffler 2009; Willcockson and Wall-Scheffler 
2012) reported previously in similarly trained participants. 
It is likely that other studies that have observed linear ̇E
aero–speed relationships have used a range of speeds that 
did not capture the lowest point of the curve (Fletcher et al. 
2009; Shaw et al. 2014) or used a lesser trained group of 
runners (Black et al. 2018).
Crucially, the trend for an increase in ̇Eaero with faster 
speeds above sLTP − 2 kmh−1 is in opposition to the rela-
tionship demonstrated between ̇Oaero and running speed. ̇E
aero represents a theoretically more valid measure of RE as 
it estimates actual energy turnover, whereas ̇Oaero is sim-
ply a measure of the ̇VO2 per unit of running distance. The 
increase in ̇Eaero as speed progressed from sLTP − 2 km h−1 
towards sLTP, therefore, reflects the increase in RER value, 
indicating an increased reliance on carbohydrate as an 
energy source. As running speed increases, joint angular 
velocities are greater and ground contact time reduces, 
which requires a greater reliance on metabolically ineffi-
cient type II muscle fibres (Fletcher and MacIntosh 2017). 
An increased recruitment of high threshold motor units is 
likely to be the mechanism that drives the rate of carbohy-
drate utilization, and as the energy yield from carbohydrates 
per mole of  O2 is also greater than lipids (Jeukendrup and 
Wallis 2005), this generates higher ̇Eaero at faster speeds. It 
is, therefore, recommended that ̇Eaero should be used as a 
measure of RE as this provides a more valid indicator of the 
metabolic demand of running compared to ̇Oaero.
Fig. 3  Percentage utiliza-
tion of maximum oxygen 
uptake and maximum aerobic 
energy expenditure across four 
sub-maximal relative speeds 
(n = 56). sLTP speed at lactate 
turnpoint
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European Journal of Applied Physiology (2019) 119:1865–1874 
1 3
Exercise intensity is often prescribed relative to an 
individual’s ̇VO2max, thus expression of relative aerobic 
intensity as a percentage of ̇Eaeromax would be more mean-
ingful. However, prescribing exercise intensity on either 
basis has been criticised due to the heterogeneity at which 
other important exercise thresholds (critical speed, anaer-
obic threshold, lactate threshold) occur (Baldwin et al. 
2000; Scharhag-Rosenberger et al. 2010). Therefore, an 
intensity expressed relative to either ̇VO2max or ̇Eaeromax 
may represent a severe intensity (relative to critical power) 
in one individual but provide a steady-state condition for 
another individual. We attempted to account for this in 
the present study by comparing the relationship between 
% ̇VO2max or % ̇Eaeromax across a range of running speeds 
expressed relative to each individual’s sLTP. A significant 
main effect method of measurement (% ̇VO2max versus % ̇E
aeromax) was detected (F = 5.38, p = 0.021); however, no 
differences were identified at individual relative speeds. 
The divergent nature of the gradients (Fig. 3) as relative 
intensity decreases suggests that at slower relative speeds, 
the use of % ̇Eaeromax becomes more important. Therefore, 
it is recommended that if exercise is prescribed based on 
maximal aerobic values, intensity is expressed as a frac-
tion of ̇Eaeromax, rather than ̇VO2max. However, a superior 
method for prescribing running intensity is to base calcula-
tions on sLTP (or a similar objective metabolic threshold), 
which would reduce inter-individual differences in relative 
intensity, thus providing a more valid strategy.
This study has several limitations that should be 
acknowledged. First, physiological testing predominantly 
took place during the pre-season or early competitive rac-
ing period, with the duration between a participants’ race 
performance and laboratory testing typically 3–8 weeks. 
Although every attempt was made to minimize this time 
gap, small changes in the physiological profile of partici-
pants cannot be discounted, which may have influenced the 
results. Second, participants performed laboratory testing 
2 h post-prandial; however, it is less certain whether this 
requirement was adhered to prior to races. Participants 
possessed ≥ 2 years’ racing experience; therefore, it is 
unlikely that subtle differences in pre-race routine con-
found the results to a large extent. Third, middle-distance 
running performance is limited by anaerobic factors, in 
addition to the aerobic determinants measured in this 
study (Thompson 2017). These anaerobic variables were 
not quantified in this investigation and are likely to explain 
a large proportion of the variability in performance cur-
rently unaccounted for in the regression models. Moreo-
ver, investigating the determinants of longer race distances 
(≥ 5 km), which have a greater reliance on aerobic sources 
of energy, would also have been of interest in this age 
group.
Conclusions
Expression of RE and fractional utilization in terms of ̇E
aero rather than ̇Oaero does not appear to alter the ability 
of these determinants to predict middle-distance running 
performance in adolescents. RE, fractional utilization at 
sLT and ̇VO2max accounted for approximately 80% of the 
variability in 3 km performance in adolescent males and 
females. These variables could explain less (40–60%) of 
the variation in performance over shorter race distances 
and very little over 1.5 km in females. s ̇VO2max and sLTP 
were confirmed as other important indicators of middle-
distance performance in adolescent runners with the 
strength of relationships tending to be greater over longer 
distances. Results also indicate markedly different pro-
files in the ̇Oaero–speed response compared to ̇Eaero–speed 
relationship. It is recommended that RE is quantified in 
̇Eaero units, which provides a more valid reflection of the 
metabolic demand of running across a range of speeds. 
Finally, there were differences observed in the slope of the 
relationships between running speed and the proportion of 
̇VO2max or ̇Eaeromax utilized at each speed, suggesting this 
should be accounted for if prescribing exercise intensity 
using this method.
Acknowledgements The authors would like to thank the participants 
and their parents/guardians for the time they committed to this study. 
The technical support provided by Ian Grant is also greatly appreciated.
Author contributions RB and PH conceived and designed research. RB 
conducted experiments, analyzed data and wrote the manuscript. All 
authors proof read, contributed to editing, and approved the manuscript.
Compliance with ethical standards 
Conflict of interest The authors declare that they have no conflict of 
interest.
Open Access This article is distributed under the terms of the Creative 
Commons Attribution 4.0 International License (http://creativecom-
mons.org/licenses/by/4.0/), which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided you give appropriate 
credit to the original author(s) and the source, provide a link to the 
Creative Commons license, and indicate if changes were made.  
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Quantification of aerobic determinants of performance in post-pubertal adolescent middle-distance runners.
Blagrove, Richard C,Howatson, Glyn,Pedlar, Charles R,Hayes, Philip R