Vol.:(0123456789)
1 3
European Journal of Applied Physiology (2020) 120:961–968 
https://doi.org/10.1007/s00421-020-04312-w
ORIGINAL ARTICLE
Gross and delta efficiencies during uphill running and cycling 
among elite triathletes
Magnus Carlsson1,2 · Viktor Wahrenberg1 · Marie S. Carlsson1 · Rasmus Andersson1 · Tomas Carlsson1,2 
Received: 18 October 2019 / Accepted: 31 January 2020 / Published online: 1 April 2020 
© The Author(s) 2020
Abstract
Purpose To investigate the gross efficiency (GE) and delta efficiency (DE) during cycling and running in elite triathletes.
Methods Five male and five female elite triathletes completed two incremental treadmill tests with an inclination of 2.5° 
to determine their GE and DE during cycling and running. The speed increments between the 5-min stages were 2.4 and 
0.6 km h−1 during the cycling and running tests, respectively. For each test, GE was calculated as the ratio between the 
mechanical work rate (MWR) and the metabolic rate (MR) at an intensity corresponding to a net increase in blood-lactate 
concentration of 1 mmol l−1. DE was calculated by dividing the delta increase in MWR by the delta increase in MR for each 
test. Pearson correlations and paired-sample t tests were used to investigate the relationships and differences, respectively.
Results There was a correlation between  GEcycle and  GErun (r = 0.66; P = 0.038; R2 = 0.44), but the correlation between  DEcycle 
and  DErun was not statistically significant (r = − 0.045; P = 0.90; R2 = 0.0020). There were differences between  GEcycle and 
 GErun (t = 80.8; P < 0.001) as well as between  DEcycle and  DErun (t = 27.8; P < 0.001).
Conclusions Elite triathletes with high GE during running also have high GE during cycling, when exercising at a treadmill 
inclination of 2.5°. For a moderate uphill incline, elite triathletes are more energy efficient during cycling than during run-
ning, independent of work rate.
Keywords Triathlon · Cycling economy · Running economy · Incline · Metabolic rate · Mechanical work rate
Abbreviations
α 
 Treadmill inclination
DE 
 Delta efficiency
DEcycle 
 Delta efficiency during uphill cycling
DErun 
 Delta efficiency during uphill running
ΔMR 
 Change in metabolic rate
ΔMWR 
 Change in mechanical work rate
g 
 Gravitational acceleration
GE 
 Gross efficiency
GEcycle 
 Gross efficiency during uphill cycling at the 
lactate threshold
GErun 
 Gross efficiency during uphill running at the 
lactate threshold
LT 
 Lactate threshold, i.e. the mechanical work 
rate at which the blood-lactate concentration 
increased 1 mmol l−1 above the lowest meas-
ured value
mtot 
 Total mass of participant and equipment
MR 
 Metabolic rate
MWR 
 Mechanical work rate
RERmean  Mean respiratory exchange ratio
μ 
 Rolling-resistance coefficient of the bicycle
̇VO2max 
 Maximal oxygen uptake
̇VO2mean 
 Mean oxygen uptake
Introduction
Triathlon comprises stages of swimming, cycling and run-
ning in a sequential order. In a World Cup Olympic-distance 
competition (i.e. 1.5 km swimming, 40 km cycling and 
10 km running), all three stages are important for overall 
race performance (Landers et al. 2000; Ofoghi et al. 2016). 
An analysis of the International Triathlon Union’s champi-
onship results from 2008 to 2012 revealed that the winners’ 
Communicated by Jean-René Lacour.
 * Tomas Carlsson 
 
tca@du.se
1 
School of Education, Health and Social Studies, Dalarna 
University, Högskolegatan 2, 791 88 Falun, Sweden
2 
Swedish Unit for Metrology in Sports, Dalarna University, 
Högskolegatan 2, 791 88 Falun, Sweden
962
 
European Journal of Applied Physiology (2020) 120:961–968
1 3
mean race times were 1 h 46 min (men) and 1 h 58 min 
(women) (Ofoghi et al. 2016). Moreover, it was found that 
elite triathletes’ mean heart rate during an Olympic-distance 
competition was 92% of their maximal heart rate, which 
indicates the competition’s high-intensity character (Le 
Meur et al. 2009).
From a physiological perspective, endurance perfor-
mance is determined by the sum of the aerobic and anaero-
bic energy contribution multiplied by gross efficiency (GE) 
(Joyner and Coyle 2008). In triathlon, performance is mainly 
determined by maximal oxygen uptake ( ̇VO2max ), lactate/
ventilatory threshold and oxygen uptake kinetics, which 
together reflect the aerobic energy contribution, and exer-
cise economy (i.e. GE during the specific exercise mode) 
(Jones and Carter 2000). In line with these findings, lactate-
threshold variables and peak oxygen uptake in cycling and 
running were found to be predictors of Olympic-distance 
triathlon performance (Miura et al. 1997; Schabort et al. 
2000). Hence, the ability to exercise at a lower percentage 
of ̇VO2max for a given submaximal workload (i.e. better econ-
omy) has been suggested to be of great importance for suc-
cess in triathlon (Dengel et al. 1989; Sleivert and Rowlands 
1996). Accordingly, cycling economy and running economy 
have been reported to be correlated with performance in 
triathlon (Miura et al. 1997), and economy of movement has 
been suggested to be an important determinant of triathlon 
performance (Dengel et al. 1989; Tucker and Tucker 2013).
The economy of movement is reflected by the functioning 
of the cardiorespiratory, metabolic, neuromuscular and bio-
mechanical systems (Barnes and Kilding 2015; Ettema and 
Lorås 2009). In line with this concept, it has been suggested 
that running economy is related to factors such as muscle 
morphology, elastic elements and joint mechanics (Barnes 
and Kilding 2015; Joyner and Coyle 2008; Lacour and Bour-
din 2015). In cycling, mechanisms such as muscle-fibre-type 
transformation, changes in muscle-fibre-shortening veloci-
ties, changes within the mitochondria and biomechanical 
factors have been proposed to be related to improved cycling 
economy (Coyle et al. 1991; Hopker et al. 2009).
There are several ways to express cycling efficiency. Two 
of these measures of efficiency are based on the relationship 
between the work performed and the energy expenditure; 
GE is the ratio between the mechanical work rate (MWR) 
and the metabolic rate (MR) (i.e. GE = MWR/MR), whereas 
delta efficiency (DE) is the ratio between the delta increase 
in MWR and the delta increase in MR (i.e. DE = ΔMWR/
ΔMR). In cycling, GE varies between approximately 18 
and 23% in different individuals (Coyle et al. 1992), and 
the corresponding range in DE is approximately 18–27% 
(Coyle et al. 1992; Ettema and Lorås 2009). The DE is usu-
ally somewhat higher than GE because the basal metabolic 
rate and metabolic cost of zero-load exercise are excluded 
from DE calculations.
Running economy is often measured as oxygen uptake at 
a given submaximal running speed (e.g. 16 km h−1) while 
running on a level treadmill, where a better running econ-
omy is indicated by a lower oxygen consumption (Barnes 
and Kilding 2015). During level treadmill running, zero 
external work is performed against gravity, frictional forces 
or air resistance; hence, it is not appropriate to express run-
ning economy as GE or DE using a treadmill inclination of 
0°. Previously, it has been found that GE during running 
increases with steeper inclines (Minetti et al. 2002), which 
emphasize the importance of taking the incline into account 
when running efficiency is evaluated.
A recent study investigated the relationship between 
triathletes’ energy expenditure during level running at 
12 km h−1 and during ergometer cycling at a power output of 
200 W, and no significant correlation was found between the 
gross metabolic rates (Swinnen et al. 2018). Other studies 
have compared DE during running and cycling using differ-
ent methods to apply external loads (e.g. running up different 
inclines, applying impeding horizontal forces during level 
treadmill running and treadmill cycling on a tricycle), but 
the relationship between running and cycling DE was not 
investigated in either study (Bijker et al. 2001, 2002).
To the best of our knowledge, no previous study has used 
a fixed treadmill inclination to investigate elite triathletes’ 
running and cycling efficiencies. The purpose of this study 
was to investigate gross efficiency and delta efficiency dur-
ing cycling and running in elite triathletes.
Methods
Participants
Five male (age: 24 ± 5 years, stature: 181 ± 4 cm, and body 
mass: 73 ± 4 kg) and five female (age: 22 ± 6 years, stature: 
169 ± 8 cm, and body mass: 64 ± 9 kg) elite triathletes volun-
teered to participate in the study and completed the GE and 
DE tests. During a 5-year period, all ten triathletes had been 
in the top 8 in the Swedish championships; seven of the par-
ticipants had at least one podium finish, and two participants 
had previously won the Swedish championships in triathlon.
Testing procedures
The participants were instructed to only perform light train-
ing on the 2 days preceding their scheduled test days, to be 
well hydrated, to refrain from alcohol (24 h) and caffeine 
(12 h) and to avoid eating within 2 h prior to testing. On 
the day of the tests, the participants completed a health-
status questionnaire, and thereafter, the participant’s stat-
ure (Harpenden Stadiometer, Holtain Limited, Crymych, 
Great Britain) and body mass (Midrics 2, Sartorius AG, 
963
European Journal of Applied Physiology (2020) 120:961–968 
1 3
Goettingen, Germany) were measured. Additionally, the 
mass of the equipment the participant used in the cycling 
test (i.e. bicycle, cycling shoes, helmet and harness) and 
running test (i.e. running shoes and harness) were weighed.
The cycling and running tests were performed on a motor-
driven treadmill (Saturn 450/300rs, h/p/cosmos sports & 
medical GmbH, Nussdorf-Traunstein, Germany). Through-
out the tests, expired air was continuously analysed using 
a metabolic cart in mixing-chamber mode (Jaeger Oxycon 
Pro, Erich Jaeger Gmbh, Hoechberg, Germany). The meta-
bolic cart was calibrated according to the specifications of 
the manufacturer before each test, and at the start of each 
new 5-min stage, a ‘zeroing’ of the  O2 and  CO2 sensors was 
performed. After the warm-ups and after each stage was 
completed, capillary-blood samples were collected from a 
fingertip and thereafter analysed to determine blood-lactate 
concentrations (Biosen 5140, EKF-diagnostic GmbH, Bar-
leben, Germany).
Cycling test
Prior to the cycling test, the participants performed a 
standardized warm-up. The 7.5-min warm-up started with 
5 min at a treadmill inclination of 1° and treadmill speed 
of 5.56 m s−1 (20 km h−1) for the men and 5.00 m s−1 
(18 km h−1) for the women, which was followed by 2.5 min 
at the initial work intensity of the cycling test (i.e. incli-
nation, 2.5°; speed, 4.56 m s−1 (16.4 km h−1) (men) or 
3.22 m s−1 (11.6 km h−1) (women)). After the warm-up was 
completed, a capillary-blood sample was collected, and 
thereafter the rolling-resistance coefficient of the partici-
pant’s bicycle was determined using a previously described 
method (Carlsson et al. 2016). In brief, the treadmill speed 
was set at 5.56 m s−1 (20 km h−1), with the rider facing 
downhill, and the treadmill’s negative inclination was then 
adjusted until the participant sitting on the bicycle (without 
pedalling) did not move in either the backward or forward 
direction on the treadmill. Based on the equilibrium incli-
nation, the bicycle’s rolling-resistance coefficient (μ) was 
calculated from the formula μ = mtot · g · sin α/mtot · g · cos α, 
where mtot is the mass of the participant, including the mass 
of the equipment (kg), g is the acceleration due to gravity 
(9.82 m s−2 at the location of the sport-science laboratory) 
and α is the treadmill inclination (°).
Throughout the cycling test, the treadmill inclination was 
2.5°, and the participants were permitted to use a self-chosen 
cadence. For each of the subsequent stages, the speed was 
increased by 0.67 m s−1 (2.4 km h−1). Each stage lasted for 
5 min, and the mean oxygen uptake ( ̇VO2mean ) and mean 
respiratory exchange ratio  (RERmean) during the last 2 min 
of the stages were used for calculation of gross efficiency 
 (GEcycle) and delta efficiency  (DEcycle) during cycling. 
The stages were separated by a 1-min pause to collect a 
capillary-blood sample, and the participants rated their per-
ceived exertion (RPE) on a scale of 6–20 (Borg 1970). The 
pre-determined criteria for permitting the participants to 
commence another stage were as follows: the participant’s 
RPE had to be lower than 17 (“Very hard”), and the previous 
stage’s RER had to be lower than 1.0.
Running test
To minimize the influence of the cycling test on the sub-
sequent running test, the running test was initiated 60 min 
after the completion of the cycling test. It has previously 
been reported that approximately 30 min of passive recovery 
is sufficient to reduce the blood-lactate concentration from 
3.9 ± 0.3 mmol l−1 to baseline values (1.0 ± 0.1 mmol l−1) in 
moderately trained adults (Menzies et al. 2010); hence, the 
time for the elite triathletes to recover after the submaximal 
cycling test was considered to be sufficient. The partici-
pants were permitted to use a self-chosen stride frequency 
throughout the test. Prior to the start of the running test, the 
participants performed a 5-min warm-up at a treadmill incli-
nation of 2.5°, and the treadmill speeds were 10.0 km h−1 
and 8.2 km h−1 for the men and women, respectively. After 
the warm-up was completed, a capillary-blood sample was 
collected. Thereafter, the running test was initiated with the 
same intensities as in the warm-up; it consisted of 5-min 
stages with a 1-min pause between stages to collect a cap-
illary-blood sample, and to have the participants rate their 
perceived exertion. Throughout the test, the treadmill incli-
nation was fixed at 2.5°, and the treadmill speed increment 
was 0.6 km h−1 between stages. The criteria for permitting 
the participants to commence another stage were the same as 
those for the cycling test. The ̇VO2mean and  RERmean during 
the last 2 min of the stages were used for calculation of the 
gross efficiency  (GErun) and delta efficiency  (DErun) during 
running.
Calculation of delta efficiency
For each completed stage in both tests, i.e. when 
RER was < 1.0 and blood-lactate concentration 
was < 4.0 mmol l−1, the mechanical work rate (MWR) and 
metabolic rate (MR) were calculated. The MWR (W) dur-
ing the cycling test was the sum of the work against gravity 
and the work related to overcoming the rolling resistance of 
the bicycle: MWR = (mtot · g · sin α · v + mtot · g · cos α · μ 
· v), where v is the treadmill speed (m s−1). In the running 
test, only the component related to work performed against 
gravity was included in the MWR calculation. The MR (W) 
was based on the participant’s ̇VO2mean (l s−1) and  RERmean: 
MR = k1 · ̇VO2mean · k2, where k1 is 3.815 + 1.232 ·  RERmean 
(Lusk 1928) and k2 is 4186 and converts kcal to J. Linear 
regression was used to determine the relationship between 
964
 
European Journal of Applied Physiology (2020) 120:961–968
1 3
MWR and MR for each participant. Based on the relation-
ship,  DEcycle and  DErun were calculated by dividing the delta 
increase in MWR by the delta increase in MR for each test.
Calculation of gross efficiency
For both tests, the GE was calculated as the ratio between the 
MWR and MR during the last 2 min of each stage. To make 
an adequate comparison between  GEcycle and  GErun, the 
MWR when the blood-lactate concentration had increased 
1 mmol l−1 above the lowest measured value (LT) was used. 
To establish the work rate at the LT, a third-order polyno-
mial equation was fitted to each of the participant’s obtained 
MWR/blood-lactate concentration combinations, i.e. it was 
calculated even for those stages that resulted in lactate val-
ues exceeding 4 mmol l−1. The polynomial equation was 
then used to calculate the MWR at the LT. Thereafter, linear 
regression was used to determine the relationship between 
MWR and GE for each participant. Based on the linear equa-
tion and the participant’s MWR at the LT, the GE at the LT 
was calculated.
Statistical analyses
The test results are presented as the mean and standard 
deviation (SD). The agreement of test variables with a nor-
mal distribution was assessed with the Shapiro–Wilk test. 
Pearson’s product–moment correlation coefficient (r) test 
was used to investigate the relationship between  GEcycle 
and  GErun as well as between  DEcycle and  DErun. The guide-
lines for the interpretation of the strength of the correlation 
are as follows: small correlation for 0.1 ≤|r|< 0.3, moder-
ate correlation for 0.3 ≤|r|< 0.5, and large correlation for 
|r|≥ 0.5 (Cohen 1988). Paired-samples t tests were used to 
investigate differences between  GEcycle and  GErun as well 
as between  DEcycle and  DErun. The Cohen’s effect-size cri-
teria were used to interpret the magnitude of the effect size 
(η2) and to enable making more informative inferences 
from the results. The substantial effects were divided into 
more fine-graded magnitudes as follows: small effect for 
0.01 ≤ η2 < 0.06, moderate effect for 0.06 ≤ η2 < 0.14, and 
large effect for η2 ≥ 0.14 (Cohen 1988). All statistical analy-
ses were assumed to be significant at an alpha level of 0.05. 
The statistical analyses were conducted using the IBM SPSS 
Statistics software, Version 25 (IBM Corporation, Armonk, 
NY, USA).
Results
The test results of the cycling and running test are pre-
sented in Tables 1 and 2, respectively. The mass of the 
equipment was 9.6 ± 0.5 kg during the cycling test and 
0.9 ± 0.2 kg during the running test. The bicycles’ μ was 
determined to 0.0042 ± 0.0006 N N−1. The intercepts for 
the relationship between MWR and oxygen uptake were 
0.44 ± 0.12 l min−1 and 0.28 ± 0.20 l min−1 for the cycling 
and running test, respectively. The blood-lactate concentra-
tions at LT were 1.9 ± 0.2 mmol l−1 for the cycling test and 
2.2 ± 0.3 mmol l−1 for the running test. The maximum blood-
lactate concentration after each test was 4.3 ± 1.1 mmol l−1 
and 4.0 ± 1.8 mmol l−1 for the cycling and running test, 
respectively.
The test results in the DE tests were  DEcycle = 23.5 ± 1.6% 
and  DErun = 8.3 ± 0.5%. All test variables were normally 
distributed (all P > 0.05). There was a correlation between 
 GEcycle and  GErun (r = 0.66; P = 0.038; R2 = 0.44) (Fig. 1), 
and the participants’ sex was not a contributing factor 
Table 1  Test results from the cycling test
All values are presented as mean ± standard deviation
̇VO2mean mean oxygen uptake (l  min−1), RER respiratory exchange 
ratio (l  l−1), MR metabolic rate (W), MWR mechanical work rate (W), 
GE gross efficiency (%), LT the MWR at which the blood-lactate con-
centration increased 1 mmol l−1 above the lowest measured value. All 
ten participants completed stage 1–5. Stages 6 and 7 were completed 
by eight and zero participants, respectively, N/A not applicable
Stage
̇VO2mean
RER
MR
MWR
GE
1
2.16 ± 0.50
0.80 ± 0.03
726 ± 170
144 ± 37
19.7 ± 0.8
2
2.42 ± 0.51
0.84 ± 0.03
820 ± 175
168 ± 39
20.4 ± 0.7
3
2.72 ± 0.55
0.85 ± 0.03
921 ± 187
193 ± 41
20.9 ± 0.7
4
3.03 ± 0.60
0.86 ± 0.03
1032 ± 206
217 ± 44
21.0 ± 0.6
5
3.32 ± 0.63
0.90 ± 0.02
1140 ± 216
242 ± 46
21.2 ± 0.7
6
3.61 ± 0.67
0.93 ± 0.02
1250 ± 232
269 ± 47
21.5 ± 0.7
7
N/A
N/A
N/A
N/A
N/A
LT
3.14 ± 0.62
0.87 ± 0.04
1069 ± 209
226 ± 45
21.1 ± 0.7
Table 2  Test results from the running test
All values are presented as mean ± standard deviation
̇VO2mean mean oxygen uptake (l  min−1), RER respiratory exchange 
ratio (l  l−1), MR metabolic rate (W), MWR mechanical work rate (W), 
GE gross efficiency (%), LT the MWR at which the blood-lactate con-
centration increased 1 mmol l−1 above the lowest measured value. All 
ten participants completed stage 1–5. Stage 6 and 7 was completed by 
9 and 5 participants, respectively.
Stage
̇VO2mean
RER
MR
MWR
GE
1
2.73 ± 0.47
0.83 ± 0.03
923 ± 157
76 ± 15
8.2 ± 0.5
2
2.89 ± 0.49
0.84 ± 0.03
977 ± 164
81 ± 15
8.2 ± 0.6
3
3.03 ± 0.51
0.85 ± 0.03
1027 ± 172
86 ± 16
8.3 ± 0.6
4
3.23 ± 0.54
0.86 ± 0.04
1097 ± 180
90 ± 17
8.2 ± 0.6
5
3.38 ± 0.53
0.87 ± 0.04
1152 ± 177
95 ± 17
8.3 ± 0.5
6
3.57 ± 0.63
0.89 ± 0.05
1222 ± 210
101 ± 19
8.2 ± 0.5
7
3.79 ± 0.52
0.89 ± 0.05
1298 ± 171
112 ± 15
8.6 ± 0.2
LT
3.41 ± 0.69
0.88 ± 0.04
1165 ± 227
96 ± 22
8.2 ± 0.5
965
European Journal of Applied Physiology (2020) 120:961–968 
1 3
(P = 0.79). There was a significant relationship between 
oxygen uptake values during cycling and running at LT 
(r = 0.95; P < 0.001; R2 = 0.90). No significant relationship 
was found between  DEcycle and  DErun (r = − 0.045; P = 0.90; 
R2 = 0.0020) (Fig. 2), and the participants’ sex was not a 
contributing factor (P = 0.38).
There were differences between  GEcycle and  GErun 
(t = 80.8; P < 0.001; η2 = 0.99) as well as between  DEcycle 
and  DErun (t = 27.8; P < 0.001; η2 = 0.98) (Fig. 3). Moreo-
ver, there was a difference between  GEcycle and  DEcycle 
(t = − 5.85; P < 0.001; η2 = 0.79); however, no difference 
was found between  GErun and  DErun (t = − 0.40; P = 0.70; 
η2 = 0.018).
Discussion
The results of this study demonstrate that there is a large 
correlation between elite triathletes’ GE during running and 
cycling on a moderate uphill incline. However, no corre-
lation was found between DE during running and cycling. 
The results reveal that GE and DE differ between cycling 
and running, with large effect sizes, where cycling is more 
energy efficient than running on a moderate uphill incline.
The finding that  GErun and  GEcycle are strongly correlated 
(Fig. 1) is consistent with results of previous studies that 
reported a significant positive correlation between cyclists’ 
running economy and cycling economy when the economy 
of movement was measured during level running and ergom-
eter cycling, respectively (Lundby et al. 2017; Swinnen et al. 
2018). Moreover, in the current study, there was a signifi-
cant correlation between gross metabolic rates (i.e. oxygen 
uptake) during cycling and running. This result contradicts 
the result from recent study that found no significant rela-
tionship between the gross metabolic rates during running 
and cycling for nine sub-elite triathletes (Swinnen et al. 
10
9
8
7
26
25
24
23
22
21
20
19
GEcycle (%)
GErun (%)
Fig. 1  Significant relationship between gross efficiency during run-
ning  (GErun) and gross efficiency during cycling  (GEcycle) (P < 0.05)
10
9
8
7
26
25
24
23
22
21
20
19
DEcycle (%)
DErun (%)
Fig. 2  No significant relationship between delta efficiency dur-
ing running  (DErun) and delta efficiency during cycling  (DEcycle) 
(P > 0.05)
Fig. 3  Significant differences between gross efficiency during run-
ning  (GErun) and cycling  (GEcycle) is reported as †P < 0.001, and 
between delta efficiency during running  (DErun) and cycling  (DEcycle) 
is reported as ‡P < 0.001. Squares and circles represent mean values, 
and error bars represent ± 1 standard deviation
966
 
European Journal of Applied Physiology (2020) 120:961–968
1 3
2018); however, the relationship was close to significance 
(P = 0.053).
The large correlation between exercise-mode efficiencies 
found in the current study indicates that an elite triathlete 
with a high  GErun also has a high  GEcycle. This result con-
tradicts previous findings that the efficiency in one exercise 
mode does not predict the efficiency in other exercise modes 
(Daniels et al. 1984). However, it should be noted that in the 
study by Daniels et al. (1984), trained runners were tested in 
exercise modes outside their specific sport (i.e. bench step-
ping, arm cranking, graded walking and cycling) in addi-
tion to running. In the case of the participants in the current 
study, one can assume that to become a national level elite 
triathlete it is important to be efficient at all three disciplines 
in triathlon, which partly could explain the interrelationship 
of  GErun and  GEcycle.
The exercise efficiency is determined by the cardiores-
piratory, metabolic, neuromuscular and biomechanical effi-
ciencies (Barnes and Kilding 2015). The cardiorespiratory 
and metabolic efficiencies reflect the delivery of oxygen 
to the force-producing muscles and the adenosine triphos-
phate re-synthesis therein (Barnes and Kilding 2015; Saun-
ders et al. 2004). The neuromuscular and biomechanical 
efficiencies reflect the interactions between the neural and 
musculoskeletal systems as well as the efficiency of convert-
ing produced power to forward propulsion (Anderson 1996; 
Barnes and Kilding 2015). The energy expenditure during 
cycling and running is related to the increase in potential 
energy during the pedal cycle/stride cycle (i.e. the raising 
of centre of mass vertically during the pedal cycle/stride 
cycle), the translational kinetic energy (i.e. the braking and 
propelling of the body mass in the forward direction paral-
lel to the surface) and the rotational kinetic energy (i.e. the 
swinging of the legs and arms) as well as the maintenance of 
balance and energy cost of supporting body weight (Bergh 
1987; Hoogkamer et al. 2014). Hence, a triathlete’s GE is 
determined by these four underlying efficiencies (i.e. cardi-
orespiratory, metabolic, neuromuscular and biomechanical 
efficiency) and at least one of these underlying efficiencies is 
significantly higher for a ‘more efficient’ triathlete compared 
to their ‘less efficient’ counterpart.
In the current study, it was found that  GEcycle was sig-
nificantly lower than  DEcycle (Fig. 3), and the difference 
was associated with a large effect size. This difference is to 
a large extent explained by the influence of baseline energy 
expenditure in the GE calculations, which previously has 
suggested being an artefact (Gaesser and Brooks 1975). 
The relative contribution of the baseline energy expendi-
ture in the GE calculations decreases gradually with 
increasing work intensity; hence, it should be expected that 
the GE during cycling is related to work intensity. This 
is in line with a previous study that reported a positive 
relationship between cyclists’ GE and crank inertial load 
(Bertucci et al. 2012). In the running test, there was no dif-
ference between  GErun and  DErun, which means that triath-
letes’ GE during moderate uphill running does not change 
significantly with increasing work rate. Calculations of GE 
during uphill running, based on reported values for run-
ning speed and treadmill inclination as well as the mean 
values of body mass and oxygen uptake (Hoogkamer et al. 
2014), showed that GE was independent of running speed 
(2–3 m s−1); GE was calculated to be approximately 7% 
at a 2° incline and 10% at a 3° incline, which are in good 
agreement with the results in the current study.
When comparing  GErun with  GEcycle at the same exter-
nal work rate and treadmill incline, cycling was shown 
to be more energy efficient than running (21.1% versus 
8.2%, respectively) (Fig. 3), despite the limitation that the 
equation for calculating MWR during cycling does not 
account for the work done to overcome the friction of the 
drivetrain of the bicycle. Hence, the  GEcycle is therefore 
somewhat underestimated. At an equivalent metabolic 
energy expenditure rate, the mechanical power output 
during cycling was approximately 2.5 times higher than 
during running. Hence, based on the previously presented 
equation that endurance performance is equal to the sum 
of aerobic and anaerobic energy contributions multiplied 
by GE (Joyner and Coyle 2008), it can be concluded that 
the cycling speed is much higher than the running speed 
for a moderate uphill incline at the same work intensity as 
a consequence of the higher GE during cycling. The GE 
difference between cycling and running is to a large extent 
explained by differences in the factors related to force gen-
eration to support body weight, an increase in potential 
energy during the pedal cycle/stride cycle and to trans-
lational kinetic energy. Running entails a considerably 
higher vertical raising of the centre of mass (~ 8–10 cm) 
(Cavagna et al. 2005; Tartaruga et al. 2012) compared to 
cycling, where the raising of the centre of mass is mini-
mal during pedalling (Connolly 2016). The importance of 
having a relatively low vertical centre-of-mass displace-
ment during running is indicated by a lower energy cost 
and thus better running economy (Folland et al. 2017). 
Moreover, the stride cycle during running implies a decel-
eration at the foot plant, followed by an acceleration of 
body mass at push-off (Hamner et al. 2010). Based on fun-
damental physics, these deceleration/acceleration phases 
are associated with a significant energy cost; however, the 
relative energy-cost contribution related to translational 
kinetic energy decreases during uphill running, because 
on inclines the braking forces at the foot plant decrease 
(Gottschall and Kram 2005). In cycling, the fluctuation in 
speed during the pedal cycle is lower than in running due 
to the continuous supply of power (Fintelman et al. 2016; 
van Ingen Schenau et al. 1990). The described energy-
expenditure differences between the exercise modes result 
967
European Journal of Applied Physiology (2020) 120:961–968 
1 3
in a reduced biomechanical efficiency during running com-
pared to cycling, which is the major factor explaining the 
lower GE during running.
The corresponding reasoning could be applied to under-
stand the difference, and large effect size, between  DErun 
and  DEcycle (Fig. 3), because DE reflects how much a tri-
athlete needs to increase his/her MR for an increment in 
MWR. Hence, the enhanced biomechanical efficiency dur-
ing cycling compared to running is reflected in a higher 
 DEcycle than  DErun. This finding contradicts results from 
previous studies that reported higher DE during running 
(~ 44%) compared to cycling (~ 25%), where DE was 
derived from tests using a constant running speed with an 
incremental increase in treadmill inclination and station-
ary ergometer cycling (Bijker et al. 2001, 2002). Previ-
ously it was reported that the metabolic cost of running 
parallel to the running surface decreases with incline, 
whereas the efficiency of producing mechanical power 
to lift the centre of mass vertically is constant and inde-
pendent of incline and running speed (Hoogkamer et al. 
2014). Hence, the methodological differences (i.e. constant 
speed and incremental increase in incline vs. incremental 
increase in speed and constant incline) could to a large 
extent explain the contradicting DE values during running.
The correlation analysis showed that participants’ sex was 
not a contributing factor to the relationship between  GEcycle 
and  GErun. This result is in line with previously reported 
results where GE during cycling did not differ between male 
and female competitive cyclists when the sexes were com-
pared at the same relative intensity (Hopker et al. 2010). 
Moreover, in a recently published review investigating fac-
tors affecting the energy cost of running, it was concluded 
that men and women with the same body mass have similar 
running economies (Lacour and Bourdin 2015). However, 
because of the low number of participants in the current study 
further research is warranted to investigate potential sex dif-
ferences in GE for elite triathletes during cycling and running.
Conclusions
The results show that elite triathletes with high GE dur-
ing running also have a high GE during cycling, when 
exercising at a treadmill inclination of 2.5°. However, a 
triathlete’s relative efficiency in attaining an increased 
power output in terms of DE is not transferable between 
the two exercise modes. In a moderate uphill incline, elite 
triathletes are more energy efficient during cycling than 
running, independent of work rate.
Acknowledgements Open access funding provided by Dalarna 
University.
Author contributions All authors contributed to the study concep-
tion and design, data collection, and analysis. The first draft of the 
manuscript was written by MC and TC and all authors commented on 
previous versions of the manuscript. All authors read and approved 
the final manuscript.
Compliance with ethical standards 
Conflict of interest The authors declare that they have no conflicts of 
interest.
Ethical approval The study was approved by the Research Ethics Com-
mittee at Dalarna University, Falun, Sweden, and the test procedures 
were performed in accordance with the World Medical Association’s 
Declaration of Helsinki—Ethical Principles for Medical Research 
Involving Human Subjects 2008.
Informed consent Written informed consent was obtained from all 
individual participants included in the study.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, 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 changes 
were made. The images or other third party material in this article are 
included in the article’s Creative Commons licence, unless indicated 
otherwise in a credit line to the material. 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://creat iveco mmons .org/licen ses/by/4.0/.
References
Anderson T (1996) Biomechanics and running economy. Sports Med 
22:76–89. https ://doi.org/10.2165/00007 256-19962 2020-00003 
Barnes KR, Kilding AE (2015) Running economy: measurement, 
norms, and determining factors. Sports Med Open 1:8. https ://
doi.org/10.1186/s4079 8-015-0007-y
Bergh U (1987) The influence of body mass in cross-country skiing. 
Med Sci Sports Exerc 19:324–331
Bertucci WM, Betik AC, Duc S, Grappe F (2012) Gross efficiency and 
cycling economy are higher in the field as compared with on an 
axiom stationary ergometer. J Appl Biomech 28:636–644. https 
://doi.org/10.1123/jab.28.6.636
Bijker KE, De Groot G, Hollander AP (2001) Delta efficiencies of 
running and cycling. Med Sci Sports Exerc 33:1546–1551. https 
://doi.org/10.1097/00005 768-20010 9000-00019 
Bijker KE, de Groot G, Hollander AP (2002) Differences in leg muscle 
activity during running and cycling in humans. Eur J Appl Physiol 
87:556–561. https ://doi.org/10.1007/s0042 1-002-0663-8
Borg G (1970) Perceived exertion as an indicator of somatic stress. 
Scand J Rehabil Med 2:92–98
Carlsson M, Carlsson T, Wedholm L, Nilsson M, Malm C, Tonkonogi 
M (2016) Physiological demands of competitive sprint and dis-
tance performance in elite female cross-country skiing. J Strength 
Cond Res 30:2138–2144. https ://doi.org/10.1519/jsc.00000 00000 
00132 7
Cavagna GA, Heglund NC, Willems PA (2005) Effect of an increase in 
gravity on the power output and the rebound of the body in human 
968
 
European Journal of Applied Physiology (2020) 120:961–968
1 3
running. J Exp Biol 208:2333–2346. https ://doi.org/10.1242/
jeb.01661 
Cohen JW (1988) Statistical power analysis for the behavioral sciences, 
2nd edn. Lawrence Erlbaum Associates, Hillsdale
Connolly JW (2016) Understanding the magic of the bicycle. Morgan 
& Claypool, Bristol
Coyle EF, Feltner ME, Kautz SA, Hamilton MT, Montain SJ, Bay-
lor AM, Abraham LD, Petrek GW (1991) Physiological and 
biomechanical factors associated with elite endurance cycling 
performance. Med Sci Sports Exerc 23:93–107. https ://doi.
org/10.1249/00005 768-19910 1000-00015 
Coyle EF, Sidossis LS, Horowitz JF, Beltz JD (1992) Cycling effi-
ciency is related to the percentage of type I muscle fibers. Med 
Sci Sports Exerc 24:782–788. https ://doi.org/10.1249/00005 768-
19920 7000-00008 
Daniels JT, Scardina N, Foley P (1984) VO2max during five modes of 
exercise. In: Bachl N, Prokop L, Suckert R (eds) Proceedings of 
the world congress on sports medicine. Urban & Schwartzenberg, 
Vienna, pp 604–615
Dengel DR, Flynn MG, Costill DL, Kirwan JP (1989) Determinants of 
success during triathlon competition. Res Q Exerc Sport 60:234–
238. https ://doi.org/10.1080/02701 367.1989.10607 445
Ettema G, Lorås HW (2009) Efficiency in cycling: a review. Eur J Appl 
Physiol 106:1–14. https ://doi.org/10.1007/s0042 1-009-1008-7
Fintelman DM, Sterling M, Hemida H, Li FX (2016) Effect of different 
aerodynamic time trial cycling positions on muscle activation and 
crank torque. Scand J Med Sci Sports 26:528–534. https ://doi.
org/10.1111/sms.12479 
Folland JP, Allen SJ, Black MI, Handsaker JC, Forrester SE (2017) 
Running technique is an important component of running econ-
omy and performance. Med Sci Sports Exerc 49:1412–1423. https 
://doi.org/10.1249/mss.00000 00000 00124 5
Gaesser GA, Brooks GA (1975) Muscular efficiency during steady-
rate exercise: effects of speed and work rate. J Appl Physiol 
38:1132–1141
Gottschall JS, Kram R (2005) Ground reaction forces during down-
hill and uphill running. J Biomech 38:445–452. https ://doi.
org/10.1016/j.jbiom ech.2004.04.023
Hamner SR, Seth A, Delp SL (2010) Muscle contributions to propul-
sion and support during running. J Biomech 43:2709–2716. https 
://doi.org/10.1016/j.jbiom ech.2010.06.025
Hoogkamer W, Taboga P, Kram R (2014) Applying the cost of gen-
erating force hypothesis to uphill running. Peerj. https ://doi.
org/10.7717/peerj .482
Hopker J, Passfield L, Coleman D, Jobson S, Edwards L, Carter 
H (2009) The effects of training on gross efficiency in 
cycling: a review. Int J Sports Med 30:845–850. https ://doi.
org/10.1055/s-0029-12377 12
Hopker J, Jobson S, Carter H, Passfield L (2010) Cycling efficiency in 
trained male and female competitive cyclists. J Sports Sci Med 
9:332–337
Jones AM, Carter H (2000) The effect of endurance training on param-
eters of aerobic fitness. Sports Med 29:373–386. https ://doi.
org/10.2165/00007 256-20002 9060-00001 
Joyner MJ, Coyle EF (2008) Endurance exercise performance: the 
physiology of champions. J Physiol 586:35–44. https ://doi.
org/10.1113/jphys iol.2007.14383 4
Lacour JR, Bourdin M (2015) Factors affecting the energy cost of level 
running at submaximal speed. Eur J Appl Physiol 115:651–673. 
https ://doi.org/10.1007/s0042 1-015-3115-y
Landers GJ, Blanksby BA, Ackland TR, Smith D (2000) Morphology 
and performance of world championship triathletes. Ann Hum 
Biol 27:387–400. https ://doi.org/10.1080/03014 46005 00448 65
Le Meur Y, Hausswirth C, Dorel S, Bignet F, Brisswalter J, Bernard T 
(2009) Influence of gender on pacing adopted by elite triathletes 
during a competition. Eur J Appl Physiol 106:535–545. https ://
doi.org/10.1007/s0042 1-009-1043-4
Lundby C, Montero D, Gehrig S, Hall UA, Kaiser P, Boushel R, Lun-
dby AKM, Kirk N, Valdivieso P, Fluck M, Secher NH, Edin F, 
Hein T, Madsen K (2017) Physiological, biochemical, anthro-
pometric, and biomechanical influences on exercise economy 
in humans. Scand J Med Sci Sports 27:1627–1637. https ://doi.
org/10.1111/sms.12849 
Lusk G (1928) The elements of the science of nutrition, 4th edn. W. B. 
Saunders Co., Philadephia
Menzies P, Menzies C, McIntyre L, Paterson P, Wilson J, Kemi OJ 
(2010) Blood lactate clearance during active recovery after an 
intense running bout depends on the intensity of the active recov-
ery. J Sports Sci 28:975–982. https ://doi.org/10.1080/02640 
414.2010.48172 1
Minetti AE, Moia C, Roi GS, Susta D, Ferretti G (2002) Energy cost 
of walking and running at extreme uphill and downhill slopes. J 
Appl Physiol 93:1039–1046. https ://doi.org/10.1152/jappl physi 
ol.01177 .2001
Miura H, Kitagawa K, Ishiko T (1997) Economy during a simu-
lated laboratory test triathlon is highly related to Olympic 
distance triathlon. Int J Sports Med 18:276–280. https ://doi.
org/10.1055/s-2007-97263 3
Ofoghi B, Zeleznikow J, Macmahon C, Rehula J, Dwyer DB (2016) 
Performance analysis and prediction in triathlon. J Sports Sci 
34:607–612. https ://doi.org/10.1080/02640 414.2015.10653 41
Saunders PU, Pyne DB, Telford RD, Hawley JA (2004) Factors affect-
ing running economy in trained distance runners. Sports Med 
34:465–485. https ://doi.org/10.2165/00007 256-20043 4070-00005 
Schabort EJ, Killian SC, Gibson AS, Hawley JA, Noakes TD (2000) 
Prediction of triathlon race time from laboratory testing in 
national triathletes. Med Sci Sports Exerc 32:844–849. https ://
doi.org/10.1097/00005 768-20000 4000-00018 
Sleivert GG, Rowlands DS (1996) Physical and physiological factors 
associated with success in the triathlon. Sports Med 22:8–18. 
https ://doi.org/10.2165/00007 256-19962 2010-00002 
Swinnen W, Kipp S, Kram R (2018) Comparison of running and 
cycling economy in runners, cyclists, and triathletes. Eur J 
Appl Physiol 118:1331–1338. https ://doi.org/10.1007/s0042 
1-018-3865-4
Tartaruga MP, Brisswalter J, Peyre-Tartaruga LA, Avila AO, Alberton 
CL, Coertjens M, Cadore EL, Tiggemann CL, Silva EM, Kruel 
LF (2012) The relationship between running economy and bio-
mechanical variables in distance runners. Res Q Exerc Sport 
83:367–375. https ://doi.org/10.1080/02701 367.2012.10599 870
Tucker R (2013) Economy. In: Friel JG, Vance JS (eds) Triathlon Sci-
ence. Human Kinetics Press, Champaign.
van Ingen Schenau GJ, Vanwoensel W, Boots PJM, Snackers RW, 
Degroot G (1990) Determination and interpretation of mechanical 
power in human movement: application to ergometer cycling. Eur 
J Appl Physiol Occup Physiol 61:11–19. https ://doi.org/10.1007/
bf002 36687 
Publisher’s Note Springer Nature remains neutral with regard to 
jurisdictional claims in published maps and institutional affiliations.

Gross and delta efficiencies during uphill running and cycling among elite triathletes.
Carlsson, Magnus,Wahrenberg, Viktor,Carlsson, Marie S,Andersson, Rasmus,Carlsson, Tomas