Gross and delta efficiencies during uphill running and cycling among elite triathletes

Triathlon comprises stages of swimming, cycling and running 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 championship results from 2008 to 2012 revealed that the winners’ 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 performance is determined by the sum of the aerobic and anaerobic energy contribution multiplied by gross efficiency (GE) (Joyner and Coyle 2008). In triathlon, performance is mainly determined by maximal oxygen uptake (\(\dot{V}{\text{O}}_{{{\text{2max}}}}\)), lactate/ventilatory threshold and oxygen uptake kinetics, which together reflect the aerobic energy contribution, and exercise 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 \(\dot{V}{\text{O}}_{{{\text{2max}}}}\) for a given submaximal workload (i.e. better economy) has been suggested to be of great importance for success 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 biomechanical 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 Bourdin 2015). In cycling, mechanisms such as muscle-fibre-type transformation, changes in muscle-fibre-shortening velocities, 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 usually 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 economy 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 running 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 different 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 during cycling and running in elite triathletes.

The test results of the cycling and running test are presented 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 concentrations 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 DE_cycle = 23.5 ± 1.6% and DE_run = 8.3 ± 0.5%. All test variables were normally distributed (all P > 0.05). There was a correlation between GE_cycle and GE_run (r = 0.66; P = 0.038; R² = 0.44) (Fig. 1), and the participants’ sex was not a contributing factor (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 DE_cycle and DE_run (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 GE_cycle and GE_run (t = 80.8; P < 0.001; η² = 0.99) as well as between DE_cycle and DE_run (t = 27.8; P < 0.001; η² = 0.98) (Fig. 3). Moreover, there was a difference between GE_cycle and DE_cycle (t = − 5.85; P < 0.001; η² = 0.79); however, no difference was found between GE_run and DE_run (t = − 0.40; P = 0.70; η² = 0.018).

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 correlation 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 GE_run and GE_cycle 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 ergometer cycling, respectively (Lundby et al. 2017; Swinnen et al. 2018). Moreover, in the current study, there was a significant 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 relationship between the gross metabolic rates during running and cycling for nine sub-elite triathletes (Swinnen et al. 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 GE_run also has a high GE_cycle. This result contradicts 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 stepping, arm cranking, graded walking and cycling) in addition 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 GE_run and GE_cycle.

The exercise efficiency is determined by the cardiorespiratory, metabolic, neuromuscular and biomechanical efficiencies (Barnes and Kilding 2015). The cardiorespiratory and metabolic efficiencies reflect the delivery of oxygen to the force-producing muscles and the adenosine triphosphate re-synthesis therein (Barnes and Kilding 2015; Saunders et al. 2004). The neuromuscular and biomechanical efficiencies reflect the interactions between the neural and musculoskeletal systems as well as the efficiency of converting 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 parallel 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. cardiorespiratory, 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 GE_cycle was significantly lower than DE_cycle (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 expenditure 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 difference between GE_run and DE_run, which means that triathletes’ GE during moderate uphill running does not change significantly with increasing work rate. Calculations of GE during uphill running, based on reported values for running 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 GE_run with GE_cycle at the same external 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 GE_cycle 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 generation to support body weight, an increase in potential energy during the pedal cycle/stride cycle and to translational 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 minimal during pedalling (Connolly 2016). The importance of having a relatively low vertical centre-of-mass displacement 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 deceleration at the foot plant, followed by an acceleration of body mass at push-off (Hamner et al. 2010). Based on fundamental 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 in a reduced biomechanical efficiency during running compared to cycling, which is the major factor explaining the lower GE during running.

The corresponding reasoning could be applied to understand the difference, and large effect size, between DE_run and DE_cycle (Fig. 3), because DE reflects how much a triathlete needs to increase his/her MR for an increment in MWR. Hence, the enhanced biomechanical efficiency during cycling compared to running is reflected in a higher DE_cycle than DE_run. 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 stationary ergometer cycling (Bijker et al. 2001, 2002). Previously 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 independent 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 GE_cycle and GE_run. 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 compared at the same relative intensity (Hopker et al. 2010). Moreover, in a recently published review investigating factors 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 differences in GE for elite triathletes during cycling and running.

The results show that elite triathletes with high GE during 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.