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Differences in leg muscle activity during running and cycling in humans


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Delta (delta) efficiency is defined as the ratio of an increment in the external mechanical power output to the increase in metabolic power required to produce it. The purpose of the present study was to investigate whether differences in leg muscle activity between running and cycling can explain the observed difference in delta efficiency between the two activities. A group of 11 subjects performed incremental submaximal running and cycling tests on successive days. The delta efficiencies during running and cycling were based on five exercise stages. Electromyograph (EMG) measurements were made of three leg muscles (gastrocnemius, vastus lateralis and biceps femoris). Kendall's correlation coefficients between the mean EMG activity and the load applied were calculated for each muscle, for both running and cycling. As expected, the mean delta efficiency during running (42%) was significantly greater than that during cycling (25%). For cycling, all muscles showed a significant correlation between mean EMG activity and the load applied. For running, however, only the gastrocnemius muscle showed a significant, but low correlation ( r=0.33). The correlation coefficients of the vastus lateralis and biceps femoris muscles were not significantly different from 0. The results were interpreted as follows. In contrast to cycling, which includes only concentric contractions, during running up inclines eccentric muscle actions play an important role. With steeper inclines, more concentric contractions must be produced to overcome the external force, whereas the amount of eccentric muscle actions decreases. This change in the relative contribution of concentric and eccentric muscle actions, in combination with the fact that eccentric muscle actions require much less metabolic energy than concentric contractions, can explain the difference between the running and cycling delta efficiency.
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K.E. Bijker ÆG. de Groot ÆA.P. Hollander
Differences in leg muscle activity during running
and cycling in humans
Accepted: 29 May 2002 / Published online: 13 July 2002
Springer-Verlag 2002
Abstract Delta (D) efficiency is defined as the ratio of an
increment in the external mechanical power output to
the increase in metabolic power required to produce it.
The purpose of the present study was to investigate
whether differences in leg muscle activity between run-
ning and cycling can explain the observed difference in D
efficiency between the two activities. A group of
11 subjects performed incremental submaximal running
and cycling tests on successive days. The Defficiencies
during running and cycling were based on five exercise
stages. Electromyograph (EMG) measurements were
made of three leg muscles (gastrocnemius, vastus later-
alis and biceps femoris). Kendall’s correlation coeffi-
cients between the mean EMG activity and the load
applied were calculated for each muscle, for both run-
ning and cycling. As expected, the mean Defficiency
during running (42%) was significantly greater than that
during cycling (25%). For cycling, all muscles showed a
significant correlation between mean EMG activity and
the load applied. For running, however, only the gas-
trocnemius muscle showed a significant, but low corre-
lation (r=0.33). The correlation coefficients of the vastus
lateralis and biceps femoris muscles were not signifi-
cantly different from 0. The results were interpreted as
follows. In contrast to cycling, which includes only
concentric contractions, during running up inclines ec-
centric muscle actions play an important role. With
steeper inclines, more concentric contractions must be
produced to overcome the external force, whereas the
amount of eccentric muscle actions decreases. This
change in the relative contribution of concentric and
eccentric muscle actions, in combination with the fact
that eccentric muscle actions require much less metabolic
energy than concentric contractions, can explain the
difference between the running and cycling Defficiency.
Keywords Locomotion ÆEfficiency ÆMuscle
contraction type ÆElectromyography
The Defficiency of an activity is the ratio of an increment
in the external mechanical power output to the increase
in metabolic power required to produce it, and is
expressed as a percentage. The Defficiency during run-
ning is significantly greater than that during cycling
(Zacks 1973; Asmussen and Bonde-Petersen 1974; Bijker
et al. 2001). Furthermore, the Defficiency of running is
also much greater than the muscle efficiency, which is
estimated to have a maximal value of 29% (Cavanagh
and Kram 1985). Poole et al. (1992) compared efficien-
cies during cycling, using measurements of both pul-
monary and leg oxygen uptake (
). No significant
difference between these Defficiencies was observed and
therefore Poole et al. (1992) concluded that, during cy-
cling, processes other than those in the exercising leg
muscles do not substantially contribute to the increased
metabolic cost at greater power outputs. For running,
changes in the metabolic energy cost of processes other
than the exercising legs can only explain the great D
efficiency if the metabolic energy cost of these processes
decreases with running against greater applied loads
(steeper inclines). This seems unlikely. Therefore, as-
suming that for cycling as well as for running the exer-
cising legs determine the metabolic cost of the
movement, the difference in the Defficiency between
running and cycling must be due to a difference in the
functioning of the exercising leg muscles.
Electromyography (EMG) can be used to study
muscle activity non-invasively. Quantified EMG mea-
surements of muscles can indicate if the muscle force is
increasing or decreasing (Deluca 1997). In addition,
Eur J Appl Physiol (2002) 87: 556–561
DOI 10.1007/s00421-002-0663-8
K.E. Bijker (&)ÆG. de Groot ÆA.P. Hollander
Institute for Fundamental and Clinical Human
Movement Sciences, Faculty of Human Movement Sciences,
Vrije Universiteit Amsterdam, Van der Boechorststraat 9,
1081 BT Amsterdam, The Netherlands
Tel.: +31-20-4448459
Fax: +31-20-4448529
Komi et al. (1987) and Praagman (2001) have shown
clear relationships between the mean EMG activity of
concentric exercise and energy expenditure. However,
for eccentric muscle actions, no relationship between
mean EMG signals and applied load can be observed
(Komi et al. 1987). Therefore, the estimation of mean
EMG activity seems to be a good way of investigating
possible differences in the actions of the leg muscles
during running and cycling.
The purpose of the present study was to investigate
differences between running and cycling in the relation-
ships between mean EMG activity of the exercising leg
muscles and the external power output. Further, we in-
vestigated if such differences in muscle actions can ex-
plain the observed difference in Defficiency between
running and cycling.
A group of 11 healthy (7 men, 4 women) subjects participated in
this study. All subjects were informed about the protocol to be used
and gave written informed consent. Their [mean (SD)] age was
23.7 (4.0) years, body mass was 69.3 (7.9) kg and height was
1.79 (0.10) m.
All subjects performed a submaximal running and cycling test on
different days. After a warm up period of 10 min, the subjects
started the test protocol. Each test consisted of five, 6 min-long
exercise stages to ensure steady-state measurements, followed by a
rest of 4 min. Heart rate values (beatsÆper minute) were collected
using a heart rate monitor (Polar Vantage). To ensure that only the
aerobic energy system was involved, only those exercise trials with
heart rate values less than 85% of the maximal heart rate (Snyder
et al. 1994) and respiratory exchange ratios (R) values less than 0.95
were used in the calculation of Defficiency. Maximal heart rate was
estimated as (220 minus age in years).
During the running test, the subjects ran on a treadmill at a
freely chosen comfortable velocity (between 2.81 and 3.75 mÆs
Stride frequency was controlled with help of a metronome. The
subjects ran up inclines between 0% and 5% in random order. The
mechanical power (P
) increment between two successive incli-
nation values was about 20 W.
The study of Marsh et al. (2000) showed that pedal cadence
does not influence cycling Defficiency. Therefore, for practical
reasons, in the present study during the cycling test all subjects
cycled at a cadence of 80 r.p.m. The mean (SD) P
during the initial cycling stage was 56.2 (8.7) W. The difference
between two successive stages during cycling was about 25 W.
Cycling exercise stages were also presented in random order.
Calculation of Defficiency
During the last 2 min of each exercise stage, the volume flow rate,
and CO
concentrations of the expired gas were measured
breath-by-breath (Oxycon Champion, Mijnhart). The Rvalues
were calculated over the same period of time. Assuming purely
aerobic energy production, metabolic power (P
, in watts) was
calculated as (Garby and Astrup 1987):
Pmet ¼ð4940 Rþ16040Þ=60½
VV O2ð1Þ
is in litres per minute.
In the cycling test, the external P
was calculated from the
product of crank torque and crank angular velocity. In the running
test, extra external P
was calculated as:
Pmech ¼mbgvsinðaÞð2Þ
where m
is the mass of the subject (kilograms), gis the acceleration
due to gravity and equal to 9.81 mÆs
,vis the velocity of the tread-
mill (metres per second) and ais the angle of inclination (degrees).
For each subject, linear regression equations were calculated
from the extra external P
and associated P
data. The D
efficiencies were calculated from the regression coefficient of the
regression lines.
EMG measurements
The EMG signals from three superficial leg muscles [gastrocnemius
(lateral head), vastus lateralis and biceps femoris] were recorded
using Ag-AgCl surface electrodes with an inter electrode distance of
approximately 2.0 cm. Before attaching the electrodes, a skin im-
pedance below 2.5 kWwas ensured by shaving, sanding and cleaning
the skin. The EMG signals were amplified, analogue band-pass fil-
tered (10–200 Hz) and sampled at a frequency of 1,000 Hz. During
the running and cycling tests, EMG signals were recorded during the
first 20 s of the 5th min of each exercise stage. Off-line, the digital
EMG signals were corrected for offset, full-wave rectified and low-
pass filtered (12 Hz). From the 20 s recordings, mean EMG values
were calculated from ten successive running and cycling cycles.
Statistical analysis
For running as well as for cycling, for each muscle the non para-
metric Kendall’s correlation coefficient (Siegel and Castellan 1988)
was calculated between the mean EMG value and the external
output. Differences in Defficiency between running and cy-
cling were tested for significance using a Student’s t-test for paired
comparisons (P=0.05).
In Fig. 1 a typical example of the two regression lines
and the resulting Defficiencies is presented. As expected,
Fig. 1. Typical example of the regression lines for running and
cycling, calculated from the data for the extra external mechanical
power output and the metabolic power required. Both Defficiencies
(eff) are presented in the figure
the mean (SEM) Defficiency during running was sig-
nificantly greater than that during cycling [42% (3.2)
compared to 25% (1.5), P<0.001].
For cycling, all muscles showed a substantial increase
in mean EMG activity with increased P
(Fig. 2). For running however, only the gastrocnemius
muscle showed an increase in mean EMG activity
(Fig. 2). For the vastus lateralis muscle, the difference in
increase in EMG activity between cycling and running is
illustrated in Fig. 3, where raw rectified EMG data are
presented for each exercise stage of the running and
cycling test.
For cycling, all three leg muscles measured showed a
significant relationship between mean EMG activity and
the external P
output. For running however, only
the correlation coefficient of the gastrocnemius muscle
was significantly different from 0 (see Table 1).
The main finding of the present study was that during
running the vastus lateralis and biceps femoris muscles
did not show a relationship between the mean EMG
activity and the increased external P
output. For
cycling, all muscles measured did show a relationship
between EMG activity and external P
Ericson et al. (1985), van Ingen Schenau et al. (1997)
and Miura et al. (2000) concur that during cycling only
concentric muscle actions are involved. Komi et al.
(1987) and Shinohara et al. (1997) showed that there is a
positive relationship between the EMG activity of con-
centric exercise and the load applied. The results of the
present study therefore support the idea that during
cycling concentric muscle actions dominate. Since the
efficiency of concentric exercise has a maximal value of
30% (Cavanagh and Kram 1985), the Defficiency during
cycling should not exceed this value. In the present study
the mean cycling Defficiency was 25%. Results from
previous studies (Asmussen and Bonde-Petersen 1974;
Suzuki 1979; Coyle et al. 1992; Bijker et al. 2001) have
also shown that the Defficiency of cycling is indeed less
than the efficiency of concentric exercise.
Since Komi et al. (1987) did not observe a relationship
between EMG activity during eccentric muscle actions
and external P
output, our data for running suggest
that during running up shallow inclines non-concentric
muscle actions play an important role. There is little
doubt that level running includes stretch-shortening
cycles (Margaria 1976; Taylor 1985; van Ingen Schenau
et al. 1997). During the landing phase the active muscles
are stretched to decelerate body mass whereas during the
push off phase the active muscles are shortening. The
active stretch allows the tendons of the muscles to store
elastic energy, which can be re-used during the subse-
quent concentric phase. As a result, the gross efficiency
during running can be much greater than the muscle
efficiency (van Ingen Schenau et al. 1997; Ettema 2001).
Fig. 2. Increases in mean electromyogram (EMG) activity of three
leg muscles resulting from increases in external mechanical power
output during running (circles) and cycling (squares). The increase
in mean EMG activity is calculated as the percentage increase with
respect to the exercise stage having the lowest mechanical power
Previous studies suggested that during running up
inclines storage and re-use of elastic energy also takes
place, which could explain the large Defficiencies ob-
tained during running (Lloyd and Zacks 1972; Asmus-
sen and Bonde-Petersen 1974). According to van Ingen
Schenau (1984), however, during running up inclines,
work produced by the muscles to overcome the external
force is lost and can therefore not be stored. Since
muscles can only recover energy that has been previously
stored, the storage and re-use of elastic energy could not
explain the great running Defficiency. In such reasoning,
however, van Ingen Schenau (1984) assumes that
stretching of the muscle also implies a stretch of the
contractile elements of the muscle-tendon complex. Of
course, if that is the case it is difficult to explain how a
muscle can contribute to external work (De Haan et al.
1989). Roberts et al. (1997), however, showed that in the
gastrocnemius muscle of turkeys who ran on the level,
no stretch of the contractile elements occurred. Fur-
thermore, Kram and Taylor (1990) based their cost-of-
generating-force hypothesis on the idea that during level
running the contractile elements of the muscle operate
isometrically. Therefore, assuming that during running
on the level, as well as up inclines, in the stretching phase
no stretch of the contractile elements takes place, it is
likely that in both running situations (level and incli-
nation) storage and re-utilization of elastic energy plays
a role. Elastic energy storage and re-use can only explain
the great Defficiency during running if during running
up inclines the amount of elastic energy stored and re-
used increases. Minetti et al. (1994), however, asserted
that during running up any gradient a fixed maximal
amount of elastic energy is stored and re-used. There-
fore, although storage and re-use of elastic energy
probably improves the economy of running up inclines,
it cannot explain the great Defficiency during running.
As stated above, stretch-shortening cycles include
both eccentric and concentric muscle actions. During
running up inclines, the amount of concentric contrac-
tions has to increase to overcome the external force (i.e.
gravity) whereas the contribution of eccentric muscle
decreases (Minetti et al. 1994; Taylor 1994), but will still
be substantial. It is well known that the metabolic cost
of eccentric muscle actions is much less than that of
concentric contractions (Asmussen 1953; Bigland-
Ritchie and Woods 1976; Rall 1985). As Minetti et al.
(1993) showed, a change in the relative contribution of
concentric and eccentric muscle actions, combined with
the difference in metabolic costs between both muscle
actions will lead to very small increases in the metabolic
cost for running up shallow inclines. Consequently, for
running up shallow inclines, as used in the present study,
the Defficiency will be much greater than the concentric
muscle efficiency. During running up steep inclines,
concentric contractions will probably dominate, just as
they do during cycling. As a result, the Defficiency
during running up steep inclines should be much less
than the Defficiency during running up shallow inclines.
It would be interesting to test this hypothesis in further
Fig. 3. A typical example of raw rectified electromyogram (EMG)
data of the vastus lateralis muscle for the different exercise stages
during cycling (left column) and running (right column)
Table 1. Kendall’s correlation
coefficients between mean
electromyogram (EMG) values
and external P
Gastrocnemius muscle Vastus lateralis muscle Biceps femoris muscle
Cycling 0.55
Running 0.33
0.02 0.06
Correlation coefficient is significantly different from 0
The gastrocnemius muscle has a large compliant
Achilles tendon, which seems to be ideal for stretching.
Therefore, at first sight it seems strange that this muscle
shows a significant relationship between the mean EMG
activity and the load applied during running up shallow
inclines, suggesting that concentric contractions play an
important role. Hof and van den Berg (1983) showed
that there is a difference between ankle plantar flexor
and knee extensor muscles in their contribution of ec-
centric and concentric muscle actions during level run-
ning. Whereas, during level running, for the knee
extensors the contribution of eccentric muscle actions
was substantial, for the ankle plantar flexors concentric
contractions already played a prominent role. Conse-
quently, during running up inclines, where more con-
centric contractions must be produced, the
gastrocnemius muscle (which is an ankle plantar flexor)
will show a much better relationship between EMG
activity and the load applied than the vastus lateralis
muscle (knee extensor).
In our explanation of the great Defficiency of run-
ning, as well as a constant amount of elastic energy
stored and re-used, we have also assumed that the
amount of internal P
produced remained constant
during running up shallow inclines. Minetti et al. (1994),
indeed, showed that for the range of inclines that was
used in the present study, the amount of internal P
produced remained nearly constant and would therefore
not influence the Defficiency during running. Another
assumption we made was that the average vertical
ground reaction force will not change substantially
during running up shallow inclines. Since the metabolic
cost of running seems to be directly proportional to the
average vertical ground reaction force (Kram 2000) a
change in this force would influence the Defficiency.
However, the inclines used in the present study were so
small that the change in the average vertical ground
reaction force, and consequently also in the metabolic
cost of supporting the mass of the body, would have
been negligible and would not have influenced the D
efficiency during running.
In conclusion, the present study showed a difference
between running and cycling in the relationship between
the mean EMG activity of leg muscles and the load
applied. This difference can be used to explain the ob-
served difference in Defficiency between the two types of
locomotion. For cycling, the high correlation coefficients
confirm the theory that concentric contractions are the
main muscle actions. For running, the lack of a rela-
tionship between EMG activity and the load applied
suggests that during running up shallow inclines, ec-
centric muscle actions also play an important role. A
change in the relative contributions of concentric and
eccentric muscle actions, combined with the large dif-
ference in metabolic cost between both muscle action
types can explain the great Defficiency during running.
Acknowledgements The authors gratefully acknowledge J. Gerrit-
sen and L. Snel for their assistance in collecting the data and Dr.
R. Kram for his useful comments on a previous version of the
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Participation and performance trends of male and female athletes have been thoroughly analyzed in various endurance sports. Knowing these trends can help coaches and athletes prepare for competitions and may influence their training strategy and career planning. However, duathlon events—consisted of two splits of running (Run1 and Run2) interspersed by a split of cycling (Bike)—have not been thoroughly studied, unlike other endurance sports. The present study aimed to compare participation and performance trends in duathletes who competed in duathlon races hosted by World Triathlon or affiliated National Federations between 1990 and 2021. A total of 25,130 results of age group finishers who competed in run-bike-run duathlon races of varying distances were analyzed with different general linear models. Races were divided into three distances: short-distance (up to 5.5 km run, 21 km bike, 5 km run), medium-distance (5–10 km run, 30–42 km bike, 7–11 km run) and long-distance (at least 14 km run, 60 km bike, 25 km run). On average, women represented 45.6% of all finishers in short-distance, 39.6% in medium-distance and 24.9% in long-distance duathlon races. Throughout the years, men were consistently faster than women in all three race legs (Run 1, Bike, and Run 2) in all three distances across all age groups, and women could not reduce the performance gap. Concerning the age of peak performance, duathletes of the age group 30–34 finished most often in the top three in short- and medium-distance duathlons, whereas male duathletes of the age group 25–29 and female duathletes of the age group 30–34 finished most often in the top three in long-distance duathlons. Women participated less, especially in longer distances, and were constantly slower than men. Duathletes of the age group 30–34 finished most often in the top three. Future studies should analyze participation and performance trends in further subgroups (e.g., elite athletes) and pacing behaviours.
... The endurance protocol in the present study was specific to the muscle group of the explosive strength test, whereas running has been shown to induce less loads on thighs compared to cycling. 21 Similarly, in the present study the explosive strength was tested dynamically whereas, in the aforementioned study the explosive strength was evaluated using isometric rate of force development at one specific angle only. Importantly, it should be noted that we used long high-intensity intervals on a bicycle ergometer in the present study and that the influence on explosive strength may differ with the endurance training modality (eg, cycling vs running). ...
Purpose: We aimed at investigating the acute effects of lower-body high-intensity interval training (HIIT) on upper- and lower-body explosive strength assessed by mean propulsive velocity (MPV) in naturally menstruating women. In addition, we assessed the combination of lower-body HIIT and squat, as well as lower-body HIIT and bench press, on bench press and squat MPV. Methods: Thirteen women (age: 23 [2] y, menstrual cycle length: 28.4 [2.0] d) completed 2 training modalities on separate days (separated by 30 [4.2] d) consisting of HIIT followed by lower-body (HIIT + LBS) or upper-body (HIIT + UBS) strength loading. Squat and bench press MPV were assessed before HIIT (T0), after HIIT (T1), after the strength loading (T2), and 24 hours postloading (T3). Results: Mixed factorial analysis of variance indicated a significant effect for time in bench press and squat (P < .001) but not for interaction. Pairwise comparison showed that bench press MPV remained unchanged (P = 1.000) at T1 but was reduced at T2 compared with T0 (HIIT + LBS: -8.2% [3.9%], HIIT + UBS: -13.8% [12.1%], P < .001) and T1 (HIIT + LBS: -7.1% [3.2%], HIIT + UBS: -12.7% [8.7%], P < .001). Squat MPV decreased at T1 (HIIT + LBS: -6.0% [8.8%], HIIT + UBS: -4.8% [5.4%], P = .009) and was found to be decreased at T2 compared with T0 in both conditions (HIIT + LBS: -6.9% [3.3%], HIIT + UBS: -7.4% [6.1%], P < .001) but not compared with T1 (P = 1.000). Bench press and squat MPV returned to baseline at T3 compared with T0 (P > .050). Conclusion: Lower- but not upper-body explosive strength was reduced by HIIT. HIIT combined with upper- or lower-body strength loading resulted in a reduction of squat and bench press explosive strength.
... Therefore, in running, force production would possibly have been enhanced for a given neural input (de Haan et al. 1991) delaying the onset of peripheral fatigue with a lower recruitment of type II motor units during running compared with cycling HIIT. Muscle activation across a number of muscle groups (i.e., knee extensors, knee flexors and plantar flexors) is likely to have occurred in running, whereas concentric actions of the knee extensors would predominate in cycling leading to a lower efficiency (Bijker et al. 2002). A greater contribution of the upper body musculature to overall V O 2 during running means the metabolic cost of upper body exercise during cycling makes a smaller contribution to the total exercise V O 2 . ...
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Purpose The acute physiological, perceptual and neuromuscular responses to volume-matched running and cycling high intensity interval training (HIIT) were studied in team sport athletes. Methods In a randomized cross-over design, 11 male team sport players completed 3 × 6 min (with 5 min between sets) repeated efforts of 15 s exercising at 120% speed (s $$\dot{\text{V}}$$ V ˙ O 2max ) or power (p $$\dot{\text{V}}$$ V ˙ O 2max ) at $$\dot{\text{V}}$$ V ˙ O 2max followed by 15 s passive recovery on a treadmill or cycle ergometer, respectively. Results Absolute mean $$\dot{\text{V}}$$ V ˙ O 2 (ES [95% CI] = 1.46 [0.47–2.34], p < 0.001) and heart rate (ES [95% CI] = 1.53 [0.53–2.41], p = 0.001) were higher in running than cycling HIIT. Total time at > 90% $$\dot{\text{V}}$$ V ˙ O 2max during the HIIT was higher for running compared to cycling (ES [95% CI] = 1.21 [0.26–2.07], p = 0.015). Overall differential RPE (dRPE) (ES [95% CI] = 0.55 [− 0.32–1.38], p = 0.094) and legs dRPE (ES [95% CI] = − 0.65 [− 1.48–0.23], p = 0.111) were similar, whereas breathing dRPE (ES [95% CI] = 1.01 [0.08–1.85], p = 0.012) was higher for running. Maximal isometric knee extension force was unchanged after running (ES [95% CI] = − 0.04 [− 0.80–0.8], p = 0.726) compared to a moderate reduction after cycling (ES [95% CI] = − 1.17 [− 2.02–0.22], p = 0.001). Conclusion Cycling HIIT in team sport athletes is unlikely to meet the requirements for improving run-specific metabolic adaptation but might offer a greater lower limb neuromuscular load.
... Moreover, the type of muscle contraction could also affect oxygen demand. For instance, the primary contraction type of the lower limb muscle groups in cycling is concentric contraction (Bijker et al., 2002), whereas the squatting movement involves eccentric contraction that requires less oxygen (Douglas et al., 2017). Another critical factor is the mode of recovery during the recovery period. ...
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Purpose: The purpose of the present study was to compare the acute physiological and perceptual responses between resistance-type high-intensity interval training (R-HIIT)and cycling high-intensity interval training (C-HIIT). Methods: Twelve healthy and active men randomly performed C-HIIT and R-HIIT. The C-HIIT protocol was performed on a cycle ergometer and consisted of ten 60 s working intervals at 90% PPO separated by a 60 s active recovery at 25% PPO. The R-HIIT protocol consisted of ten 60 s working intervals (barbell back squat with a load of 20% bodyweight, maximum 30 reps) separated by 60 s passive recovery period in an unloaded standing position. Oxygen consumption ( V ˙ O 2 ), heart rate (HR), energy expenditure (EE) and rating of perceived exertion (RPE) were measured during exercise. Blood lactate concentration (Blac), serum testosterone and cortisol, and heart rate variability (HRV) were measured before and after exercise. Results: Peak ( p < 0.05) and average V ˙ O 2 ( p < 0.001), aerobic ( p < 0.001) and total EE ( p < 0.05) were higher during C-HIIT compared to R-HIIT. Blac after exercise ( p < 0.05) and anaerobic glycolytic EE ( p < 0.05) during exercise were higher in R-HIIT compared to C-HIIT. No differences ( p > 0.05) in peak and average HR, serum testosterone and cortisol, HRV, and RPE responses were observed between C-HIIT and R-HIIT. Conclusion: The R-HIIT protocol can elicit similar cardiovascular, hormones, and perceptual responses as C-HIIT but with a higher contribution to the anaerobic glycolysis energy system. In contrast, C-HIIT is superior to R-HIIT for increasing oxygen consumption during exercise. Therefore, the two types of HIIT may lead to different metabolic and neuromuscular adaptations.
... This type of varied and prolonged muscle action during intense cycling effort related to generating high power output can severely stress skeletal muscle [8] and cause structural damage within the muscle fiber [9], which had previously been confirmed in MTB cyclists [10]. The varied static, eccentric, and concentric phases can be observed in MTB [4], while in road cycling the concentric muscle action is definitely dominant [11]. ...
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The aim of this study was to determine if 1 h after a cycling race, changes in plasma creatine kinase activity (CK) and myoglobin concentrations (MB) differ between mountain bike and road cyclists and if these changes show any correlation with race performance. Male mountain bike cyclists (n = 11) under 23 years old and male road cyclists (n = 14), also under 23 years old, were studied following one of their respective races. The cyclists had blood drawn 2 h before and 1 h after the race to assess CK and MB, then the change in pre- and post-race difference was calculated (ΔCK and ΔMB). Each cyclist’s performance time was recorded and the time difference from the winner was calculated (TD). The cyclists’ aerobic capacity was assessed during the incremental test, which determines maximal oxygen uptake and maximal aerobic power. It was observed that 1 h after the cycling race, CK (p = 0.001, η2 = 0.40, F = 15.6) and MB (p = 0.000, η2 = 0.43, F = 17.2) increased, compared to pre-race values. Post-race CK increased only in road cyclists, while post-race MB increased only in mountain bike cyclists. Smaller TD were found for lower ΔMB in road cyclists but for higher ΔCK in mountain bike cyclists.
... Cycling primarily affects the lower body and the muscular system, causing a more local and muscular internal load. By contrast, running is more challenging for the whole body, with more influence on the vestibular, proprioceptive, and visual systems, causing a global internal load (Bijker, De Groot, & Hollander, 2002;Nardone et al., 1997). Nevertheless, since the latter are the systems primarily involved in dynamic postural control (Fusco et al., 2020), the effect was expected to be greater after running than after cycling (Paillard, 2012;Wright et al., 2013). ...
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Dynamic postural control is one of the essential factors in situations where non-contact injuries mainly occur, i.e., landing, cutting, or stopping. Therefore, testing of dynamic postural control should be implemented in injury risk assessment. Moreover, non-contact injuries mainly occur under loaded conditions when the athlete is physically stressed. Therefore, risk factors and mechanisms of these injuries should also be regarded under loading conditions and not only when the athlete is recovered. Current studies examining the influence of physical load on risk factors, such as dynamic postural control, often use cycling protocols to stress the participants. Nevertheless, most types of sports require running as a central element and the induced internal load after cycling might not be the same after running. Therefore, the current study aimed to examine the influence of a running and a cycling protocol on dynamic postural control and to determine the potential injury risk under representative conditions. In total, 128 sport students (64 males and 64 females, age: 23.64 ± 2.44, height: 176.54 ± 8.96 cm, weight: 68.85 ± 10.98 kg) participated in the study. They were tested with the Y Balance Test before and after one loading protocol. A total of 64 participants completed a protocol on a cycle ergometer and the other 64 on a treadmill. A mixed ANOVA showed significant interactions of time and load type. Dynamic postural control was reduced immediately after cycling but did not change after running. These findings indicate a load type dependence of dynamic postural control that must be considered while assessing an athlete’s potential injury risk and they support the need for more representative designs.
Endurance exercise performance is known to be closely associated with the three physiological pillars of maximal O2 uptake ( V ̇ O 2 max $\dot{V}_{{\rm O}_{2}{\rm max}}$ ), economy or efficiency during submaximal exercise, and the fractional utilisation of V ̇ O 2 max $\dot{V}_{{\rm O}_{2}{\rm max}}$ (linked to metabolic/lactate threshold phenomena). However, while 'start line' values of these variables are collectively useful in predicting performance in endurance events such as the marathon, it is not widely appreciated that these variables are not static but are prone to significant deterioration as fatiguing endurance exercise proceeds. For example, the 'critical power' (CP), which is a composite of the highest achievable steady-state oxidative metabolic rate and efficiency (O2 cost per watt), may fall by an average of 10% following 2 h of heavy intensity cycle exercise. Even more striking is that the extent of this deterioration displays appreciable inter-individual variability, with changes in CP ranging from <1% to ∼32%. The mechanistic basis for such differences in fatigue resistance or 'physiological resilience' are not resolved. However, resilience may be important in explaining superlative endurance performance and it has implications for the physiological evaluation of athletes and the design of interventions to enhance performance. This article presents new information concerning the dynamic plasticity of the three 'traditional' physiological variables and argues that physiological resilience should be considered as an additional component, or fourth dimension, in models of endurance exercise performance.
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Background Exercise training performed at maximal fat oxidation (FATmax) is an efficient non-pharmacological approach for the management of obesity and its related cardio-metabolic disorders. Objectives Therefore, this work aimed to provide exercise intensity guidelines and training volume recommendations for maximizing fat oxidation in patients with obesity. Methods A systematic review of original articles published in English, Spanish or French languages was carried out in EBSCOhost, PubMed and Scopus by strictly following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement. Those studies that analyzed maximal fat oxidation (MFO) and FATmax in patients with obesity (body fat > 25% for men; > 35% for women) by calculating substrate oxidation rates through indirect calorimetry during a graded exercise test with short-duration stages (< 10 min) were selected for quantitative analysis. The accuracy of relative oxygen uptake (% peak oxygen uptake [% ̇VO2peak]) and relative heart rate (% peak heart rate [%HRpeak]) for establishing FATmax reference values was investigated by analyzing their intra-individual and inter-study variation. Moreover, cluster analysis and meta-regression were used for determining the influence of biological factors and methodological procedures on MFO and FATmax. Results Sixty-four manuscripts were selected from 146 records; 23 studies only recruited men (n = 465), 14 studies only evaluated women (n = 575), and 27 studies included individuals from both sexes (n = 6434). The majority of the evaluated subjects were middle-aged adults (aged 40–60 y; 84%) with a poor cardiorespiratory fitness (≤ 43 mL·kg−1·min−1; 81%), and the reported MFO ranged from 0.27 to 0.33 g·min−1. The relative heart rate at FATmax (coefficient of variation [CV]: 8.8%) showed a lower intra-individual variation compared with relative oxygen uptake (CV: 17.2%). Furthermore, blood lactate levels at FATmax ranged from 1.3 to 2.7 mmol·L−1 while the speed and power output at FATmax fluctuated from 4 to 5.1 km·h−1 and 42.8–60.2 watts, respectively. Age, body mass index, cardiorespiratory fitness, FATmax, the type of ergometer and the stoichiometric equation used to calculate the MFO independently explained MFO values (R2 = 0.85; p < 0.01). The MFO in adolescents was superior in comparison with MFO observed in young and middle-aged adults. On the other hand, the MFO was higher during treadmill walking in comparison with stationary cycling. Body fat and MFO alone determined 29% of the variation in FATmax (p < 0.01), noting that individuals with body fat > 35% showed a heart rate of 61–66% HRpeak while individuals with < 35% body fat showed a heart rate between 57 and 64% HRpeak. Neither biological sex nor the analytical procedure for computing the fat oxidation kinetics were associated with MFO and FATmax. Conclusion Relative heart rate rather than relative oxygen uptake should be used for establishing FATmax reference values in patients with obesity. A heart rate of 61–66% HRpeak should be recommended to patients with > 35% body fat while a heart rate of 57–64% HRpeak should be recommended to patients with body fat < 35%. Moreover, training volume must be higher in adults to achieve a similar fat oxidation compared with adolescents whereas exercising on a treadmill requires a lower training volume to achieve significant fat oxidation in comparison with stationary cycling.
Repeated, episodic bouts of skeletal muscle contraction undertaken frequently as structured exercise training is a potent stimulus for physiological adaptation in many organs. Specifically in skeletal muscle, remarkable plasticity is demonstrated by the remodeling of muscle structure and function in terms of muscular size, force, endurance, and contractile velocity as a result of the functional demands induced by various types of exercise training. This plasticity, and the mechanistic basis for adaptations to skeletal muscle in response to exercise training, is underpinned by activation and/or repression of molecular pathways and processes induced in response to each individual acute exercise session. These pathways include the transduction of signals arising from neuronal, mechanical, metabolic, and hormonal stimuli through complex signal transduction networks, which are linked to a myriad of effector proteins involved in the regulation of pre- and post-transcriptional processes, and protein translation and degradation processes. This review therefore describes acute exercise-induced signal transduction and the molecular responses to acute exercise in skeletal muscle including emerging concepts such as epigenetic pre- and post-transcriptional regulation, and the regulation of protein translation and degradation. A critical appraisal of methodological approaches and the current state of knowledge informs a series of recommendations offered as future directions in the field.
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The amount of energy used to run a mile is nearly the same whether it is run at top speed or at a leisurely pace (although it is used more rapidly at the higher speed). This puzzling independence of energy cost and speed is found generally among running animals, although, on a per gram basis, cost is much higher for smaller animals. Running involves little work against the environment; work is done by muscles and tendons to lift and accelerate the body and limbs. Some of the work is recovered from muscle-tendon springs without metabolic cost and work rate does not parallel metabolic rate with either speed or size. Regardless of the amount of work muscles do, they must be activated and develop force to support the weight of the body. Load-carrying experiments have shown that the cost of supporting an extra newton of load is the same as the weight-specific cost of running. Size differences in cost are proportional to stride frequency at equivalent speeds, suggesting that the time available for developing force is important in determining cost. We report a simple inverse relationship between the rate of energy used for running and the time the foot applies force to the ground during each stride. These results support the hypothesis that it is primarily the cost of supporting the animal's weight and the time course of generating this force that determines the cost of running.
This lecture explores the various uses of surface electromyography in the field of biomechanics. Three groups of applications are considered: those involving the activation timing of muscles, the force/EMG signal relationship, and the use of the EMG signal as a fatigue index. Technical considerations for recording the EMG signal with maximal fidelity are reviewed, and a compendium of all known factors that affect the information contained in the EMG signal is presented. Questions are posed to guide the practitioner in the proper use of surface electromyography. Sixteen recommendations are made regarding the proper detection, analysis, and interpretation of the EMG signal and measured force. Sixteen outstanding problems that present the greatest challenges to the advancement of surface electromyography are put forward for consideration. Finally, a plea is made for arriving at an international agreement on procedures commonly used in electromyography and biomechanics.
It is widely accepted that the series elastic component (SEC) of muscles and tendons plays an important role in dynamic human movements. Many experiments seem to show that during a pre-stretch movement energy can be stored in the SEC which is re-used during the subsequent concentric contraction. Mechanical calculations were performed to calculate the capacity for muscles and tendons to store elastic energy. The storage of elastic energy in muscle tissue appears to be negligible. In tendons some energy can be stored but the total elastic capacity of the tendons of the lower extremities appears far too small to explain reported advantages of a pre-stretch during jumping and running.Based on literature concerning chemical change and enthalpy production during experiments on isolated muscles, a model is proposed which can explain the advantages of a preliminary counter movement on force and work output during the subsequent concentric contraction. The main advantage of a pre-stretch, as seen in movements like jumping, throwing and running, seems to be to prevent a waste of cross bridges at the onset of a contraction in taking up the slack of the muscle. The model can explain why the mechanical efficiency in running can be much higher than in cycling. A muscle which is stretched prior to concentric contraction can do more work at the same metabolic cost when compared with a concentric contraction without pre-stretch.
This target article addresses the role of storage and reutilization of elastic energy in stretch-shortening cycles. It is argued that for discrete movements such as the vertical jump, elastic energy does not explain the work enhancement due to the prestretch. This enhancement seems to occur because the prestretch allows muscles to develop a high level of active state and force before starting to shorten. For cyclic movements in which stretch- shortening cycles occur repetitively, some authors have claimed that elastic energy enhances mechanical efficiency. In the current article it is demonstrated that this claim is often based on disputable concepts such as the efficiency of positive work or absolute work, and it is argued that elastic energy cannot affect mechanical efficiency simply because this energy is not related to the conversion of metabolic energy into mechanical energy. A comparison of work and efficiency measures obtained at different levels of organization reveals that there is in fact no decisive evidence to either support or reject the claim that the stretch- shortening cycle enhances muscle efficiency. These explorations lead to the conclusion that the body of knowledge about the mechanisms and energetics of the stretch-shortening cycle is in fact quite lean. A major challenge is to bridge the gap between knowledge obtained at different levels of organization, with the ultimate purpose of understanding how the intrinsic properties of muscles manifest themselves under in-vivo-like conditions and how they are exploited in whole-body activities such as running. To achieve this purpose, a close cooperation is required between muscle physiologists and human movement scientists performing inverse and forward dynamic simulation studies of whole-body exercises.
The mechanical activity of the human quadriceps muscle during maximal incremental cycle ergometry was investigated by mechanomyography (MMG). MMG and surface electromyography (EMG) recordings of vastus lateralis muscle activity were obtained from nine males. Cycle ergometry was performed at 60 rev/min and work load was incremented step wise by 20 W (3.2 Nm) every minute until volitional fatigue. The mean amplitudes of MMG (mMMG) and EMG (mEMG) during the contraction phase were calculated from the last six contractions in each load. The duration, load and work rate of exercise at exhaustion were 13.3 (1.6) min, 44.1 (5.5) Nm, 276.7 (34.7) W, respectively. A linear relationship between mMMG and load was evident in each subject (r = 0.868–0.995), while mEMG seemed to dissociate as the load became greater. In the grouped mean data, mMMG was linearly related to load whether aligned to the absolute (r = 0.995) or maximal (r = 0.995) load. Involvement of the noise component was further investigated by studying passive cycling by four subjects. Pedals were rotated passively for the first half of each stage (PAS) and the subject then pushed the pedals for the second half (ACT). In the lighter load region, the mMMG of ACT was as small as that of PAS. However, the change in the mMMG of PAS was very small compared with that of ACT. In conclusion, this study demonstrates a linear relationship between the mMMG of the quadriceps muscle and work load during maximal incremental cycle ergometry. The effect of movement noise was thought to be small and stable.
The influence of different percentages of slow-twitch (ST) and fast-twitch (FT) fibers in vastus lateralis on delta efficiency expressed by delta work (x)/delta energy liberation (y) in y = a + bx was studied in six subjects during cycling on an ergometer at 60 or 100 rpm at work loads below 80% of VO2max. Three subjects had an average of 78% ST fibers (ST group) and the other subjects had an average of 76% FT fibers (FT group). There was no difference between the two groups in delta efficiency at 60 rpm, but at 100 rpm the efficiency of the ST group was significantly lower than that of the FT group (19.6 vs. 28.8%, P less than 0.01). In the ST group respiratory exchange ratio (R) was higher at 100 rpm than at 60 rpm, but the FT group had similar R values at both pedal revolution rates. The most important finding was the reduced efficiency when pedaling frequency was increased from 60 to 100 rpm in the ST group (23.3 to 19.6%). Predominant use of ST fibers at rapid pedal rates may require a substantial increase in energy expenditure.
1. Integrated electromyogram (e.m.g.) from the vastus lateralis muscles, and steady-state rates of oxygen uptake, were measured simultaneously during the performance of set rates of positive (concentric) and negative (eccentric) work at 50 rev/min on a motorized bicycle ergometer. 2. Similar experiments were also carried out at other pedalling rates and using other leg muscles. 3. The relationships between each of the variables (integrated e.m.g., oxygen consumption) and mean torque on pedals were found to be linear (r greater than 0-98) with a remarkable degree of reproducibility in surface e.m.g. for each subject over several months. 4. The ratio of the e.m.g. slopes at 50 rev/min (positive/negative) was 1-96 +/- 0-12 while the same ratio for the oxygen uptake slopes was 6-34 +/- 0-82. The discrepancy between the ratios suggests that not only is less muscle fibre activity required to maintain the same exerted force during negative work exercise, but there is also a substantial reduction in the oxygen uptake when the fibres are stretched. This was observed for all speeds of pedalling.
We determined that the variability in the oxygen cost and thus the caloric expenditure of cycling at a given work rate (i.e., cycling economy) observed among highly endurance-trained cyclists (N = 19; mean +/- SE; VO2max, 4.9 +/- 0.1 l.min-1; body weight, 71 +/- 1 kg) is related to differences in their % Type I muscle fibers. The percentage of Type I and II muscle fibers was determined from biopsies of the vastus lateralis muscle that were histochemically stained for ATPase activity. When cycling a Monark ergometer at 80 RPM at work rates eliciting 52 +/- 1, 61 +/- 1, and 71 +/- 1% VO2max, efficiency was determined from the caloric expenditure responses (VO2 and RER using open circuit spirometry) to steady-state exercise. Gross efficiency (GE) was calculated as the ratio of work accomplished.min-1 to caloric expenditure.min-1, whereas delta efficiency (DE) was calculated as the slope of this relationship between approximately 50 and 70% VO2max. The % Type I fibers ranged from 32 to 76%, and DE when cycling ranged from 18.3 to 25.6% in these subjects. The % Type I fibers was positively correlated with both DE (r = 0.85; P less than 0.001; N = 19) and GE (r = 0.75; P less than 0.001; N = 19) during cycling. Additionally, % Type I fibers was positively correlated with GE (r = 0.74; P less than 0.001; N = 13) measured during the novel task of two-legged knee extension; performed at a velocity of 177 +/- 6 degrees.s-1 and intensity of 50 and 70% of peak VO2 for that activity.(ABSTRACT TRUNCATED AT 250 WORDS)
Insights into muscle energetics during exercise (e.g., muscular efficiency) are often inferred from measurements of pulmonary gas exchange. This procedure presupposes that changes of pulmonary O2 (VO2) associated with increases of external work reflect accurately the increased muscle VO2. The present investigation addressed this issue directly by making simultaneous determinations of pulmonary and leg VO2 over a range of work rates calculated to elicit 20-90% of maximum VO2 on the basis of prior incremental (25 or 30 W/min) cycle ergometry. VO2 for both legs was calculated as the product of twice one-leg blood flow (constant-infusion thermodilution) and arteriovenous O2 content difference across the leg. Measurements were made 3-5 min after each work rate imposition to avoid incorporation of the VO2 slow component above the lactate threshold. For all 17 subjects, the slope of pulmonary VO2 (9.9 +/- 0.2 ml O2.W-1.min-1) was not different (P greater than 0.05) from that for leg VO2 (9.2 +/- 0.6 ml O2.W-1.min-1). Estimation of "delta" efficiency (i.e., delta work accomplished divided by delta energy expended, calculated from slope of VO2 vs. work rate and a caloric equivalent for O2 of 4.985 cal/ml) using pulmonary VO2 measurements (29.1 +/- 0.6%) was likewise not significantly different (P greater than 0.05) from that made using leg VO2 measurements (33.7 +/- 2.4%). These data suggest that the net VO2 cost of metabolic "support" processes outside the exercising legs changes little over a relatively broad range of exercise intensities. Thus, under the conditions of this investigation, changes of VO2 measured from expired gas reflected closely those occurring within the exercising legs.