A Plyometric Warm-Up Protocol Improves Running Economy in Recreational Endurance Athletes

Abstract

This study explored the impact of two differing warm-up protocols (involving either resistance exercises or plyometric exercises) on running economy (RE) in healthy recreationally active participants. Twelve healthy university students [three males, nine females, age 20 ± 2 years, maximal oxygen uptake (38.4 ± 6.4 ml min–1 kg–1)] who performed less than 5 h per week of endurance exercise volunteered to participant in this study. All participants completed three different warm-up protocols (control, plyometric, and resistance warm-up) in a counterbalanced crossover design with trials separated by 48 h, using a Latin-square arrangement. Dependent variables measured in this study were RE at four running velocities (7, 8, 9, and 10 km h–1), maximal oxygen uptake; heart rate; respiratory exchange rate; expired ventilation; perceived race readiness; rating of perceived exertion, time to exhaustion and leg stiffness. The primary finding of this study was that the plyometric warm-up improved RE compared to the control warm-up (6.2% at 7 km h–1, ES = 0.355, 9.1% at 8 km h–1, ES = 0.513, 4.5% at 9 km h–1, ES = 0.346, and 4.4% at 10 km h–1, ES = 0.463). There was no statistically significant difference in VO2 between control and resistance warm-up conditions at any velocity. There were also no statistically significant differences between conditions in other metabolic and pulmonary gas exchange variables; time to exhaustion; perceived race readiness and maximal oxygen uptake. However, leg stiffness increased by 20% (P = 0.039, ES = 0.90) following the plyometric warm-up and was correlated with the improved RE at a velocity of 8 km h–1 (r = 0.475, P = 0.041). No significant differences in RE were found between the control and resistance warm-up protocols. In comparison with the control warm-up protocol, an acute plyometric warm-up protocol can improve RE in healthy adults.

Full text

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ORIGINAL RESEARCH
published: 12 March 2020
doi: 10.3389/fphys.2020.00197
Edited by:
Francis Degache,
MotionLab, Switzerland
Reviewed by:
Jason Moran,
University of Essex, United Kingdom
Xin Ye,
The University of Mississippi,
United States
*Correspondence:
Liang Yu
yuliang@bsu.edu.cn
Benedict Duncan
benedict.duncan@worc.ac.uk
Specialty section:
This article was submitted to
Exercise Physiology,
a section of the journal
Frontiers in Physiology
Received: 01 November 2019
Accepted: 20 February 2020
Published: 12 March 2020
Citation:
Wei C, Yu L, Duncan B and
Renfree A (2020) A Plyometric
Warm-Up Protocol Improves Running
Economy in Recreational Endurance
Athletes. Front. Physiol. 11:197.
doi: 10.3389/fphys.2020.00197
A Plyometric Warm-Up Protocol
Improves Running Economy in
Recreational Endurance Athletes
ChenGuang Wei1, Liang Yu1*, Benedict Duncan2*and Andrew Renfree2
1School of Sport Science, Beijing Sport University, Beijing, China, 2School of Sport and Exercise Science, University
of Worcester, Worcester, United Kingdom
This study explored the impact of two differing warm-up protocols (involving either
resistance exercises or plyometric exercises) on running economy (RE) in healthy
recreationally active participants. Twelve healthy university students [three males, nine
females, age 20 ±2 years, maximal oxygen uptake (38.4 ±6.4 ml min−1kg−1)] who
performed less than 5 h per week of endurance exercise volunteered to participant in this
study. All participants completed three different warm-up protocols (control, plyometric,
and resistance warm-up) in a counterbalanced crossover design with trials separated
by 48 h, using a Latin-square arrangement. Dependent variables measured in this study
were RE at four running velocities (7, 8, 9, and 10 km h−1), maximal oxygen uptake;
heart rate; respiratory exchange rate; expired ventilation; perceived race readiness;
rating of perceived exertion, time to exhaustion and leg stiffness. The primary finding
of this study was that the plyometric warm-up improved RE compared to the control
warm-up (6.2% at 7 km h−1, ES = 0.355, 9.1% at 8 km h−1, ES = 0.513, 4.5% at
9 km h−1, ES = 0.346, and 4.4% at 10 km h−1, ES = 0.463). There was no statistically
significant difference in VO2between control and resistance warm-up conditions at any
velocity. There were also no statistically significant differences between conditions in
other metabolic and pulmonary gas exchange variables; time to exhaustion; perceived
race readiness and maximal oxygen uptake. However, leg stiffness increased by 20%
(P= 0.039, ES = 0.90) following the plyometric warm-up and was correlated with the
improved RE at a velocity of 8 km h−1(r= 0.475, P= 0.041). No significant differences
in RE were found between the control and resistance warm-up protocols. In comparison
with the control warm-up protocol, an acute plyometric warm-up protocol can improve
RE in healthy adults.
Keywords: plyometric, resistance, warm-up, leg stiffness, post-activation potentiation, running economy
Abbreviations: ANOVA, analysis of variance ES effect size; BMI, body mass index; HR, heart rate; LSD, least-significance
difference; LT, lactate threshold; PAP, post-activation potentiation; RE, running economy; RER, respiratory exchange rate;
RPE, rating of perceive exertion; SD, standard deviation; VE, expired ventilation; VO2, oxygen uptake; VO2max, maximal
oxygen uptake.
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INTRODUCTION
Distance running performance is determined by three major
physiological variables; VO2max ; lactate threshold (LT) and
running economy (RE) (Coyle, 1995). VO2max refers to the
maximal volume of oxygen that the individual can uptake and
utilize per minute, and is one of the key determinants of
superior endurance running performance (Bassett and Howley,
2000). However, it is not possible to predict endurance running
performance using VO2max alone because VO2 max only sets
the upper limits for the endurance performance (Costill et al.,
1973;Nummela et al., 2006) and does not take into account
the extent to which an athlete is able to utilize their maximal
aerobic power. Therefore, despite having similar endurance
performance abilities, runners may display wide variation in
VO2max values, indicating that other factors play a major role
in determining exercise performance. LT is generally defined
as the absolute workload above which blood lactate levels rise
exponentially during incremental exercises (Weltman, 1995).
However, elite endurance runners may have similar values in
the above variables ranging from 68.2 ml min−1kg−1to
84.1 ml min−1kg−1in VO2max , and from 80% VO2max to
85% VO2max in LT, respectively (Sjodin and Svedenhag, 1985;
Bassett and Howley, 2000;Barnes and Kilding, 2015). It is
therefore evident that endurance performance is influenced by
other variables. RE is defined as the energy demand for a given
velocity of submaximal running, and is measured via steady-state
oxygen uptake (Saunders et al., 2006;Barnes and Kilding, 2015).
Previous research (Daniels and Daniels, 1992) indicated that
improvements in RE may result in superior running performance
due to a reduced energetic cost at submaximal intensities, even
amongst athletes with similar VO2max values, suggesting that to
some extent it may be possible to compensate for limitations in
VO2max with superior RE capabilities.
Running economy is complex and multifactorial, and
is related to biomechanical, metabolic, neuromuscular, and
cardiorespiratory factors (Barnes and Kilding, 2015). One of the
primary determinants of RE is leg stiffness (Arampatzis et al.,
2006;Barnes and Kilding, 2015;Barnes et al., 2015). Stiffness can
be defined as the resistance of an object or body to deformation
and is calculated as the ratio of force to length (Blickhan, 1989).
Dalleau et al. (1998) demonstrated that RE is associated with
the stiffness of the propulsive leg, with greater stiffness eliciting
the best RE. Additionally, Arampatzis et al. (2006) corroborated
this finding by separating 28 distance runners into 3 groups
according to RE and found the runners who had the highest leg
stiffness displayed the best RE. The potential mechanism for these
observations may be the redistribution of muscular output within
the lower extremities and increased energy storage while running
(Avela and Komi, 1998).
Active warm-up is one of the most commonly used
warm-up techniques in endurance athletes as it can induce
specific cardiovascular and metabolic changes that are beneficial
to endurance running performance (Bishop, 2003). It is
acknowledged that post-activation potentiation (PAP) can be
induced by the pre-activation of skeletal muscles through heavy
exercises, which is beneficial to performance in weightlifting,
running and sprinting activities (Hodgson and Docherty, 2005).
Barnes et al. (2015) explored the acute influence of a resistance
intervention on RE and running performance in highly trained
endurance runners by incorporating 20% body mass weighted
vest strides as a part of the warm-up protocol. This intervention
was found to enhance RE (6.0 ±1.6%) and running performance,
and regression analysis found that increased leg stiffness (r= 0.88)
was one of the potential mechanisms of improved RE.
In addition to resistance exercises, numerous studies have
also explored the effects of plyometric training on RE and
running performance (Turner et al., 2003;Saunders et al.,
2006;Bílý et al., 2017;Giovanelli et al., 2017;Marcello et al.,
2017). Plyometric training utilizes the stretch-shortening cycle
whereby a stretch of the muscle is immediately followed by
a rapid muscle action (Rimmer and Sleivert, 2000). Such an
action can be induced through a combination of eccentric and
concentric exercises, and can be used to enhance the capability of
muscles to produce power by exaggerating the stretch-shortening
cycle. It includes various exercises such as bounding, jumping,
and hopping (Lundin, 1985;Turner et al., 2003). Previous
research has demonstrated that short-term plyometric training
could enhance RE and running performance in elite endurance
athletes. Blagrove et al. (2019) found that just six repetitions of a
depth jump (a single set of plyometric training) could produce
a moderate improvement (3.7%, effect size: 0.67) in RE in
national standard male endurance runners, a similar magnitude
of enhancement in RE to that achieved with a 6–14 weeks’
plyometric intervention. Barnes and Kilding (2015) hypothesized
that changes in neuromuscular characteristics are associated
with the improved RE following the plyometric intervention.
Similarly, Cornu et al. (1997) and Spurrs et al. (2003) found that
the improved RE is accompanied by an increase in leg stiffness,
which allows muscles to store and utilize elastic energy more
efficiently, resulting in less energy consumption while running.
Essentially, endurance runners would be able to produce greater
propulsion with the same or less energy consumption, which can
improve RE and running performance.
Based on previous studies, it is suggested that the beneficial
influences of resistance training and plyometric training on
RE and running performance may be derived from the PAP
effect and/or increased leg stiffness. However, there no study
has explored the effectiveness of the two warm-up protocols
on RE in healthy adult recreational athletes. Therefore, the
purpose of this study is to determine whether acute resistance
and plyometric warm-up protocols can improve RE in healthy
adults. It was hypothesized that, compared with a control warm-
up, the plyometric and resistance warm-up protocols would
contribute to larger improvements in RE and leg stiffness
without significant changes in other metabolic and pulmonary
gas exchange indicators, RPE, perceived race readiness, time to
exhaustion, or VO2max .
MATERIALS AND METHODS
Subjects
Twelve healthy university students (three males, nine
females, age 20 ±2 years; body mass 58.8 ±8.5 kg; body
height 165.8 ±7.6 cm; BMI 21.3 ±2.1 kg m−2; VO2max
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TABLE 1 | Subjects characteristics (N= 12, 9 females, 3 males).
Characteristics Mean ±SD
Age (year) 20.25 ±2.3
Body mass (kg) 58.8 ±8.5
Body height (cm) 165.8 ±7.6
BMI (kg m−2) 21.3 ±2.1
Body fat percentage (%) 25.5 ±6.4
VO2max (ml min−1kg−1) 38.6 ±6.3
Training time (h week−1)<5
38.6 ±6.3 ml min−1kg−1; body fat percentage 25.5 ±6.4%)
volunteered to participate in this study (Table 1). All participants
were (1) free from any cardiovascular and neurological diseases,
and were not suffering from any musculoskeletal injuries;
(2) were not participating in systematic endurance training, and
had a total exercise load of less than 5 h per week. All participants
provided written informed consent prior to participation in any
of the experimental procedures that had received prior ethical
approval at the University of Worcester.
Procedures
Participants completed three experimental sessions (control,
plyometric, and resistance warm-up) in a counterbalanced
crossover design, using a Latin-square arrangement, with trials
separated by 48 h. An initial visit was used to familiarize
participants with testing equipment and procedures. Participants
were instructed to perform no strenuous exercise within the 48 h
prior to testing in order to avoid fatigue and delayed muscle
soreness. They were also instructed to refrain from caffeine
and alcohol consumption in the 24 h prior to testing, and to
avoid the consumption of food in the 3 h prior to each testing
session. All the tests were conducted in a laboratory with similar
temperature and humidity (21.6 ±1.4◦C, 48 ±5%), and at the
same time of the day for each subject to avoid any influences of
the circadian rhythm.
In the control and resistance conditions, participants
performed a 10-min self-paced jog on a motorized treadmill
followed by six, 10 s strides with or without extra load with a
1-min rest period between each on treadmill. The velocity of
strides during the first condition was controlled by participants,
and the velocity was recorded and repeated in the next condition.
In the resistance condition, subjects performed the strides whilst
wearing a weighted vest equal to 20% of body mass (Barnes
et al., 2015). The warm-up procedure was followed by a 10-min
recovery, then followed by five maximal continuous straight-leg
jumps for the determination of leg stiffness. At the end of the rest
following warm-up, perceived race readiness for each subject was
determined. Participants then performed the running tests on a
motorized treadmill.
During the plyometric intervention, subjects performed 2 ×8
squat jumps, 2 ×8 scissor jumps, and 2 ×8 double leg bounds
(2 sets of 8 repetitions) as a part of warm-up, and had 60 s to
recover between each set. Prior to the intervention, participants
were shown the technique to be used during jumping through use
of three videos. In the squat jumps, participants were required to
start with feet wide and chest up, to squat low so that thighs were
parallel with the ground, then drive their arms up and push off the
floor. In the scissor jumps, participants were requested to stand
with one leg in front and one leg behind, maintain a right angle
between thigh and calf, then drive their arms up and push off the
floor whilst reversing leg positions. In the double leg bounds, the
participants commenced from the same starting position as in
the squat jumps. However, they then jumped forward as far as
possible with the arms up. The total amount of time spent in each
of the three warm-up protocols was recorded. Participants were
instructed to wear the same pair of running shoes during the three
tests. The full experimental protocol is illustrated in Figure 1.
Measurement
Anthropometric Characteristics Measurement
Body height and body mass were measured using a Seca 213
Stadiometer (Seca, United Kingdom) and Sartorius Combics
scales (Bovenden, Germany). A Bodystat (Isle of Man, British
Isles) device was used to measure body fat percentage through
two electrodes positioned on the participants’ right hand
and foot joints.
5-Jump Plyometric Test
Participants conducted five maximal continuous straight-leg
jumps on a force plate (Watertown, MA, United States). They
were requested to keep the legs as straight as possible throughout
jumping and to try to obtain the maximum height on each jump
with the contact time kept as fast as possible. Leg stiffness was
calculated as the relative power (N kg−1) divided by the vertical
displacement (m) measured during the 5-jump plyometric test
(Morin et al., 2005). The leg stiffness was calculated using the
following formula: Kleg =Fmax ·1L−1(Fmax means the maximal
ground reaction force during the contact; 1L refers to the peak
displacement of the leg spring (Morin et al., 2005). The peak value
was selected for the subsequent data analysis.
Perceived Race Readiness
At the end of rest following the three warm-up protocols,
participants were inquired “how effectively do you think the
warm-up was in preparation for racing?” and requested to rate
their readiness from 1 (not effective at all) to 10 (extremely
effective) (Ingham et al., 2013).
Running Test and Incremental Test
Initial velocity was 7 km h−1and increased by 1 km h−1
every 3 min up to 10 km h−1. The gradient of the motorized
treadmill was set at 1% to simulate the air resistance that athletes
experience on an outdoor track (Jones and Doust, 1996). During
the incremental test, at 10 km h−1, the gradient increased by 2.5%
every 2 min until exhaustion. HR and pulmonary gas-exchange
indicators were measured continuously with a Polar H7 heart
rate monitor (Polar, United Kingdom) and Cortex Metalyzer
(Cranlea, United Kingdom). The VO2, HR, VE, and RER were
averaged over the last minute of each running velocity. VO2max
was determined to have been achieved when two of three criteria
were achieved (1) RER >1.1; (2) VO2reached a plateau or
decreased slowly in the final stage of the test; (3) HR attained over
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FIGURE 1 | Experimental protocol.
90% of the age predicted maximum (maximal HR = 220 - age)
(Buckley et al., 2015). RPE was obtained using the Borg Category
(6–20) scale (Borg, 1998) in the final 30 s at each velocity.
DATA ANALYSIS
Normality of data was assessed using the Shapiro–Wilk test prior
to analysis. A two-way repeated measures ANOVA (analysis
of variance) was used to analyze the differences in each
variable within-subject factor: warm-up conditions (control,
plyometric, and resistance warm-up protocols); within-subject
factor: different velocities (7, 8, 9, and 10 km h−1). A two-
way repeated measures ANOVA followed by least significant
difference (LSD) post hoc test and simple effects analysis where
appropriate, were used to analyze the pairwise comparisons.
Perceived race readiness, leg stiffness, and time to exhaustion
within three warm-up interventions were assessed with one-
way ANOVA. The magnitude of differences in key dependent
variables were presented as effect sizes using the following criteria
0.2–0.5 small; 0.5–0.8 moderate; >0.8 large (Cohen, 1988).
A Pearson correlation was used to assess the relationship between
changes in leg stiffness and VO2(RE). Statistical analyses were
performed using SPSS.24. All data is presented as Mean ±SD,
and statistical significance was accepted at P<0.05.
RESULTS
There were interaction effects between the three warm-up
protocols and four running velocities for VO2[F(6,66) = 2.365,
P= 0.040], while there were no interaction effects for VE
[F(2.52,27.70) = 0.257, P= 0.823], HR [F(2.49,27.46) = 0.618,
P= 0.581], RER [F(1.46,16.12) = 2.045, P= 0.169] or
RPE [F(6,66) = 1.548, P= 0.215]. In addition, none of
the three warm-up protocols had any main effects on
VE[F(2,22) = 0.591, P= 0.562], HR [F(2,22) = 1.723,
P= 0.202], RER [F(1.247,13.715) = 0.006, P= 0.966] or
RPE [F(2,22) = 1.069, P= 0.360].
Effect of Running Velocity on VO2
VO2increased with increased velocity (control warm-up
protocol: F= (3,33) = 119.109, P<0.01; Plyometric warm-up
protocol: F= (3,33) = 60.682, P<0.01; Resistance warm-up
protocol: F= (3,33) = 241.410, P<0.01). Values for VO2
following each warm-up protocol and at each running velocity
are presented in Table 2.
Effect of Warm-Up Protocol on VO2
In comparison with the control warm-up protocol, at
7 km h−1, VO2was lower in the plyometric condition
[control: 26.75 ±2.66 ml min−1kg−1; plyometric:
25.08 ±2.39 ml min−1kg−1(F(2,22) = 3.368, P= 0.032,
ES = 0.355)]. There was no significant difference in VO2
between the control and resistance exercise conditions
[26 ±2.69 ml min−1kg−1(F(2,22) = 3.368, P= 0.202,
ES = 0.144)]. Individual values and Mean ±SD for VO2at the
velocity of 7 km h−1within the three protocols are displayed
in Figure 2A.
Similarly, at all velocities, VO2was significantly lower
following the plyometric warm-up protocol compared to
the control condition: 30.08 ±3.42 ml min−1kg−1to
27.33 ±4.30 ml min−1kg−1, [F(2,22) = 8.781, P= 0.006,
ES = 0.513] (8 km h−1), 33.58 ±3.61 ml min−1kg−1to
32.08 ±2.61 ml min−1kg−1, [F(2,22) = 4.287, P= 0.034,
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TABLE 2 | The effects of four running velocities on VO2following the control, plyometric, and resistance warm-up protocols.
Pvalues within
Interventions 7 km h−18 km h−19 km h−110 km h−14 velocities
Control warm-up 26.75 ±2.667 30.08 ±3.423** 33.58 ±3.605** 36.08 ±3.630** <0.01
Plyometric warm-up 25.08 ±2.39 27.33 ±4.29** 32.08 ±2.61** 34.50 ±3.55** <0.01
Resistance warm-up 26.00 ±2.70 30.67 ±3.34** 33.83 ±3.83** 36.42 ±3.58** <0.01
Significant difference (**P <0.01) from 7 km h−1.
FIGURE 2 | Individual values and Mean ±SD for VO2at the velocity of 7 km h-1(A),8kmh-1(B),9kmh-1(C), and 10 km h-1(D) within three warm-up protocols.
Significant difference (∗P<0.05, ∗∗P<0.01) from control warm-up.
ES = 0.346] (9 km h−1) and 36.08 ±3.63 ml min−1kg−1
to 34.50 ±3.55 ml min−1kg−1, [F(2,22) = 4.653, P= 0.010,
ES = 0.463] (10 km h−1), respectively. However, no statistically
significant differences were found in VO2between control
and resistance warm-up protocols (P= 0.570, ES = 0.030) at
10 km h−1. Individual values and Mean ±SD for VO2at the
velocity of 8, 9, and 10 km h−1within three warm-up protocols
are shown in Figures 2B–D.
Effect of Warm-Up Protocol on
Perceived Race Readiness, Leg Stiffness
and Time to Exhaustion
No statistical significant changes were found in perceived
race readiness or time to exhaustion between the
warm-up protocols. Leg stiffness showed significant
increases following the plyometric and resistance warm-
up protocols, increasing from 18.59 ±4.50 kN m−1to
22.38 ±3.91 kN m−1,F(2,33) = 3.754, P= 0.039, ES = 0.541
and 23.08 ±4.51 kN m−1,F(2,33) = 3.754, P= 0.016,
ES = 0.765 (Table 3). Individual values and Mean ±SD for
leg stiffness following the three warm-up protocols are shown
in Figure 3.
TABLE 3 | Influence of 3 protocols of warm-up on leg stiffness, perceived race
readiness, and time to exhaustion.
Plyometric Resistance
Indicators Control warm-up warm-up
Leg stiffness 18.59 ±4.50 22.38 ±3.91* 23.08 ±4.51*
Perceived race 4.67 ±1.37 5.08 ±1.62 5.00 ±2.00
readiness
Time to exhaustion 14.09 ±2.50 14.09 ±2.39 14.43 ±2.60
Significant difference (*P <0.05) from control warm-up.
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FIGURE 3 | Individual values and Mean ±SD for leg stiffness within three
warm-up protocols. Significant difference (∗P<0.05) from control warm-up.
Correlations Between Changes in VO2
and Changes in Leg Stiffness at Each
Running Velocity Following the
Plyometric Warm-Up Protocol
No statistically significant correlations were found between
changes in VO2and changes in leg stiffness at velocities of
7 km h−1(r= 0.058, P= 0.185), 9 km h−1(r= 0.226, P= 0.057), or
10 km h−1(r= 0.050, P= 0.187) following the plyometric warm-
up protocol. However, increased leg stiffness was moderately
correlated with improved RE at 8 km h−1(r= 0.475, P= 0.041).
In addition, no statistically significant correlations were found
between changes in VO2and changes in leg stiffness at velocities
of 7 km h−1(r= -0.154, P= 0.063), 8 km h−1(r= -0.226,
P= 0.057), 9 km h−1(r= -0.050, P= 0.187), or 10 km h−1
(r= -0.080, P= 0.198) following the resistance warm-up protocol.
DISCUSSION
This study investigated the influences of plyometric and
resistance warm-up protocols on RE in healthy adults. The
primary finding of this study was that a plyometric warm-
up can improve RE (6.2% at the velocity of 7 km h−1,
ES = 0.355, 9.1% at the velocity of 8 km h−1, ES = 0.513,
4.5% at the velocity of 9 km h−1, ES = 0.346, and
4.4% at the velocity of 10 km h−1, ES = 0.463) with
no statistically significant changes in other metabolic and
pulmonary gas exchange indicators, RPE, time to exhaustion,
perceived race readiness or VO2max in comparison to a
control protocol. However, no significant differences were
found in RE at any velocity between the control and
resistance protocols even though the leg stiffness showed a
significant increase (24%, ES = 0.765) following the resistance
warm-up intervention.
Numerous studies have explored the influence of chronic
(ranging from 4 to 14 weeks) plyometric and/or resistance
training interventions on RE and running performance
(Spurrs et al., 2003;Guglielmo et al., 2009;Berryman et al.,
2010). Balsalobre-Fernández et al. (2016) suggested it is
optimal and practical for highly trained endurance athletes
to perform 8–12 weeks’ low to high intensity resistance and
plyometric training, with a frequency of 2–3 sessions per
week, for the purpose of enhancing RE. In contrast, few
studies have explored the acute effects of plyometric and
resistance warm-up protocols on RE in healthy adults and
endurance athletes. Even though an enhancement in RE
was found in the present study following the plyometric
warm-up, it is unclear whether the improvement can
be translated to competitive endurance athletes using
the same protocol.
In comparison with the control warm-up, leg stiffness
increased by 20% (P= 0.039, ES = 0.541) and 24% (P= 0.016,
ES = 0.765) following plyometric and resistance warm-up
protocols, respectively. Significant correlations between changes
in RE and changes in leg stiffness following the plyometric warm-
up intervention were found at 8 km h−1only. Previous research
has found improved RE and increased leg stiffness following
plyometric and resistance interventions (Millet et al., 2002;
Spurrs et al., 2003;Barnes et al., 2015;Moore, 2016). However,
in the present study, only two participants demonstrated
improvements in RE following the resistance warm-up protocol.
One possibility is that the exercise intensities utilized in the
present study were inappropriate for the purpose of inducing
any PAP effect. Resistance exercise can induce PAP, which
may have beneficial effects on RE and running performance
by increasing the phosphorylation of myosin regulatory light
chains and Ca2+sensitivity in striated muscles (MacIntosh,
2010;Boullosa et al., 2018). However, fatigue and PAP effect
can exist in the body at the same time (Rassier and Macintosh,
2000), and is dependent not only ntraining protocols, but
also the individual’s fitness level (Hamada et al., 2000). In
the present study, a 20% body mass weighted vest was used,
because this loading had previously been demonstrated to
increase leg stiffness and improve RE in more well trained
runners (Barnes et al., 2015). Given the difference in training
status of the experimental participants, a possible reason why
the resistance warm-up produced no change in RE in our
study may be that the 20% body mass weighted vest was
inappropriate for inducing a PAP effect in this population. If
the load was too heavy, then the negative effects of fatigue
on RE may have counteracted any beneficial influences of
a PAP effect on RE and running performance. Alternatively,
it may simply be the case that the protocol utilized was
insufficient to produce any PAP effect in the first place.
Given the data available to us, we are unable to determine
if the absence of benefit of the resistance protocol resulted
from inability to induce PAP, or resultant fatigue. However,
it is noteworthy that excessive fatigue was not apparent
during testing itself and it that there were no differences in
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Wei et al. Plyometric Warm-Up and Running Economy
perceived race readiness between conditions, which may be
expected to be the case if the vest was indeed too heavy.
Additionally, it may also suggest that leg stiffness is not the only
factor of influencing RE.
Running economy was improved at all running velocities
following the plyometric warm-up protocol. However, increased
leg stiffness was only moderately (r= 0.475, P= 0.041) correlated
with improved RE at 8 km h−1, and not at other velocities.
This may suggest that improved leg stiffness was not the only
factor responsible for the enhanced RE following the plyometric
warm-up. PAP effects have previously been demonstrated to be
induced through the pre-activation of skeletal muscles through
heavy exercises (Hodgson and Docherty, 2005), and Blagrove
et al. (2019) found that just six repetitions of a depth jump
produced moderate improvements in RE in national standard
male endurance runners through inducing a PAP effect. PAP
effects can increase phosphorylation of myosin regulation light
chains and Ca2+sensitivity in striated muscle (Boullosa et al.,
2018), leading to an increased rate of force development, and
peak tension through an increased number of cross-bridges
formed (MacIntosh, 2010). These neuromuscular adaptations
may allow runners to maintain a constant running velocity with a
relative low energy cost (Blagrove et al., 2018). In addition, any
PAP effects induced by a plyometric warm-up may potentiate
the recruitment of type I muscle fibers, thereby postponing the
activation of less efficient type II muscle fibers and reducing
energy consumption during running (Schumann et al., 2016).
Moreover, elastic energy induced by a plyometric warm-up can be
stored in the tendons and skeletal muscles, making an extensive
contribution to propulsion (Anderson, 1996). This may reduce
ground contact times, and is likely to further reduce energy
consumption (improve the RE) during endurance exercise. The
above mechanisms may explain the improved RE following
the plyometric warm-up protocol. However, we acknowledge
that in the present study, PAP effects were not specifically
measured, meaning our ability to fully explain the underpinning
mechanisms responsible for the observed effects on RE and
performance is limited.
Limitations
Limitations to the current study include (1) Total time for the
three warm-up interventions were slightly different, being 16 min
for the control condition, 16 min for the resistance condition
and an average of 16.34 min (ranging from 15.22 to 17.3 min)
for plyometric condition. (2) As described above, the possible
reason of resistance warm-up inducing no change in RE in this
present study may be the unsuitable intensity, leading to fatigue
in participants. However, no measure of fatigue was made during
the study. (3) Even though enhancement in RE was found in
present study following the plyometric warm-up, it is unclear
whether the improvement can be translated to the competitive
endurance athletes using the same plyometric warm-up protocol.
Therefore, the present study suggests that more attention should
be paid to explore the optimal intensity of resistance training
using weighed vest by monitoring some fatigue and PAP effect
related indicators, and also explore the beneficial effects of acute
plyometric warm-up on RE and running performance in elite
endurance athletes.
CONCLUSION
In conclusion, the primary finding of this study was plyometric
warm-up can improve RE (6.2% at 7 km h−1, ES = 0.355, 9.1%
at 8 km h−1, ES = 0.513, 4.5% at 9 km h−1, ES = 0.346 and
4.4% at 10 km h−1, ES = 0.463) (Supplementary Datasheet 1).
However, no statistical significant changes in other metabolic
and pulmonary gas exchange indicators, time to exhaustion,
perceived race readiness, and VO2max were found in comparison
with the control and resistance warm-up protocols. In addition
to this, increased leg stiffness following the plyometric warm-
up protocol was related to the improved RE at the velocity
of 8 km h−1(r= 0.475, P= 0.041) in healthy adults. Future
studies should endeavor to elucidate the effect of plyometric
warm-up protocols on RE and running performance in elite
endurance runners.
DATA AVAILABILITY STATEMENT
All datasets generated for this study are available on request to the
corresponding author.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by the Ethical Committee of the University of
Worcester, United Kingdom. All participants provided informed
consent prior to participation in experimental procedures.
AUTHOR CONTRIBUTIONS
CW, BD, LY, and AR were involved in study design and data
interpretation. CW collected and analyzed the data. All authors
approved the final version of manuscript.
FUNDING
This study was supported by a grant from the International
Cooperation Research Project of Beijing Sport University
(No. 2018GJ012).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fphys.
2020.00197/full#supplementary-material
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fphys-11-00197 March 10, 2020 Time: 20:22 # 8
Wei et al. Plyometric Warm-Up and Running Economy
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Conflict of Interest: The authors declare that the research was conducted in the
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