Comparison of Energy Contributions and Workloads in Male and Female Badminton Players During Games Versus Repetitive Practices

fphys-12-640199 June 24, 2021 Time: 16:11 # 1

ORIGINAL RESEARCH

published: 24 June 2021

doi: 10.3389/fphys.2021.640199

Edited by:

Nicolas Babault,

Université Bourgogne

Franche-Comté, France

Reviewed by:

Cristina Cortis,

University of Cassino, Italy

Daniel Rojas-Valverde,

National University of Costa Rica,

Costa Rica

*Correspondence:

Xiaoping Chen

[email protected]

Specialty section:

This article was submitted to

Exercise Physiology,

a section of the journal

Frontiers in Physiology

Received: 16 December 2020

Accepted: 20 April 2021

Published: 24 June 2021

Citation:

Fu Y, Liu Y, Chen X, Li Y, Li B,

Wang X, Shu Y and Shang L (2021)

Comparison of Energy Contributions

and Workloads in Male and Female

Badminton Players During Games

Versus Repetitive Practices.

Front. Physiol. 12:640199.

doi: 10.3389/fphys.2021.640199

Comparison of Energy Contributions

and Workloads in Male and Female

Badminton Players During Games

Versus Repetitive Practices

Yue Fu

, Yu Liu

, Xiaoping Chen

1,3

, Yongming Li

, Bo Li

, Xinxin Wang

, Yang Shu

and

Lei Shang

School of Kinesiology, Shanghai University of Sport, Shanghai, China,

Key Laboratory of Exercise and Health Sciences of

Ministry of Education, Shanghai University of Sport, Shanghai, China,

China Institute of Sport Science, Beijing, China,

School of Physical Education and Sport Training, Shanghai University of Sport, Shanghai, China,

School of Competitive

Sport, Beijing Sport University, Beijing, China

Purpose: The aim of this study was to compare the energy contributions and workloads

in men and women during badminton matches versus frequently used multi-ball

smash practices.

Methods: Fourteen badminton players performed one badminton singles game and

one session of smashing practice on separate days. The energy contributions were

examined in terms of each individual’s three energy systems and substrate oxidation,

while workloads included heart rate (HR), Player Load (PL), accelerations, decelerations,

changes of direction, and jumps.

Results: (1) During games, male players exhibited higher adenosine triphosphate–

phosphocreatine system contribution (E

PCr

, kJ) (p = 0.008) and average rate of

carbohydrate oxidation (R

CHO

, g/min) (p = 0.044) than female players, while female

players showed greater absolute PL (p = 0.029) and more accelerations (p = 0.005) than

male players. Furthermore, players who lost performed higher relative PL (p = 0.017)

than those who won. (2) Higher energy system contributions, including E

PCr

(kJ)

(p = 0.028), E

HLa

(kJ) (p = 0.024), E

Aer

(kJ) (p = 0.012), E

Tot

(kJ) (p = 0.007), and R

CHO

(g/min) (p = 0.0002), were seen in male players during repetitive spike practices. Male

players also made greater number of jumps (p = 0.0002). (3) Players exhibited higher

aerobic energy contribution (p < 0.001), mean HR (p = 0.002), and HRmax (p = 0.029)

during games, while exhibiting greater anaerobic energy contribution (p < 0.001) and

relative PL (p = 0.001) during repetitive practices.

Conclusion: The similarities between male and female badminton players in

proportional use of the three energy systems during games and repetitive spike training

indicate similar relative energy demands for both genders. However, considering the

need for higher aerobic capacity in competition, it might be advisable to design

appropriate work:rest ratios for repetitive practices in daily training.

Keywords: athlete monitoring, match loads, smash training, energy supply, triaxial accelerometer

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Fu et al. Energy Contributions and Workloads

INTRODUCTION

Badminton is a physically demanding racquet sport that

involves frequent bouts of high-intensity activity, and a

complex skill concerning repeated acceleration, deceleration,

changes of direction (CoD), and jumps (Jum) (Cabello and

Gonzalez, 2003; Abdullahi et al., 2019). Well-trained badminton

players are able to stroke using a diverse set of sport-speciﬁc

techniques at varying frequencies throughout a match. To

improve various kinds of stroke techniques, high-repetition

practices are used extensively during daily training. However,

the workload of high-repetition stroke techniques has received

limited attention. A deeper understanding of the high-repetition

techniques may help to develop sport-speciﬁc training programs

that could enhance performance in competitive badminton.

The contribution of each energy system in matches and

for speciﬁc badminton skills is unclear. Previous studies

exploring the energetic proﬁle of badminton players in games

showed a 60–70% aerobic-dominant proﬁle (Chin et al., 1995;

Faccini and Dai Monte, 1996; Deka et al., 2017), and some

scientists found the adenosine triphosphate–phosphocreatine

(ATP-PCr) system (E

PCr

) and the glycolysis system (E

HLa

)

to be the main suppliers of energy (Li and Ling, 1997).

However, energy contributions may be inﬂuenced by diﬀerent

physical loads, such as diﬀerent strokes, foot movements

and the frequencies with which these actions take place.

Therefore, a better understanding of the badminton player’s

energy contribution can only be established by investigating

the energetic proﬁle for each combination of these and

similar actions.

Quantifying the physiological and physical loads imposed

by competitions and training drills is vital to understanding

the dose–response nature of the exercise process with regard

to optimizing players’ performances. Athletic ability, gender,

and posture are related to badminton injuries by understanding

loading characteristics. For example, unskilled female players

have been shown to be more vulnerable to lower extremity

injuries (Lam et al., 2018), and postures have been associated with

knee injuries during badminton games (Sasaki et al., 2018).

Workloads have been extensively investigated in diﬀerent

sports (Garcia et al., 2019, 2020; McFadden et al., 2020).

Workloads include heart rate (HR), rating of perceived exertion

(RPE), Player Load (PL), accelerations (Acc), decelerations (Dec),

CoD, Jum, and so on. Although studies that quantify these

loads are mostly limited to match performance or selected

training periods (Bartlett et al., 2017; Simpson et al., 2020;

Taylor et al., 2020), the loads required in various sport-

speciﬁc practices are equally important. Liu found that the

player’s lower back is an ideal location for a wearable sensor

capable of monitoring overall badminton external loads (Liu

et al., 2021). Trivial to moderate relationships have been found

between internal and external match loads in male, singles

badminton players (Abdullahi et al., 2019). However, additional

research comparing energy contributions and workloads in

male and female badminton players is warranted in order to

determine potential gender diﬀerences in practice strategies

and recovery needs. At present, no study has compared the

energy contributions and workloads in men and women during

badminton matches and repetitive training.

Therefore, the main purpose of this study was to compare

the energy contributions and workloads in male and female

badminton players. The further aim was to describe diﬀerences of

energy contributions and workloads between badminton matches

and intermittent stroke practices with a 1:2 work:rest ratio.

MATERIALS AND METHODS

Participants

Fourteen healthy sub-elite badminton players who competed at

the national level in their age group volunteered to participate

in this study. The players stopped training 24 h before testing.

They were instructed to maintain a regular diet and not to

perform additional vigorous exercise during the experiment.

On the day of testing, participants ﬁnished breakfast at least

1 h before reporting to the training center. All participants

were medically screened to ensure no contraindications to study

participation. Anthropometric and performance characteristics

of these participants are presented in Table 1. Prior to the study,

the players, their coaches, and guardians were informed of the

test procedures and potential risks. After having the beneﬁts and

risks explained to them, the players and their guardians provided

informed written consent. Ethical approval (approval number:

20200901) was obtained from the research ethics committee of

the China Institute of Sport Science, Beijing, China.

Design and Procedures

The study design was cross-sectional. All participants performed

one badminton singles game and one session of repetitive spike

practice on separate days (both tests were conducted indoors at

similar times of day: players who performed in the morning or

afternoon also practiced in the morning or afternoon). Energy

contributions and workloads were monitored by simultaneous

gas exchange measurements, HR technology and accelerometer

technology during games and practices. Before the formal test,

the players performed 15 min sparring practice and dynamic

stretching to warm up. After sitting still for 10 min, they

put on the portable spirometry system (K4b

, Cosmed, Rome,

Italy), HR monitor (Polar Accurex Plus, Polar Electro Inc.,

Kempele, Finland), and Catapult OptimEye S5 (Catapult Sports,

TABLE 1 | Participants’ characteristics.

Age (years) Height (cm) Body mass (kg) Training

experience

(years)

Males

(N = 8)

18.25 ± 3.41 181.88 ± 9.26 70.99 ± 17.80 10.88 ± 3.00

Females

(N = 6)

16.50 ± 2.51 168.67 ± 3.88 54.95 ± 5.86 9.17 ± 2.56

Total

(N = 14)

17.50 ± 3.08 176.21 ± 9.90 64.12 ± 15.87 10.14 ± 2.85

Data are presented as mean ± SD.

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Fu et al. Energy Contributions and Workloads

FIGURE 1 | Test ﬂow chart of a badminton match.

FIGURE 2 | Test ﬂow chart of multi-ball stroke practice for each player.

Melbourne, VIC, Australia). Standard calibration was performed

with a 3-L syringe and a standard gas with a known composition

: 15.00%, CO

: 5.09%), which was corrected for barometric

pressure and humidity prior to the test. Players wore the

abovementioned devices during the games and practice sessions.

Prior to the warmup, immediately before the ﬁrst set, between

each set and during the recovery period following the games

or practice, 10 µl of capillary blood was collected from the ear

lobe to determine the blood lactate concentration (Biosen C

line, EKF Diagnostic, Magdeburg, Germany). The accumulated

blood lactate values were used to calculate the energy from the

anaerobic lactic pathway.

Badminton Match Play

Prior to the formal test, the players were divided into seven pairs

for the singles games; pairing was organized to ensure that players

were of a similar skill level. They were also instructed to dress and

eat as they usually would for a match. The games would follow

the rules of the International Badminton Federation: three games

played to 21 points; if both players score 20 points, a player must

lead his or her opponent by 2 points to win. During the game,

when the leading player scored 11 points, both players took a 1-

min rest. Between the rounds, players had a 2-min break. The

competitions were judged by a national referee. In order to make

the test resemble an actual game as much as possible, the winners

and losers were given diﬀerent rewards as incentives. The speciﬁc

test process is shown in Figure 1.

Repetitive Stroke Practice

Fourteen players performed six sets of spike practice 10 times. An

experienced coach was responsible for continuous serves from

the other half of the court to ensure that the participants could

perform the overhead stroke smoothly. After each set, the players

took a break (sitting still) for twice the exercise time. The speciﬁc

test process is shown in Figure 2.

Energy Contributions Monitoring

The energy contributions monitored by portable spirometry

included the ATP-PCr system/anaerobic alactic contribution

PCr

), the glycolytic system/anaerobic lactic energy contribution

HLa

), the oxidative system/aerobic energy contribution (E

Aer

the total energy contribution (E

Tot

), the average rate of

carbohydrate oxidation (R

CHO

), and the average rate of lipid

oxidation (R

Lip

). Calculations of the average energy contributions

and energy costs were made using gas exchange data that were

recorded during the test and rest periods.

Calculation of the energy system’s contributions

To estimate the energy expenditure of all tests, the sum of the

contributions of the three energy systems was determined in

accordance with the methodology used by other studies in sports

(di Prampero, 1981; Davis et al., 2014; Julio et al., 2017; Li et al.,

2018, 2020):

(1) The ATP-PCr system contribution was shown as E

PCr

and

estimated using the ﬁrst 3-min fast phase of the

after

exercise (games or practices), with a caloric equivalent of

0.021131 kJ/ml at respiratory exchange ratio >1.0. The ﬁrst

3-min slow phase of the

was determined by using an

approximated exponential equation estimated from a non-

linear ﬁtting procedure. The equation was derived from the

actual

of the second 3 min after exercise.

Fast phase of the

(ml)

= actual

(ml) − slow phase of the

(ml)

PCr

(kJ) = fast phase of the

(ml) × 0.021131(kJ/ml)

(2) The glycolytic system contribution, shown as anaerobic

lactic energy contribution (E

HLa

), was calculated from the

accumulated blood lactate during the test (maximal value

subtracted resting value) with the O

-lactate equivalent of

3.0 ml/mM/kg (assuming that the accumulation of 1 mM

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Fu et al. Energy Contributions and Workloads

in lactate was equivalent to 3 ml O

per kilogram of body

mass). Resting blood lactate was the value before warmup

and maximal blood lactate was the largest of all values.

Accumulated blood lactate (mM) =

maximal blood lactate (mM) − resting blood lactate (mM)

HLa

(kJ) = accumulated blood lactate (mM)

× 3.0(ml/mM/kg) × body mass (kg) × 0.021131(kJ/ml)

(3) The oxidative system contribution, shown as aerobic

energy contribution (E

Aer

), was calculated from the

accumulated

during test above resting levels, with

a caloric equivalent of 0.021131 kJ/ml at respiratory

exchange ratio >1.0. Rest levels were deﬁned as

4.0 ml/min/kg for males and 3.5 ml/min/kg for females

in a standing posture. Total

during exercises (games

or practices) was calculated from the portable spirometry

system and expressed in milliliters.

Resting

for males (ml)

= 4.0 (ml/min/kg) × bodymass (kg) × duration (min)

Resting

for females (ml)

= 3.5(ml/min/kg) × bodymass(kg) × duration(min)

Accumulated

(ml) = total

(ml) − resting

(ml)

(kJ) = accumulated

(ml) × 0.021131(kJ/ml)

(4) The total energy expenditure (E

) was computed as the

sum of E

PCr

, E

HLa

, and E

Aer

. In addition, the contributions

of the three energy systems were expressed as a percenage

of the total energy expenditure.

Tot

(kJ) = E

PCr

(kJ) + E

HLa

(kJ) + E

Aer

(kJ)

PCr

(%) = E

PCr

(kJ) ÷ E

Tot

(kJ) × 100%

HLa

(%) = E

HLa

(kJ) ÷ E

Tot

(kJ) × 100%

Aer

(%) = E

Aer

(kJ) ÷ E

Tot

(kJ) × 100%

Calculation of substrate oxidation

Substrate oxidation was estimated for the interval session,

including work and rest periods. Carbohydrate and lipid

oxidation rates were calculated by the non-protein respiratory

quotient (Peronnet and Massicotte, 1991; Pettersson et al., 2019),

Oxygen consumption (

) and carbon dioxide production

(

VCO

) were expressed in liters per minute (L/min) and

oxidation rate in grams per minute (g/min):

Carbohydrate oxidation rate

= (4.585 ×

VCO

) − (3.226 ×

)

Lipid oxidation rate

= (1.695 ×

) − (1.701 ×

VCO

)

Workloads Monitoring

The workload variables used in this study were HR, absolute

PL, relative PL, Acc, Dec, CoD (left and right), and Jum. A 10-

Hz GPS device ﬁtted with a 100-Hz triaxial accelerometer,

gyroscope, and magnetometer (OptimEye S5, Catapult Sports,

Melbourne, VIC, Australia) was securely positioned between

the participant’s scapulae using a custom-made vest. The device

ﬁrmware version was 7.40. The data were processed by the

manufacturer’s software (OpenField, v1.21.1, Catapult Sports,

Melbourne, VIC, Australia). The numbers of Acc, Dec, CoD,

and Jum and CoD, including both left turns (CoD left) and

right turns (CoD right), were measured by the device’s inertial

sensors, throughout the test. (Data between sets were excluded).

Absolute PL was deﬁned as the sum of the acceleration vectors

as assessed through the accelerometer (Catapult OptimEye S5)

in three axes (lateral, vertical, and anterior/posterior). Relative

PL was determined as PL per minute in each period. Both

absolute PL and relative PL were measured in arbitrary units

(au). The PL variable demonstrated strong validity and reliability

indices to assess the neuromuscular load of each referee, and

the corresponding value was calculated through the following

equation:

Player Load

t=n

t=0

(

t=n

− X

t=n−1

)

(

t=n

− Y

t=n−1

)

(

t=n

− Z

t=n−1

)

100

where X refers to acceleration in the medial–lateral direction, Y

refers to vertical acceleration, and Z represents acceleration from

the anterior-to-posterior direction. Time is represented by t and

n refers to number.

Statistical Analyses

Statistical analyses were conducted using the IBM SPSS

statistical software (version 25.0, IBM Corporation, Armonk,

NY, United States). Results were expressed as means ± standard

deviations (SD). The Shapiro–Wilk test was used to assess

normality. Comparisons between male and female players as well

as players who won their matches compared with the losing

players were carried out using independent-sample t-tests if

data satisﬁed normal distribution. Otherwise, non-parametric

test (Mann–Whitney U test) was used to compare groups.

Signiﬁcance level was set at p < 0.05.

RESULTS

Results of Energy Contributions and

Workloads During Game

The 14 players played a total of seven games. Six matches reached

two rounds; only one game (female) had three rounds. The

average match duration was 24.93 ± 6.33 min. Statistics for the

energy contributions and workloads are presented in Table 2.

According to non-parametric distributions, only the comparisons

of absolute PL (AU) and decelerations (n) between men and

women and the comparisons of E

PCr

(%), relative PL (AU), and

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Fu et al. Energy Contributions and Workloads

TABLE 2 | Descriptive results of energy contributions and workloads during a badminton match.

Males Females Victory Defeat Total

Energy contributions E

PCr

(kJ) 45.04 ± 10.04 28.55 ± 5.14* 44.82 ± 13.43 33.42 ± 8.19 38.17 ± 11.69

HLa

(kJ) 13.62 ± 11.04 7.30 ± 3.19 12.96 ± 10.84 9.67 ± 8.11 11.19 ± 9.20

Aer

(kJ) 832.07 ± 175.63 826.76 ± 226.96 817.37 ± 213.89 840.88 ± 178.66 830.03 ± 187.54

Tot

(kJ) 920.82 ± 164.26 862.62 ± 224.83 914.19 ± 214.84 883.98 ± 176.39 896.57 ± 184.38

PCr

(%) 5.01 ± 1.47 3.51 ± 1.09 5.05 ± 1.83 3.90 ± 1.09 4.38 ± 1.48

HLa

(%) 1.74 ± 1.61 0.87 ± 0.42 1.74 ± 1.74 1.11 ± 0.93 1.38 ± 1.29

Aer

(%) 93.25 ± 2.98 95.62 ± 1.38 93.20 ± 3.52 94.98 ± 1.75 94.24 ± 2.65

CHO

(g/min) 1.56 ± 0.69 0.96 ± 0.12* 1.52 ± 0.81 1.17 ± 0.38 1.33 ± 0.62

Lip

(g/min) 0.67 ± 0.16 0.52 ± 0.08 0.64 ± 0.19 0.59 ± 0.12 0.61 ± 0.15

Workloads Mean HR (bpm) 162.38 ± 18.35 171.17 ± 8.93 168.57 ± 19.42 163.71 ± 10.63 166.14 ± 15.25

HRmax (bpm) 194.50 ± 15.00 198.50 ± 4.76 194.29 ± 13.61 198.14 ± 9.84 196.21 ± 11.58

Absolute PL (AU) 111.85 ± 19.77 147.82 ± 31.24* 118.76 ± 24.49 135.77 ± 35.21 127.27 ± 30.44

Relative PL (AU) 5.02 ± 0.26 4.92 ± 0.86 4.66 ± 0.39 5.29 ± 0.57

4.98 ± 0.57

Accelerations (n) 41.75 ± 14.92 72.50 ± 18.63* 50.00 ± 17.23 59.86 ± 27.12 54.93 ± 22.42

Decelerations (n) 46.38 ± 29.91 68.17 ± 29.86 55.71 ± 35.68 55.71 ± 28.02 55.71 ± 30.82

CoD left (n) 144.13 ± 40.76 165.67 ± 75.82 143.00 ± 62.66 163.71 ± 53.08 153.36 ± 56.81

CoD right (n) 82.25 ± 35.25 70.00 ± 11.54 83.86 ± 37.50 70.14 ± 11.39 77.00 ± 27.56

Jumps (n) 27.63 ± 16.17 31.50 ± 19.58 30.71 ± 20.69 27.86 ± 14.16 29.29 ± 17.09

Values are expressed as means ± SD. E

PCr

, ATP-PCr system contribution; E

HLa

, glycolytic system contribution; E

Aer

, aerobic energy contribution; E

Tot

, total energy

contribution; R

CHO

, average rate of carbohydrate oxidation; R

Lip

, average rate of lipid oxidation; HR, heart rate; Max, maximum; PL, Player Load; CoD, changes of direction.

*Signiﬁcantly different from males (p < 0.05).

Signiﬁcantly different from victory (p < 0.05).

jumps (n) between victory and defeat used an on-parametric

test (Mann–Whitney U test); other variables used independent-

sample t-tests. There were statistically signiﬁcant diﬀerences

in E

PCr

(kJ), R

CHO

(g/min), absolute PL, and accelerations (n)

between male and female players (p < 0.05). Male players

exhibited higher anaerobic alactic capacity (p = 0.008) and

average rate of carbohydrate oxidation (p = 0.044) than female

players. The female players showed greater workloads in absolute

PL (p = 0.029) and number of accelerations (p = 0.005)

compared with their male counterparts. No other signiﬁcant

gender diﬀerences were seen during a match. Furthermore,

defeated players exhibited a higher relative PL than winners

(p = 0.017).

Results of Energy Contributions and

Workloads During Repetitive Stroke

Practice

The average spike training session was 11.4 ± 0.45 min. No

signiﬁcant diﬀerence in duration was seen between male and

female players. Descriptive statistics of energy contributions

and training load-related results are presented in Table 3.

Only comparisons of E

PCr

(%), accelerations (n), decelerations

(n), CoD Left (n), and CoD Right (n) between men and

women used an on-parametric test (Mann–Whitney U test),

because these data exhibited non-parametric distributions, and

other variables used independent-sample t-tests. Higher energy

system contributions, including E

PCr

(kJ) (p = 0.028), E

HLa

(kJ)

(p = 0.024), E

Aer

(kJ) (p = 0.012), E

Tot

(kJ) (p = 0.007), and

CHO

(g/min) (p = 0.0002), were seen in male players; there

was no diﬀerence in the percentages of the three energy systems.

Male players accumulated a signiﬁcantly greater number of jumps

(p = 0.0002) during multi-ball spike practices, but no diﬀerences

in other training load variables were seen between genders.

Differences in Energy Contributions and

Workloads Between Game and

Repetitive Stroke Practice

Comparisons of energy contributions and workloads in single-

player games and multi-ball spike training are presented in

Table 4. Only some indicators (those less inﬂuenced by duration)

were selected. Since data exhibited non-parametric distributions,

comparisons of E

HLa

(%), E

Aer

(%), and R

CHO

(g/min) between

games and stroke practices used an on-parametric test (Mann–

Whitney U test); other variables used independent-sample t-tests.

Among these indicators, players exhibited higher E

PCr

(%)

(p = 0.00003) and E

HLa

(%) (p < 0.001) during repetitive spike

training, and higher aerobic energy contribution (p < 0.001)

during games. Higher mean HR (p = 0.002) and max HR

(p = 0.029) were found during games, but greater relative PL

(p = 0.001) was seen in multi-ball spike training.

DISCUSSION

Badminton matches last around 28–78 min (10–21 min/round)

and are fast paced with intermittent moments. The duration of

a single bout is about 6–12 s, and the number of shots in a

bout is around 5–12 strokes (Faude et al., 2007; Abián-Vicén

et al., 2013; Abian et al., 2014; Gawin et al., 2015; Laﬀaye et al.,

2015; Kah Loon and Krasilshchikov, 2016; Savarirajan, 2016).

These bouts involve multiple accelerations, decelerations, CoD,

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Fu et al. Energy Contributions and Workloads

TABLE 3 | Descriptive results of energy contributions and workloads during

stroke practices.

Males Females Total

Energy

contributions

PCr

(kJ) 43.37 ± 5.53 32.07 ± 11.17* 38.53 ± 9.91

HLa

(kJ) 48.32 ± 24.81 19.86 ± 12.09* 36.12 ± 24.52

Aer

(kJ) 439.16 ± 88.83 325.46 ± 32.61* 390.43 ± 89.82

Tot

(kJ) 530.85 ± 108.10 377.39 ± 45.99* 465.09 ± 115.40

PCr

(%) 8.55 ± 2.61 8.34 ± 2.31 8.46 ± 2.39

HLa

(%) 8.71 ± 3.15 5.18 ± 3.04 7.20 ± 3.49

Aer

(%) 82.74 ± 3.31 86.48 ± 3.38 84.34 ± 3.74

CHO

(g/min) 2.44 ± 0.58 1.10 ± 0.26* 1.86 ± 0.82

Lip

(g/min) 0.56 ± 0.10 0.51 ± 0.11 0.54 ± 0.10

Workloads Mean HR

(bpm)

146.57 ± 10.42 149.73 ± 14.44 147.92 ± 11.89

HRmax

(bpm)

189.38 ± 8.00 185.50 ± 6.53 187.71 ± 7.41

Absolute PL

(AU)

77.78 ± 6.78 68.71 ± 14.10 73.89 ± 11.09

Relative PL

(AU)

6.30 ± 0.63 5.60 ± 0.91 6.00 ± 0.81

Accelerations

(n)

3.38 ± 2.62 7.83 ± 10.01 5.29 ± 6.89

Decelerations

(n)

4.38 ± 4.03 3.17 ± 3.82 3.86 ± 3.84

CoD left (n) 37.88 ± 11.78 31.50 ± 12.55 35.14 ± 12.08

CoD right (n) 16.50 ± 17.91 4.83 ± 3.54 11.50 ± 14.61

Jumps (n) 61.25 ± 21.71 9.17 ± 10.61* 38.93 ± 31.82

Values are expressed as means ± SD. E

PCr

, ATP-PCr system contribution;

HLa

, glycolytic system contribution; E

Aer

, aerobic energy contribution; E

Tot

, total

energy contribution; R

CHO

, average rate of carbohydrate oxidation; R

Lip

, average

rate of lipid oxidation; HR, heart rate; Max, maximum; PL, Player Load; CoD,

changes of direction.

*Signiﬁcantly different from males (p < 0.05).

TABLE 4 | Differences of energy contributions and workloads between

games and practices.

Game Practice

Energy contributions E

PCr

(%) 4.38 ± 1.48 8.46 ± 2.39*

HLa

(%) 1.38 ± 1.29 7.20 ± 3.49*

Aer

(%) 94.24 ± 2.65 84.34 ± 3.74*

CHO

(g/min) 1.33 ± 0.62 1.86 ± 0.82

Lip

(g/min) 0.61 ± 0.15 0.54 ± 0.1

Workloads Mean HR (bpm) 166.14 ± 15.25 147.92 ± 11.89*

HRmax (bpm) 196.21 ± 11.58 187.71 ± 7.41*

Relative Player

Load (AU)

4.98 ± 0.57 6.00 ± 0.81*

Values are expressed as means ± SD. E

PCr

, ATP-PCr system contribution;

HLa

, glycolytic system contribution; E

Aer

, aerobic energy contribution; E

Tot

, total

energy contribution; R

CHO

, average rate of carbohydrate oxidation; R

Lip

, average

rate of lipid oxidation; HR, heart rate; Max, maximum; PL, Player Load; CoD,

changes of direction.

*Signiﬁcant differences between game and multi-ball spike training.

and jumps, which can raise one’s HR to 95% of its maximum level

(HRmax) (Gawin et al., 2015; Laﬀaye et al., 2015; Abdullahi and

Coetzee, 2017). However, the workloads and average intensity

are not very high over the entire duration of a match due to

the occurrence of low-intensity intervals between bouts, which

is characterized by 72.6–74.8% ˙

max, 70–85% HRmax, and

1.98–4.6 mM blood lactate concentration (Majumdar et al., 1997;

Cabello and Gonzalez, 2003; Faude et al., 2007; Sung, 2016).

Other studies have revealed that the energy consumption is

signiﬁcantly greater in singles matches when compared with

doubles matches and that these diﬀerences are not related to

a player’s gender (Lee, 2013). Similar to the ﬁndings of the

present study, gender diﬀerences in activity patterns induced

only slightly diﬀerent physiological responses (Fernandez et al.,

2013). Our investigation explored the energy contributions and

workloads in male and female badminton players: while the

similarities in proportional use of the three energy systems

between male and female badminton players during games and

training sessions indicate similar relative energy demands for

both genders, male players showed higher E

PCr

(kJ) during games

and greater energy contributions, including E

PCr

(kJ), E

HLa

(kJ),

Aer

(kJ), and E

Tot

(kJ), during spike practices than female

players. At the same time, male players had a higher average rate

of carbohydrate oxidation during games and repetitive practice

sets. This suggests that players (especially males) should enhance

carbohydrate supplementation during competition and high-

intensity training. Additionally, female players showed greater

workloads in absolute PL and the number of accelerations

compared with male players, while male players accumulated

a signiﬁcantly greater number of jumps during spike practices.

No other diﬀerences in workload variables between genders

were observed. In contract to the present study, Rojas-Valverde

et al. (2020) found gender-related diﬀerences in maximum

accelerations, relative accelerations, and relative distance during

games. This is most likely related to the monitoring equipment’s

method of generating statistics for jumping: the equipment used

in this study only recorded jumps when both feet were oﬀ

the ground simultaneously at a certain vertical height; it did

not record as jumps those movements in which only one foot

left the ground or when one foot left the ground, then the

other. The diﬀerent jumping styles and heights between genders

likely explain the diﬀerence in the number of jumps recorded.

Diﬀerences in workloads between this study and other studies

may be related to the dissimilarities in type, intensity, and

duration of the activities involved (Ghosh et al., 1990, 1993; Chin

et al., 1995; Faude et al., 2007; Aydogmus, 2015; Deka et al.,

2017). For instance, the frequency and movement pattern during

an overhead stroke may diﬀer between players (Sasaki et al.,

2020) while lunging during underhand strokes on the dominant

hand side leg had greater mediolateral acceleration than other

movements (Nagano et al., 2020).

The importance of the aerobic energy supply in badminton

was underestimated in some studies, which observed that

60–70% energy is contributed by the aerobic system and

approximately 30% by the anaerobic system, with greater demand

on the anaerobic alactic metabolism than the lactic anaerobic

metabolism (Phomsoupha and Laﬀaye, 2015). The results of this

study, which observed that almost 95% energy is contributed

by the aerobic system, pointed to the need for a higher aerobic

capacity in competitive badminton players. Daily training should

be designed to further develop a suﬃcient endurance capacity.

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Fu et al. Energy Contributions and Workloads

Furthermore, there are great diﬀerences in the proportions

of the three energy systems between competition and the

intermittent spike training with a 1:2 work:rest ratio. Players

exhibited higher aerobic energy contribution during games, and

higher anaerobic energy contribution during spike training.

This suggests that we should put particular emphasis on

the aerobic ability of badminton players and that a larger

intermittent work:rest ratio of each repetitive drill should be

considered. Integrated training programs should be conducted

to combine physical demands with decision-making demands.

Aerobic assessment using indirect calorimetry is impractical

on the court due to the burden of wearing portable metabolic

devices. Some coaches use

max to distinguish players’

levels, but Ooi observed that

max may not discriminate

elite badminton players from sub-elite counterparts, suggesting

that tactical knowledge and psychological readiness could

be more important for elite athletes (Ooi et al., 2009). We

suggest that players at diﬀerent levels of expertise should

undertake diﬀerent training regimens, with diﬀerent work:rest

ratios and overall durations. Given that HR monitoring

may not provide accurate data on the energetic demands

for badminton players, an indirect calorimetry test on

court to assess energetic demands would be more precise

(Rampichini et al., 2018). Nevertheless, the results from

laboratory treadmill testing seem to be a poor predictor of

a player’s ability, compared to their game play performance

(Heller, 2010).

The present study’s limitations include a lack of repeated

match and training data and the relatively small sample size.

Future research into both energy contributions and workloads

derived from laboratory experiments is warranted in order to

understand the relative diﬀerences in workloads of each player.

The need for additional research also applies to the determination

of sprint classiﬁcations speciﬁc to badminton, as well as game-

speciﬁc CoD or acceleration.

Consequently, we encourage measuring these activities both

in future research and during practices throughout the sports

season. Future studies should expand our knowledge of energy

contributions and workloads in routine badminton drills,

including spike training with diﬀerent work:rest ratios and

other combinations of badminton techniques. Additionally,

this study should be made of badminton players at diﬀerent

levels of expertise.

APPLICATIONS AND CONCLUSION

Our ﬁndings highlight the similarities in proportional use

of the three energy systems between male and female

badminton players throughout competition and repetitive

spike training. Players (especially males) should enhance

carbohydrate supplementation during competition and high-

intensity training in accordance with the higher carbohydrate

oxidation rate observed.

Study results suggest that there are important diﬀerences

in the contributions of the three energy systems between

competition and repetitive spike training. Considering the

need for higher aerobic capacity in competition, it may

be practical for badminton coaches and athletes to choose

appropriate intermittent work:rest ratio in this technique

during high-repetition practices. We suggest that players at

diﬀerent competitive levels should undertake training regimens

of diﬀerent work:rest ratios and overall durations.

Monitoring and quantifying energy contributions and

workloads during matches and training are indispensable

for determining individualized training regimes. Training

programs should be adjusted according to speciﬁc competitive

characteristics in accordance with the demands of diﬀerent

sports. Wearable technologies are an eﬃcient method for

monitoring workloads throughout the season in order to help

enhance players’ performances.

DATA AVAILABILITY STATEMENT

The raw data supporting the conclusions of this article will be

made available by the authors, without undue reservation.

ETHICS STATEMENT

The studies involving human participants were reviewed and

approved by the Ethics Committee of China Institute of Sport

Science, China. Written informed consent to participate in this

study was provided by the participants’ legal guardian/next of kin.

AUTHOR CONTRIBUTIONS

YL and XC contributed to conception and design of the study.

YL, BL, and XC designed the study. XW and LS collected the

data. XW and YF conducted the analyses. YF and YS wrote

the manuscript. All authors read and approved the ﬁnal version

of the manuscript.

FUNDING

This work was supported by the China Institute of Sport Science

(Basic17-30) and National Key Research and Development

Program of China (2018YFF0300500).

ACKNOWLEDGMENTS

We would like to thank all the volunteers who took part in this

study that were supported by Nanjing Sport Institute, Shanghai

University of Sport and China Institute of Sport Science. We

gratefully appreciate all the volunteers who participated in

this study.

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Fu et al. Energy Contributions and Workloads

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potential conﬂict of interest.

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