Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Original article    5 
ABSTRACT 
The aim of our study was to evaluate differences in 
explosive isometric knee extension strength adaptations 
after a flywheel squat resistance training programs 
performed under low- and high-load conditions. Twenty 
physically active adults were randomly assigned to an 
individually allocated high- or low-load eight-week 
training intervention. Isometric knee extension rate of 
torque development (RTD) and rate of electromyography 
signal rise (RER) variables were assessed pre and post 
eight-week intervention. Statistically significant 
improvements in the RTD slope variables (100 and 200 ms 
time intervals after the onset of torque rise; p < 0,05) were 
observed, regardless of the training load used. Normalized 
averaged vastus lateralis and rectus femoris 
electromyography (EMG) amplitude decreased in the 
intervals 80 ms before, and 75, 100 and 200 ms after the 
onset of activation (all p < 0,05), regardless of the training 
group. Our results suggest that high- and low-load 
resistance flywheel training interventions induce similar 
increases in explosive knee extension strength, 
accompanied with a decrease in time-analog normalized 
EMG signal amplitude. 
Keywords: isoinertial, torque, explosive, moment, muscle 
adaptation, EMG 
1
Faculty of Sport, University of Ljubljana, Ljubljana, 
Slovenia 
2
Department of Automation, Biocybernetics and 
Robotics, Jožef Stefan Institute, Ljubljana, Slovenia 
3
Faculty of Health Sciences, University of Primorska, 
Izola, Slovenia 
4
Human Health Department, InnoRenew CoE, Izola, 
Slovenia 
5
Laboratory for Motor Control and Motor Behavior, 
Science to Practice Ltd., Ljubljana, Slovenia 
 
 
 
IZVLEČEK 
Namen raziskave je bil primerjati učinke dveh protokolov 
počepanja na inercijski napravi na eksplozivno moč 
iztegovalk kolena. Protokola sta se razlikovala v velikosti 
inercijskega bremena, ki je bilo za vsakega posameznika 
individualno določeno pred začetkom vadbe. Dvajset 
redno telesno aktivnih odraslih je bilo naključno 
razdeljenih v dve skupini, eno, ki je vadila z velikim 
inercijskim bremenom, in drugo, ki je vadila z majhnim 
inercijskim bremenom. Spremenljivke hitrosti prirastka 
navora in amplitude elektromiografskega signala (EMG) 
pri eksplozivnem iztegu kolena v izometrični upornici so 
bile izmerjene pred in po 8-tedenskem obdobju vadbe. V 
obeh skupinah smo odkrili statistično značilno izboljšanje 
spremenljivk hitrosti prirastka navora (100 in 200 ms po 
začetku prirastka navora; p < 0,05). Normalizirana 
amplituda EMG signala mišic vastus lateralis in rectus 
femoris v intervalih 80 ms pred začetkom prirastka navora 
in 75, 100 ter 200 ms po začetku prirastka navora pa se je 
statistično značilno zmanjšala v obeh skupinah (vse p < 
0,05). Rezultati kažejo, da inercijska vadba za moč nog z 
majhnim in velikim bremenom povzroči podoben 
napredek v hitrosti prirastka navora pri eksplozivnem 
iztegu kolena kljub zmanjšanju normalizirane amplitude 
EMG signala. 
Ključne besede: izoinercija, navor, eksplozivna 
kontrakcija, mišična prilagoditev, EMG 
Corresponding author*: Darjan Spudić,  
Faculty of Sport, University of Ljubljana, 1000 Ljubljana, 
Slovenia 
E-mail: darjan.spudic@fsp.uni-lj.si 
https://doi.org/10.52165/kinsi.29.3.5-25 
Darjan Spudić 
1,*
 
Benjamin J. Narang 
1,2
 
Primož Pori 
1
 
Vojko Strojnik 
1
 
Nejc Šarabon 
3,4,5
 
Igor Štirn 
1
 
 
EFFECTS OF TWO DIFFERENT FLYWHEEL 
RESISTANCE TRAINING PROTOCOLS ON 
EXPLOSIVE KNEE EXTENSION STRENGTH: A 
PILOT RANDOMIZED STUDY 
PRIMERJAVA UČINKOV DVEH RAZLIČNIH 
PROTOKOLOV INERCIJSKE VADBE ZA MOČ NOG 
NA EKSPLOZIVNO MOČ IZTEGOVALK KOLENA: 
RANDOMIZIRANA PILOTNA ŠTUDIJA 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   6 
INTRODUCTION 
The rate of force development (RFD) refers to the ability of the neuromuscular system to 
increase contractile force from a low or resting level when muscle activation is performed as 
quickly as possible. It is considered an important parameter of muscular performance, 
especially for athletes competing in sports that require high-speed actions (Rodríguez-Rosell et 
al., 2018). Therefore, the RFD is commonly assessed to monitor the effects of training 
interventions. The evaluation of RFD in human skeletal muscle is complex, as it is influenced 
by numerous distinct methodological factors including mode of contraction, type of instruction, 
variable type and time interval used to quantify RFD, measurement device and ambient 
temperature (Maffiuletti et al., 2016). 
Neuromuscular components that influence the expression of RFD can be assessed along with 
their corresponding mechanical variables by quantifying the electromyography (EMG) signal 
in the time-analog intervals. Motor unit (MU) discharge rate, doublet discharge, MU 
recruitment threshold and MU synchronization may play a critical role in the ability to achieve 
rapid force development during the initial phase of muscle contraction. As maximal RFD can 
only be obtained during electrical muscle stimulation with very high pulse rates (de Haan, 1998) 
(approximately double those needed to elicit maximal isometric force production in vivo [Sale, 
1988]) it was concluded in the past that such supramaximal MU discharge rates in the initial 
phase of a muscle contraction serve to maximize RFD rather than to influence maximal muscle 
strength. Moreover, concurrently active MUs may optimize the rate of force generation via 
increased superposition of MU force twitches (Rodríguez-Rosell et al., 2018). Also, an 
interesting mechanism responsible for high force generation from the onset of muscle 
contraction is the premovement silent period or premotor silence (PMS). The first detection of 
EMG activity in an agonist muscle may be a decrease that occurs prior to the first agonist EMG 
activity (Conrad et al., 1983), before the rapid increase in the signal that indicates agonist action. 
This mechanism could serve to increase the synchrony of the MU pool, which may be 
functionally related to contractile rate (Walter, 1989). It was previously found that the increase 
in amplitude of EMG signal at the beginning of muscle contraction reflects the MU discharge 
rate and the MU recruitement threshold (Del Vecchio et al., 2019), therefore EMG amplitude 
measures could be used to quantify differences in MU behaviour over time (Elgueta-Cancino 
et al., 2022) (Duchateau & Baudry, 2014). 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   7 
Increases in RFD, accompanied with increases in efferent motoneuron output (as evidenced by 
changes in EMG signal amplitude) have been reported after slow concentric heavy-resistance 
strength training and explosive or power-type resistance training (Maffiuletti et al., 2016). 
Results in the previous literature indicate that slow concentric, high load, low volume (90% 
1RM, 3x5 reps) resistance training is more advantageous than slow concentric, moderate load, 
high volume (70%, 3x12 reps) for improving RFD characteristics in closed kinetic chain 
exercises (Mangine et al., 2016). And, it was also found in the recent study of Rodriguez-Lopez 
et al. (2022) that the improvement in RFD variables did not differ between heavy load (80% 
1RM, 6x6 reps) and low load (40% 1RM, 6x12 reps) explosive-type leg press training groups. 
These results confirm a thirty-year-old finding by Behm & Sale (1993), that intended ballistic 
(explosive) effort is thought to be a more important factor in increasing RFD than the type of 
contraction performed and the actual movement velocity defined by the loading condition 
(Behm, 1995). 
Flywheel (FW) inertial resistance training is an emerging training mode that is gaining research 
interest due to the variety of positive effects of FW interventions observed in strength- and 
power-related variables (González, de Keijzer, bishop, & Beato, 2020; Maroto-Izquierdo et al., 
2017; Nuñez Sanchez & Villarreal, 2017; Petré, Wernstål, & Mattsson, 2018), and the 
functional abilities of athletes (Beato, Fleming, Coates, & Dello Iacono, 2020; Cabanillas, 
Serna, Muñoz-Arroyave, & Ramos, 2020; Raya-González, Castillo, & Beato, 2020). 
Moreover, FW devices are also frequently used in clinical settings (Gual, Fort-
Vanmeerhaeghe, Romero-Rodriguez, & Tesch, 2015; Romero-rodriguez, Gual, & Tesch, 2010; 
Tesch, Fernandez-Gonzalo, & Lundberg, 2017). A key limitation of existing research regarding 
FW resistance training is the lack of intervention studies. Consequently, the specificity of FW-
based training methods is poorly understood, which represents a limitation in designing an 
appropriate program for developing the strength or power abilities of athletes. While FW 
resistance training has been shown as a useful tool to increase RFD in squat and 
countermovement jump tests (Weng et al., 2022), to date, no study has investigated the knee 
extensor's RFD or the rate of EMG rise (RER) adaptations to an FW training intervention under 
different loading conditions. 
Increases in maximal strength following training with heavy loads results in a concurrent 
improvement in RFD (Aagaard et al., 2002). The prescription of heavy loads is related to the 
size principle for MU recruitment. The type 2 muscle fibres, which are considered 
predominately responsible for powerful and explosive athletic performances (Duchateau & 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   8 
Baudry, 2014) are theorized to be more fully recruited and thus trained when training involves 
heavy loads (Cormie et al., 2011; Kamen & Knight, 2004). In FW squats, heavier loads result 
in increased ground reaction forces during both the concentric and eccentric phases, as well as 
during the transition between the two. However, they lead to reduced velocity and power 
generation (McErlain-Naylor & Beato, 2021; Spudić et al., 2020b, 2021). Eccentric overload 
(the difference between peak force produced in the eccentric and concentric parts of the squat) 
increases with greater FW loads (Spudić et al., 2021) and the transition from the eccentric to 
the concentric part of the squat lengthens with increased FW loads (Martinez-Aranda & 
Fernandez-Gonzalo, 2017). In proportion to force production, the EMG amplitude of leg 
extensor muscles also increases with rising FW loads (Spudić et al., 2020a). The variations in 
FW squat kinetics resulting from different loading conditions could potentially induce distinct 
RFD and accompanying RER training adaptations in knee extensor muscles. 
The aim of this study was to evaluate the differences in explosive isometric knee extension 
strength adaptations after a FW squat resistance training program performed under low and 
high loading conditions. We hypothesized greater increases in the knee extension rate of torque 
development (RTD) and accompanied vastus lateralis and rectus femoris RER variables to be 
observed in the HL group. The results could help finding the optimal load for explosive strength 
improvement using FW devices which serve as the foundation for future comparisons of the 
magnitude of improvement with gravity-based resistance exercises. 
 
METHODS 
Study design 
The study was designed as a randomized parallel-group trial to investigate the effects of eight 
weeks of FW squat resistance training under high-load (HL) and low-load (LL) conditions. 
Testing of explosive isometric knee extension strength was performed 3-5 days both before and 
after the 8-week training period. 
Participants 
Twenty physically-active volunteers were randomly assigned to HL (n = 10) or LL (n = 10) 
training group. Participant characteristics are shown in Table 1. The inclusion criterion was 
strength-training experience, defined by a training history that included strength exercises at 
least two times per week for the previous year. Exclusion criteria included knee injuries (e.g. 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   9 
ligament, meniscus or cartilage damage), chronic diseases (systemic, cardiac and/or respiratory 
diseases or neuromuscular disorders), history of lower back pain or acute injuries in the past six 
months that could negatively influence squatting performance. Participants assigned to the 
experimental groups participated in an eight-week FW resistance training program. Stratified 
random sampling was used to allocate participants to the LL or HL FW training group. The 
stratification variable was the slope of the linear regression Force-Velocity curve, assessed 
using FW squats with incremental load conditions. Randomization was performed by a member 
of the research team who was not directly involved in the recruitment or assessment of 
participants, using a computer-generated random allocation program and a 1:1 ratio (LL:HL). 
Five to three weeks before the initial explosive knee extension testing and testing on the FW 
device, participants underwent familiarization sessions. This process aimed to ensure 
participants were comfortable and proficient with the testing and squat execution techniques 
before the actual testing sessions. These sessions consisted of one explosive knee extension 
strength testing session and between eight to ten FW squat familiarization sessions. During the 
familiarization sessions, an experienced investigator verbally described and demonstrated the 
correct procedure for explosive knee extension strength testing and the FW squat technique. 
After the demonstration, participants performed explosive knee extensions and completed two 
or three sets of FW squats with different FW loads, respectively. The study was approved by 
the National Medical Ethics Committee (no. 0120-690/2017/8) and adhered to the tenets of the 
Oviedo Convention and the Declaration of Helsinki. Participants were informed about the 
testing procedures and provided written informed consent prior to their participation. 
Table 1. Participant characteristics. 
Group Sex 
Age 
(y) 
Height 
(m) 
Body mass (kg) BMI (kg/m
2
) 
IPAQ (MET-
min/week) 
Peak KET 
(Nm/kg) 
Pre Post Pre Post Pre Post Pre Post 
HL 
M 
24,8 
(2,4) 
1,78 
(0,7) 
73,1 
(11,9) 
75,4 
(11,6) 
22,9 
(2,8) 
23,6 
(2,5) 
6338 
(4241) 
6096 
(5099) 
3,35 
(0,68) 
3,91 
(0,58) 
F 
24,6 
(2,7) 
1,65 
(0,4) 
59,2 
(4,2) 
59,6 
(4,1) 
21,7 
(1,1) 
21,9 
(0,9) 
4256 
(3986) 
3729 
(1446) 
3,25 
(0,25) 
3,60 
(0,69) 
M+F 
24,7 
(2,4) 
1,72 
(0,9) 
66,2 
(11,2) 
67,5 
(11,7) 
22,3 
(2,1) 
22,7 
(2,0) 
5297 
(4032) 
4912 
(3747) 
3,30 
(0,42) 
3,76 
(0,63) 
LL 
M 
24,2 
(2,8) 
1,82 
(0,7) 
80,8 
(12,5) 
81,1 
(10,7) 
24,2 
(2,5) 
24,3 
(2,1) 
3588 
(1063) 
4174 
(560) 
3,60 
(0,51) 
4,26 
(0,52) 
F 
23,2 
(2,8) 
1,68 
(0,6) 
65,4 
(10,5) 
66,5 
(10,3) 
23,0 
(2,6) 
23,4 
(2,5) 
3695 
(3935) 
3035 
(2733) 
2,84 
(0,65) 
3,38 
(0,65) 
M+F 
23,7 
(2,7) 
1,75 
(1,0) 
73,1 
(13,6) 
73,8 
(12,6) 
23,6 
(2,5) 
23,9 
(2,2) 
3641 
(2718) 
3604 
(1955) 
3,22 
(0,68) 
3,82 
(0,72) 
Combined 
24,2 
(2,5) 
1,74 
(0,9) 
69,6 
(12,6) 
70,6 
(12,2) 
23,0 
(2,3) 
23,3 
(2,1) 
4469 
(3453) 
4258 
(2985) 
3,30 
(0,42) 
3,75 
(0,63) 
Notes: Data are mean (SD). Physical activity data represent a retrospective recall of the seven days prior to each testing session. 
Abbreviations: BMI, body mass index; F, female (n = 5 in each group); HL, high-load (70% Pmax); IPAQ, international physical 
activity questionnaire (Hagströmer, Oja & Sjöström, 2006); KET, knee extension torque; LL, low-load (Pmax) ; M, male. 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   10 
Training sessions 
The training intervention consisted of FW squats using an allocated load depending on 
condition (HL vs. LL). The volume of the exercise intervention progressed throughout the eight 
weeks. The number of sets increased from three (week 1, 2), through four (week 3, 4, 5), to five 
(week 6, 7, 8) per training session. Each set consisted of ten repetitions, and there was a five-
minute rest between sets. The first two repetitions of each set were used to attain proper squat 
execution (tempo and amplitude) and the last repetition was used to decelerate the spinning FW 
safely. From repetitions three to nine (7 repetitions), the participants were instructed to perform 
the concentric phase as fast as possible, and delay braking during the first third of the eccentric 
phase while making the transition from the eccentric to the concentric phase as short as possible 
(»all-out« execution). Squat amplitude was determined from the lowest point (approximatelly 
90° knee angle) to full knee extension (approximatelly 0° knee angle). Participants crossed their 
arms and placed their hands on opposing shoulders. Lifting heels of the ground was not allowed. 
Participants received verbal encouragement and feedback from the researcher during all 
training sessions.  
Figure 1. Testing and training setup. 
 
Notes. The flywheel (FW) exercise device utilized the inertia of a spinning FW (A) to produce resistance. The FW standing 
platform (B) with force plates (C) size was 1.1 x 0,6 m, rotary shaft diameter was 3 cm and pulling rope diameter was 6 mm. 
A harness (D) was used to aid in performing FW squats. For the load allocation testing, a draw-wire sensor was installed under 
the device. The wire originated directly above the center of the axis - to avoid diagonal vertical displacement (E). The distal 
part of the wire was attached to the harness rope attachment (between the legs) (F). Sensory information was not captured 
during training sessions. 
 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   11 
Load allocation 
Load allocation was individually defined, based on the initial Force-Velocity-Power (F-v-P) 
testing in FW squats, following a previously-reported testing procedure, data aquisition and 
statistical analysis approach (Spudić et al., 2020b). Briefly, two sets of 10 consecutive squats 
were performed at four FW loading conditions (0,025, 0,075, 0,225 and 0,25 kg∙m
2
). The first 
two repetitions were used to attain proper squat execution and the last two were used to 
decelerate the spinning FW safely. Repetitions two to eight (6 repetitions) were performed »all-
out« and included in the further analyses. Participants received verbal encouragement from the 
researchers during all testing sessions. To standardize squat depth, vertical displacement was 
monitored and provided as real-time feedback on a screen in front of the participants. Rest 
periods between the different loading conditions were two minutes to allow participants to 
adequately recover from the previous load (Sabido et al., 2020). Four F-v-P outcome measures 
were determined based on linear regression analyses between mean concentric ground reaction 
force and mean concentric vertical velocity. Maximal theoretical force (F0), which represents 
the force-intercept, and the slope (k) of the F-v relationship, which in turn enabled the 
calculation of maximal theoretical velocity (velocity-intercept; V0 = F0/k) and maximal 
theoretical power (Pmax=(F0∙V0)/4) (Samozino et al., 2010). Based on individual F-v-P 
characteristics, LL were allocated to the closest FW load at which the vertical velocity 
corresponded to the calculated Pmax, while HL were allocated to the closest FW load at which 
the vertical velocity corresponded to 70% Pmax (Figure 2). Loads were determined with the 
accuracy of 0,025 kg∙m
2
. Prior to the load allocation session, participants performed the 
standardized 10-min warm-up procedure, as well as seven submaximal FW squats using a 
medium FW load (0,125 kg∙m
2
).  
  

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   12 
Figure 2. Representative individual Force-velocity-Power characteristics graph assessed with 
flywheel squats. 
 
Notes. The black line represents the linear regression between the mean force and velocity variables. The y-intercept represents 
maximal theoretical force and the x-intercept indicates maximal theoretical velocity. The grey curve is the hyperbolic 
relationship between power and velocity during FW squats using incremental loading conditions. 70% Pmax corresponds to the 
high load group (HL) and Pmax to the low load group (LL) load allocation. Abbreviations: Pmax, maximal theoretical power; v0, 
maximal theoretical velocity, F0, maximal theoretical force. 
Testing procedures 
Participants were instructed to avoid any strenuous exercise for at least two days prior to each 
testing session. Physical activity levels for the seven days leading up to each test were 
determined retrospectively upon arrival to the laboratory using the International Physical 
Activity Questionnaire (Hagströmer, Oja, & Sjöström, 2006). Before the testing and training 
sessions, participants performed a standardized 10-min warm-up procedure. This consisted of 
two minutes of alternating step-ups on a 25-cm high bench (80 repetitions per minute); arm, 
hip, knee and ankle mobility exercises (10 repetitions each); dynamic stretches of hip flexors, 
knee extensors, knee flexors and ankle extensors (10 repetitions each); and heel raise, squat, 
crunches resistance exercises (10 repetitions each). After the general warm-up, each participant 
performed five maximal squat jumps, counter-movement jumps and jump push-ups.  
Isometric knee extension testing 
Figure 3 shows the experimental setup for the knee extension tests. These were performed on a 
rigid chair (custom design), equipped with a strain-gauge force sensor (MES, Maribor, 
Slovenia) with a constant lever to the chair (knee) axis which provided torque-time data. 
Participants were in a seated position (90° hip and 60° knee flexion angle, 0° represents full 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   13 
knee and hip extension), and were securely strapped to the chair at the hip. The rotational axis 
of the lever arm was aligned to the lateral femoral epicondyle by visual inspection, and the 
lower leg was attached to the lever arm above the medial malleolus. The individual positioning 
of the seat and lever arm for each participant was replicated for their pre- and post-intervention 
tests. During the tests, participants were instructed to hold onto the arm supports on either side 
of the chair. All measurements were performed in the push-off preferred leg. Feedback was 
provided on a screen in the front of the subject. 
Figure 3. Static knee dynamometer setup for the maximal isometric contraction and rate of force 
development testing protocols.  
 
After three submaximal preconditioning trials, each participant performed three knee 
extensions at maximal voluntary contraction (MVC) effort, separated by at least 60 s rest. 
Participants were instructed to contract as forcefully as possible and to sustain maximal effort 
for at least three seconds. Visual feedback of the instantaneous dynamometer torque was 
provided to the participants on a computer screen. 
After five minutes of passive rest, explosive knee extension measures were performed in the 
same testing position as MVC. To obtain a valid measure of the ability to produce torque 
quickly, a pre-tension was defined as 5% of MVC torque. Before initiating a fast contraction, 
participants were instructed to obtain the target pre-tension utilizing a computerized digital 
readout for at least one second. Participants were instructed to contract as fast and as forcefully 
as possible, and to sustain the contraction for at least two seconds. Three valid trials were 
required for the analysis. To ensure adequate PMS, trials with an initial countermovement 
(identified by a visible drop in the torque signal) were excluded, and a replacement trial was 
performed. 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   14 
During MVCs and explosive knee extensions, EMG activity was assessed using a Trigno 
Delsys Wireless System (Delsys Inc., Massachusetts, USA), with pre-amplified self-adhesive 
wireless electrodes (dimensions: 27 x 37 x 15 mm; mass: 14.7 g; electrode material: silver; 
contact dimension: 5 x 1 mm). After skin preparation (shaving, light abrasion, and cleaning 
with alcohol; < 5 kΩ), the electrodes were placed over the vastus lateralis (vl) and rectus femoris 
(rf) of the preferred leg, according to SENIAM recommendations (Hermens et al., 2000). 
Data processing 
Signals were processed according to previous research (Maffiuletti et al., 2016). Torque and 
EMG data were simultaneously sampled at 1000 Hz using a USB Data Acquisition System 
(Power Lab, National Instruments, Austin, USA) and visualized using LabChart 8 
(ADInstruments, Bella Vista, Australia). Torque data were subsequently adjusted to account 
for the effect of gravity on the lower limb and normalized to indivdual’s body mass – separately 
for pre and post measurements, respectively. The onset of muscle contraction was defined as 
the time point at which the torque curve exceeded baseline torque value by five standard 
deviations. Baseline torque time interval was set from 1 s to 0,5 s before the time-point of peak 
RTD (defined as the maximal instantaneous slope of the torque-time curve at one ms interval) 
to avoid mistakes in the onset threshold calculation due to signal disturbance (vibrations, 
countermovement, anticipation, etc.). The rate of torque increase, defined as the slope of the 
torque-time curve at 100 and 200 ms time points relative to the onset of contraction (Slope100, 
Slope200) were identified in each trial (Figure 4). 
The EMG data were bandpass-filtered using a Butterworth fourth-order filter (20–500 Hz), and 
then rectified using a root mean square (RMS) function (50 ms window length). The onset of 
EMG activation was defined as the time point at which the EMG amplitude curve exceeded 
baseline EMG amplitude value by five standard deviations. Baseline EMG time interval was 
set from 1 s to 0,5 s before the time-point of peak RER (defined as the maximal instantaneous 
slope of the EMG amplitude-time curve at one ms interval). The following EMG variables were 
identified in each trial: (a) average EMG amplitude in the 75, 100 and 200 ms time intervals 
relative to the onset of EMG activation (EMG75, EMG100, EMG200) and, (b) average EMG 
amplitude during 80 ms time interval before the onset of muscle activation (EMG-80). This final 
variable was identified to control for the potential enhancement of performance without an 
adequate PMS (Conrad et al., 1983). Time intervals of 75 ms was used due to a decrease in 
EMG signal amplitude after 80-100 ms of neural activity after the onset of explosive contraction 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   15 
– as reported in previous studies (Aagaard et al., 2002). EMG amplitude variables were 
expressed as a percentage of the average EMG activity during the MVC trial with the highest 
torque produced (%MVC). EMG value during MVC was calculated as a peak value of MVCRMS 
signal on a one-second moving window, separately for pre and post measurements (Figure 4). 
Signal processing was performed in LabChart 8. The data were then copied into custom-written 
Excel spreadsheets (Microsoft Corporations, Redmond, Washington, USA) where the RTD and 
RER variables were calculated. 
Figure 4. Representative curve of the torque (above) and EMGRMS (below) signal. 
 
Notes. The onset of contraction was separately determined for torque and EMG signal. Area A corresponds to 80 ms before the 
activation onset, area B for first 100 ms and area C for the first 200 ms, relative to the onset of contraction or EMG activation. 
Pretension of 5% of MVC is presented with a horizontal dotted line on the torque-time curve. 
Statistical analyses 
All data are presented as mean (standard deviations - SD). An average value of three trials 
results (RFD and EMG amplitude) were entered into the analyses. The Shapiro-Wilk test 
confirmed that all data were adequately normally-distributed (p > 0,05) for parametric tests to 
be conducted. Differences between groups were checked before the start of the intervention 
using an independent-samples t-test. Then, a two-way mixed-effects ANOVA (Time [pre, post] 
* Group [LL, HL]) was used to compare the changes in the variables across the intervention 
and between groups. Equality of variances between groups were confirmed using Levene’s test 
(p > 0,05). In the event that the assumption of sphericity was violated (Mauchly’s Test of 
Sphericity < 0,05), the Greenhouse–Geisser correction was applied. The reported effect size 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   16 
(ES) from the univariate model for the comparisons was Partial eta squared (η
2
), where the 
criteria for ES were small (ES = 0,010), medium (ES = 0,059) and large (ES = 0,138) (Kotrlik 
& Williams, 2003). Intra-session reliability of the results was assessed using intraclass 
correlation coefficients (ICC2.1) (Koo & Li, 2016) and coefficients of variation (CVs) (Hopkins, 
2000). ICC2.1 values were interpreted according to guidelines (< 0,50: poor reliability, 0,50–
0,75: moderate reliability, 0,75–0,90: good reliability, > 0,90: excellent reliability) (Koo & Li, 
2016). All statistical analyses were performed using SPSS (IBM SPSS Statistics for Windows, 
Version 28.0. Armonk, NY: IBM Corp, USA). An alpha value of 0,05 was accepted as the level 
of statistical significance. 
 
RESULTS 
No statistically significant differences were found for any of the RTD and RER variables before 
the intervention (all p-values > 0.05). 
Training adaptations 
Table 2 reports the changes in the RTD parameters for each group across the interventions. A 
main effect of time was observed for all variables. Each of these main effects of time reflected 
an increase in the respective variable across the intervention. There were however no main 
effects of group (p = 0,507–0,654) or time*group interaction effects (p = 0,718–0,993). 
Table 2. Changes in the rate of torque development variables across the intervention in each 
group. 
Variable Group Pre Post 
∆ 
(Post – Pre) 
ANOVA 
Main Effect of Time Main Effect of Group Time*Group Interaction 
F-ratio p-value η² F-ratio p-value η² F-ratio p-value η² 
Slope
100
 
([Nm/kg]/s) 
HL 
18,127 
(2,494) 
19,946 
(3,446) 
1,38 (3,21) 
70,143 0,016* 0,284 0,208 0,654 0,011 0,135 0,718 0,007 
LL 
17,706 
(4,827) 
19,086 
(2,297) 
1,819 (2,004) 
Slope
200
 
([Nm/kg]/s) 
HL 
24,371 
(3,674) 
27,892 
(4,88) 
3,509 (3,598) 
260,545 <0,001* 0,596 0,458 0,507 0,025 0,021 0,993 <0,001 
LL 
22,919 
(5,060) 
26,428 
(3,878) 
3,520 (2,380) 
Note: Data are mean (SD). Abbreviations: ANOVA, analysis of variance; HL, high-load (70% Pmax); LL, low-load (Pmax); 
MVC, maximal voluntary contraction; η² - eta squared; * - p < 0,05. 
Changes in the mean EMG variables for each group across their respective interventions are 
presented in Table 3. Main effects of time were observed for all variables. Each of these main 
effects of time reflected a decrease in the respective variable across the intervention. There were 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   17 
no significant main effects of group (p = 0,101–0,607) or time*group interaction effects (p = 
0,357–0,878) in any of the EMG variables. 
Table 3. Changes in the observed vastus lateralis and rectus femoris EMG variables across the 
intervention in each group. 
Muscle Variable Group Pre Post 
∆ 
(Post – Pre) 
ANOVA 
Main Effect of Time Main Effect of Group 
Time*Group 
Interaction 
F-ratio p-value η² F-ratio p-value η² F-ratio p-value η² 
VL 
EMG 75 
(%MVC) 
HL 47,7 (22,7) 46,3 (24,2) -3,6 (22,8) 
0,257 0,616 0,008 1,547 0,223 0,046 0,052 0,821 0,002 
LL 42,6 (18,3) 39,0 (12,7) -1,4 (33,6) 
EMG 100 
(%MVC) 
HL 90,6 (22,8) 66,3 (36,8) -24,3 (29,3) 
13,303 0,002* 0,425 0,862 0,365 0,046 0,030 0,865 0,002 
LL 80,1 (28,4) 58,0 (12,8) -22,1 (27,6) 
EMG 200 
(%MVC) 
HL 91,2 (21,1) 73,3 (25,9) -17,9 (22,9) 
13,801 0,002* 0,434 2,439 0,136 0,119 0,024 0,878 0,001 
LL 79,9 (22,2) 60,4 (9,5) -19,4 (21,9) 
EMG -80 
(%MVC) 
HL 10,3 (3,9) 7,0 (5,5) -3,3 (5,7) 
9,240 0,007* 0,339 0,794 0,385 0,042 0,030 0,863 0,002 
LL 9,1 (3,8) 5,4 (4,0) -3,7 (4,5) 
RF 
EMG 75 
(%MVC) 
HL 6,1 (2,9) 5,7 (2,7) -0,4 (3,1) 
1,951 0,172 0,057 0,269 0,607 0,008 0,546 0,465 0,017 
LL 7,1 (4,9) 5,9 (3,0) -1,2 (3,4) 
EMG 100 
(%MVC) 
HL 67,6 (21,4) 54,1 (30,0) -13,5 (33,8) 
5,504 0,032* 0,256 0,509 0,486 0,031 0,031 0,864 0,002 
LL 63,3 (17,0) 47,6 (8,7) -15,7 (15,8) 
EMG 200 
(%MVC) 
HL 77,5 (19,7) 67,8 (24,4) -9,6 (28,0) 
5,790 0,029* 0,226 1,218 0,286 0,071 0,190 0,669 0,012 
LL 72,4 (12,6) 58,5 (7,5) -3,9 (8,7) 
EMG -80 
(%MVC) 
HL 13,2 (6,2) 8,3 (6,3) -4,9 (7,0) 
7,214 0,018* 0,340 3,086 0,101 0,181 0,909 0,357 0,061 
LL 8,6 (2,5) 6,2 (3,0) -2,3 (3,6) 
Note: Data are mean (SD). Abbreviations: ANOVA, analysis of variance; EMG, electromyography; HL, high-load (70% Pmax); 
LL, low-load (Pmax); MVC, maximal voluntary contraction; η² - eta squared; * - p < 0,05. 
Intra-session reliability 
The CV results for RTD variables ranged from 4,5 (3,2)% (post Slope200) to 14,1 (12,8)% (pre 
Slope100). On the contrary, EMG variables demonstrated unacceptable CVs (>10%). The CV 
results for vl EMG variables ranged from 11,4 (6,5)% (pre EMG200) to 31,1 (16,8)% (pre EMG-
80). The CV results for rf EMG variables ranged from 12,3 (9,1)% (post EMG200) to 29,3 
(21,5)% (post EMG75). Moreover, ICC values ranged from moderate (ICC = 0,649 for post 
Slope100) to excellent (ICC = 0,92 for pre Slope200) for RTD variables; from moderate (ICC = 
0,52 for pre EMG-80) to good (ICC = 0,82 for post EMG75) for vl EMG variables and from 
moderate (ICC = 0,57 for pre EMG200) to good (ICC = 0,890 for post EMG75) for rf EMG 
variables. 
 
  

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   18 
DISCUSSION 
The aim of this study was to compare the effects of HL vs. LL FW squat resistance training 
programmes on explosive isometric knee extension strength. Improvements in RTD variables 
were observed, whereas vl and rf normalized EMG amplitude variables displayed a decrease 
across the interventions. In contrast to the hypothesis, no significant differences in adaptations 
were observed between the HL and LL groups. 
The most important outcome of this study is the discovery that similar improvements in RFD 
were observed after HL and LL FW resistance squat training. In general, there is a lack of 
studies in the literature comparing the influence of LL and HL explosive or power type training 
on RFD adaptations. Considering the association between the use of heavy loads and the size 
principle for MU recruitment, as well as the engagement of type 2 muscle fibers — 
predominantly responsible for powerful and explosive athletic performances (Duchateau & 
Baudry, 2014) — we initially hypothesized that different FW loading conditions would lead to 
varying RFD and neural adaptations. However, the current data demonstrate no significant 
differences between the HL and LL groups in RTD and RER variables. On the other hand, the 
use of an intended ballistic effort is thought to be a more important factor in increasing RFD 
than the type of contraction performed and the actual movement velocity (D. G. Behm & Sale, 
1993). This seems to be also valid for FW resistance training conditions. The intention to rotate 
the FW load as quickly and forcefully as possible appears to be more relevant to improving 
explosive knee extension strength than the magnitude of the FW load. 
Notably, normalized EMG amplitudes in the early intervals (75 and 100 ms) and the late interval 
(200 ms) after the onset of contraction were reduced following the intervention, regardless of 
the training group. This contradicts other studies but supports the notion that certain latent 
factors, unassessable using the RER measurement technique, protocol, and variables employed 
in this study, led to increased associated mechanical variables. The lower EMG burst along with 
the observed improvements in RFD in this study could be attributed to several explanations: (a) 
predominance of morphological adaptations over neural changes (e.g., pennation angle 
alterations (Oliveira et al., 2016) or changes in the relative proportion of fast-twitch muscle 
fibers – IIx decrease over IIa); (b) potential antagonist relaxation contributing to increased net 
torque without agonist adaptations; and (c) RER variables normalization to corresponding EMG 
values during MVC. We noted an average increase of 16,5% in MVC knee extension torque 
values, accompanied by an 80,5% increase in non-normalized EMG amplitude. Conversely, a 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   19 
11% increase was observed in Slope200, accompanied by a mere 1% increase in non-normalized 
amplitude and, subsequently, a 19% decrease in normalized vl EMG200 amplitude. This suggests 
that participants made greater gains in maximal strength compared to explosive isometric 
strength and that neural adaptations were more pronounced during maximal strength testing 
(Del Vecchio et al., 2022). To further confirm this assumption, activation level methods 
(Strojnik & Komi, 1998) could have been utilized, while the non-normalized voltage potential 
of the surface electromyographic signal detected by the electrodes depends strongly on several 
factors that vary between individuals and also over time within an individual (Sousa & Tavares, 
2012). It appears that RTD variables increased due to gains in maximal strength, consistent with 
the findings of Aagaard et al. (2002), where increases in maximal strength following heavy load 
training lead to concomitant improvements in RTD. This conclusion is further supported by the 
absence of discernible neural adaptations during the explosive strength test. Additionally, we 
cannot dismiss the likelihood that morphological muscle-tendon adaptations influenced the 
RTD increase, particularly because typical neural adaptations during explosive strength tests 
encompass PSP, higher muscle firing frequency, MU synchronization, earlier recruitment of 
large MUs, and an increased number of doublet discharges. These adaptations usually result in 
amplified EMG amplitude during explosive strength tests (Del Vecchio et al., 2022), but were 
not evident in our study. 
Rapid force generation and underlying neural mechanisms could have been altered, presumably 
due to specific squat execution with FW load. Due to the »all-out« eccentric-concentric nature 
of repetitions in FW squats, it can be challenging to precisely control muscle tension right 
before the onset of the explosive concentric effort, in contrast to gravity-based resistance, where 
the load can be supported before the start of the movement. An inverse linear relationship was 
previouly observed between %MVC and RFD (Viitasalo, 1982). And, it was found that higher 
muscle pretension negatively influences fast MUs firing frequency, double discharges and MU 
synchronization at the initiation of the contraction (Ricard et al., 2005; Van Cutsem & 
Duchateau, 2005). Moreover, it could be speculated that eccentric muscle contraction during 
the braking phase of the FW squat provides an initial condition (i.e., high muscle pretention) 
for the concentric part of the squat that negatively affects the capacity of the MU discharge rate 
and thus the performance of an explosive voluntary contraction. It appears that not only HL, 
but also LL has exhibited the initial pretension to such as extend to negatively influence relative 
RER adaptations. Furthermore, when performing a FW squat with delaying the braking action 
in the first third of the eccentric phase, eccentric overload can be achieved (Tesch et al., 2004). 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   20 
Eccentric forces and eccentric overload were found to be higher when squatting with higer FW 
loads (Spudić et al., 2021). Neuromuscular activation of the quadriceps femoris muscle was 
found to be inhibited during slow and fast eccentric contractions despite full voluntary effort 
(Aagaard et al., 2000), which is attributed to a tension-limiting mechanism specific to eccentric 
exercise action (Alcazar et al., 2019; Amiridis & et al., 1996). Therefore, especially in HL 
group, high eccentric force demands in FW squats might have caused neuronal inhibition, 
limiting the recruitment and/or discharge of MUs and therefore representing unfavorable 
neuronal conditions for rapid force generation and consequently RER adaptations over an 8-
week period.  
Peñailillo et al. (2015) concluded that RFD100–200 is a more specific and sensitive indirect 
marker of eccentric exercise-induced muscle damage than peak torque during MVC. It could 
be speculated that the improvement of RTD and concurrent EMG variables were affected by 
the prolonged loss of muscle function after the last training session before the final testing – 
especially in the HL group. This could have been mitigated by conducting the final testing more 
than 5 days after the last training session. However, studies on the repeated bout effect after 
eccentric exercises (Nosaka et al., 2001) and FW squat exercises (Coratella et al., 2016) have 
shown a muscle protective role of previous eccentric exercise in subsequent bouts, lasting up 
to 6 or 9 months (Nosaka et al., 2001). The possibility of muscle damage affecting differences 
in RTD and EMG variables between groups in our study is therefore less likely (Warren et al., 
2002). Moreover, different stability requirements and uneven training volumes (total ground 
reaction force impulse was 55% higher in HL group) between the groups may have impacted 
the extent of adaptations within each group. Based on our unpublished data, lower posture 
stability is evident when squatting with lower FW loads. Additionally, it is possible that a higher 
number of repetitions or sets in the LL group were needed to maximize RTD adaptations. 
Conversely, fewer than seven "all-out" repetitions could have been enough to maximize RTD 
adaptations and minimize the effect of fatigue accumulation throughout the weeks of training. 
This study included a relatively small sample and, therefore, may not have been sufficiently 
powered to detect statistically significant differences between groups. Due to the lack of studies 
analyzing RTD variables after FW training, an adequate a priori estimate of the sample size 
was not possible. Based on other studies (Aagaard et al., 2002; Oliveira et al., 2016; Šarabon et 
al., 2020), it can be reasonably speculated that a larger sample size might reveal statistically 
significant differences in adaptations between groups. Moreover, the isometric knee extension 
test has been performed due to its high reliability of results (Hernández-Davó & Sabido, 2014) 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   21 
but, there is considerable controversy regarding the external validity of the isometric 
assessment, particularly the ability of the tests to monitor changes in dynamic performance, for 
example eccentric RFD (Wilson & Murphy, 1996). We have also calculated relative and 
absolute reliability coefficients for RTD and RER variables. RTD variables revealed acceptable 
reliability across three test repetitions, while RER variables revealed unacceptable reliability 
(CV > 10%). Therefore, it could be speculated that the RER results of our study lack credibility, 
and that averaging more than three repetitions should be considered to lower the noise-to-signal 
ratio. Improving the quality of the results by averaging more than three repetitions can be 
frequently observed when analyzing evoked muscle contractions (Suter & Herzog, 2001) or 
dynamic voluntary actions (Spudić et al., 2020a), but not typically when analyzing RFD or RER 
variables (Maffiuletti et al., 2016). 
 
CONCLUSION 
In summary, FW inertial resistance training has received increasing attention in recent years 
due to the many positive effects observed on athletic performance variables such as change of 
direction (Hoyo et al., 2015), jumping (Raya-González et al., 2021), and sprinting (Sagelv et 
al., 2020). However, the underlying neural mechanisms responsible for these beneficial effects 
remain unknown. This was the first study to investigate the adaptations of explosive knee 
extension strength adaptations following FW squat resistance training with different training 
loads. Contrary to our hypothesis, no differences in RTD adaptations were found between LL 
and HL groups. Both groups significantly improved their explosive isometric knee extension 
strength during the intervention, and the improvement was accompanied by a decrease in vl and 
rf EMG signal amplitude, regardless of the training load (HL vs. LL). Based on the results, the 
improvement in RTD cannot be attributed to neural mechanisms. The results of this study may 
provide valuable data for sports practitioners using FW resistance equipment, and for 
researchers seeking to investigate the use of this technique further. 
Acknowledgment 
The Slovenian Research Agency provided author D.S. with support in the form of salary 
through the program ‘Kinesiology of monostructural, polystructural and conventional sports’ 
[P5‐0147]. The funders had no role in the design of the study; in the collection, analyses, or 
interpretation of data; in the writing of the manuscript, or in the decision to publish the results. 
The authors would like to thank the participants for their cooperation. 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   22 
Declaration of Conflicting Interests 
The author(s) declared no potential conflicts of interest with respect to the research, authorship, 
and/or publication of this article. 
 
REFERENCES 
Aagaard, P., Simonsen, E. B., Andersen, J. L., Magnusson, P., & Dyhre-Poulsen, P. (2002). Increased rate of force 
development and neural drive of human skeletal muscle following resistance training. Journal of Applied 
Physiology, 93(4), 1318–1326. https://doi.org/10.1152/japplphysiol.00283.2002 
Aagaard, P., Simonsen, E. B., Andersen, J. L., Magnusson, S. P., Halkjær-Kristensen, J., & Dyhre-Poulsen, P. 
(2000). Neural inhibition during maximal eccentric and concentric quadriceps contraction: Effects of resistance 
training. Journal of Applied Physiology, 89(6), 2249–2257. https://doi.org/10.1152/jappl.2000.89.6.2249 
Alcazar, J., Csapo, R., Ara, I., & Alegre, L. M. (2019). On the shape of the force-velocity relationship in skeletal 
muscles: The linear, the hyperbolic, and the double-hyperbolic. Frontiers in Physiology, 10, 769. 
https://doi.org/10.3389/fphys.2019.00769 
Amiridis, L. G., & et al. (1996). Co-activation & tension-regulating phenomena during isokinetic knee ext in 
sedentary & highly skilled humans. Eur. J. Appl. Physiol., 73, 149–156. 
Behm, D. G., & Sale, D. G. (1993). Intended rather than actual movement velocity determines velocity-specific 
training response. Journal of Applied Physiology, 74(1), 359–368. https://doi.org/10.1152/jappl.1993.74.1.359 
Behm, D. G. (1995). Neuromuscular Implications and Applications of Resistance Training. Journal of Strength 
and Conditioning Research, 9(4), 264–274. 
Conrad, B., Benecke, R., & Goehmann, M. (1983). Premovement silent period in fast movement initiation. 
Experimental Brain Research, 51(2), 310–313. https://doi.org/10.1007/BF00237208 
Coratella, G., Chemello, A., & Schena, F. (2016). Muscle damage and repeated bout effect induced by enhanced 
eccentric squats. The Journal of Sports Medicine and Physical Fitness, 56(12), 1540–1546. 
Cormie, P., McGuigan, M., & Newton, R. (2011). Developing Maximal Neuromuscular Power, Part 2. Sports 
Medicine, 41(2), 125–146. 
de Haan, A. (1998). Force-velocity characteristics of insitu rat medial gastrocnemius muscle. Experimental 
Physiology, 83, 77–84. 
Del Vecchio, A., Casolo, A., Dideriksen, J. L., Aagaard, P., Felici, F., Falla, D., & Farina, D. (2022). Lack of 
increased rate of force development after strength training is explained by specific neural, not muscular, motor 
unit adaptations. Journal of Applied Physiology, 132(1), 84–94. https://doi.org/10.1152/japplphysiol.00218.2021 
Del Vecchio, A., Casolo, A., Negro, F., Scorcelletti, M., Bazzucchi, I., Enoka, R., Felici, F., & Farina, D. (2019). 
The increase in muscle force after 4 weeks of strength training is mediated by adaptations in motor unit recruitment 
and rate coding. Journal of Physiology, 597(7), 1873–1887. https://doi.org/10.1113/JP277250 
Duchateau, J., & Baudry, S. (2014). Maximal discharge rate of motor units determines the maximal rate of force 
development during ballistic contractions in human. Frontiers in Human Neuroscience, 8, 234. 
https://doi.org/10.3389/fnhum.2014.00234 
Elgueta-cancino, E., Evans, E., Martinez-valdes, E., & Falla, D. (2022). The Effect of Resistance Training on 
Motor Unit Firing Properties : A Systematic Review and Meta-Analysis. February. 
https://doi.org/10.3389/fphys.2022.817631 
Hermens, H. J., Freriks, B., Disselhorst-Klug, C., & Rau, G. (2000). Development of recommendations for SEMG 
sensors and sensor placement procedures. Journal of Electromyography and Kinesiology, 10(5), 361–374. 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   23 
Hernández-Davó, J. L., & Sabido, R. (2014). Rate of force development; Reliability, improvements and influence 
on performance. European Journal of Human Movement, 33(December), 46–69. 
Hopkins, W. G. (2000). Measures of Reliability in Sports Medicine and Science. Sports Medicine, 30(1), 1–15. 
https://doi.org/10.2165/00007256-200030010-00001. 
Hoyo, M., Sañudo, B., Carrasco, L., Domínguez-Cobo, S., Mateo-Cortes, J., Cadenas-Sánchez, M. M., & 
Nimphius, S. (2015). Effects of Traditional Versus Horizontal Inertial Flywheel Power Training on Common 
Sport-Related Tasks. Journal of Human Kinetics, 47(1), 155–167. https://doi.org/10.1515/hukin-2015-0071 
Kamen, G., & Knight, C. A. (2004). Training-related adaptations in motor unit discharge rate in young and older 
adults. Journals of Gerontology - Series A Biological Sciences and Medical Sciences, 59(12), 1334–1338. 
https://doi.org/10.1093/gerona/59.12.1334 
Koo, T. K., & Li, M. Y. (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for 
Reliability Research. Journal of Chiropractic Medicine, 15(2), 155–163. 
https://doi.org/10.1016/j.jcm.2016.02.012 
Kotrlik, J. W., & Williams, H. A. (2003). The Incorporation of Effect Size. Information Technology, Learning, 
and Performance Journal, 21(1), 1–7. 
Maffiuletti, N. A., Aagaard, P., Blazevich, A. J., Folland, J., Tillin, N., & Duchateau, J. (2016). Rate of force 
development: physiological and methodological considerations. European Journal of Applied Physiology, 116(6), 
109–1116. https://doi.org/10.1007/s00421-016-3346-6 
Mangine, G. T., Hoffman, J. R., Wang, R., Gonzalez, A. M., Townsend, J. R., Wells, A. J., Jajtner, A. R., Beyer, 
K. S., Boone, C. H., Miramonti, A. A., LaMonica, M. B., Fukuda, D. H., Ratamess, N. A., & Stout, J. R. (2016). 
Resistance training intensity and volume affect changes in rate of force development in resistance-trained men. 
European Journal of Applied Physiology, 116(11–12), 2367–2374. https://doi.org/10.1007/s00421-016-3488-6 
Martinez-Aranda, L. M., & Fernandez-Gonzalo, R. (2017). Effects of inertial setting on power, force, work, and 
eccentric overload during flywheel resistance exercise in women and men. Journal of Strength and Conditioning 
Research, 31(6), 1653–1661. https://doi.org/10.1519/JSC.0000000000001635 
McErlain-Naylor, S. A., & Beato, M. (2021). Concentric and eccentric inertia–velocity and inertia–power 
relationships in the flywheel squat. Journal of Sports Sciences, 39(10), 1136–1143. 
https://doi.org/10.1080/02640414.2020.1860472 
Nosaka, K., Sakamoto, K. E. I., Newton, M., & Sacco, P. (2001). How long does the protective effect on eccentric 
exercise-induced muscle damage last? MEDICINE & SCIENCE IN SPORTS & EXERCISE, 33, 1490–1495. 
Oliveira, A. S., Corvino, R. B., Caputo, F., Aagaard, P., & Denadai, B. S. (2016). Effects of fast-velocity eccentric 
resistance training on early and late rate of force development. European Journal of Sport Science, 16(2), 199–
205. https://doi.org/10.1080/17461391.2015.1010593 
Peñailillo, L., Blazevich, A., Numazawa, H., & Nosaka, K. (2015). Rate of force development as a measure of 
muscle damage. Scandinavian Journal of Medicine and Science in Sports, 25(3), 417–427. 
https://doi.org/10.1111/sms.12241 
Raya-González, J., Castillo, D., de Keijzer, K. L., & Beato, M. (2021). The effect of a weekly flywheel resistance 
training session on elite U-16 soccer players’ physical performance during the competitive season. A randomized 
controlled trial [published online ahead of print, 2021 Jan 5]. Research in Sports Medicine, 00(00), 1–15. 
https://doi.org/10.1080/15438627.2020.1870978 
Ricard, M. D., Ugrinowitsch, C., Parcell, A. C., Hilton, S., Rubley, M. D., Sawyer, R., & Poole, C. R. (2005). 
Effects of rate of force development on EMG amplitude and frequency. International Journal of Sports Medicine, 
26(1), 66–70. https://doi.org/10.1055/s-2004-817856 
Rodriguez-Lopez, C., Alcazar, J., Sanchez-Martin, C., Baltasar-Fernandez, I., Ara, I., Csapo, R., & Alegre, L. M. 
(2022). Neuromuscular adaptations after 12 weeks of light- vs. heavy-load power-oriented resistance training in 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   24 
older adults. Scandinavian Journal of Medicine and Science in Sports, 32(2), 324–337. 
https://doi.org/10.1111/sms.14073 
Rodríguez-Rosell, D., Pareja-Blanco, F., Aagaard, P., & González-Badillo, J. J. (2018). Physiological and 
methodological aspects of rate of force development assessment in human skeletal muscle. Clinical Physiology 
and Functional Imaging, 38(5), 743–762. https://doi.org/10.1111/cpf.12495 
Sabido, R., Hernández-davó, J. L., Capdepon, L., & Tous-fajardo, J. (2020). How Are Mechanical, Physiological, 
and Perceptual Variables Affected by the Rest Interval Between Sets During a Flywheel Resistance Session? 
Frontiers in Physiology, 11(663), 1–8. https://doi.org/10.3389/fphys.2020.00663 
Sagelv, E. H., Pedersen, S., Nilsen, L. P. R., Casolo, A., Welde, B., Randers, M. B., & Pettersen, S. A. (2020). 
Flywheel squats versus free weight high load squats for improving high velocity movements in football. A 
randomized controlled trial. BMC Sports Science, Medicine and Rehabilitation, 12(1), 1–14. 
https://doi.org/10.1186/s13102-020-00210-y 
Sale, D. G. (1988). Neural adaptation to resistance training. Medicine and Science in Sports and Exercise, 20(5), 
135–145. 
Samozino, P., Morin, J. B., Hintzy, F. F., & Belli, A. (2010). Jumping ability: A theoretical integrative approach. 
Journal of Theoretical Biology, 264(1), 11–18. https://doi.org/10.1016/j.jtbi.2010.01.021 
Šarabon, N., Čeh, T., Kozinc, Ž., & Smajla, D. (2020). Adapted protocol of rate of force development and 
relaxation scaling factor for neuromuscular assessment in patients with knee osteoarthritis. Knee, 27(6), 1697–
1707. https://doi.org/10.1016/j.knee.2020.09.023 
Sousa, A. S. P., & Tavares, J. M. R. S. (2012). Surface electromyographic amplitude normalization methods: A 
review. In H. Takada (Ed.), Electromyography: New Developments, Procedures, and Applications. Nova Science 
Publishers, Inc. https://repositorio-aberto.up.pt/bitstream/10216/64430/2/67854.pdf 
Spudić, D., Cvitkovič, R., & Šarabon, N. (2021). Assessment and Evaluation of Force – Velocity Variables in 
Flywheel Squats: Validity and Reliability of Force Plates, A Linear Encoder Sensor, and A Rotary Encoder Sensor. 
Appl. Sci., 11(22), 10541. 
Spudić, D., Smajla, D., & Šarabon, N. (2020a). Intra-session reliability of electromyographic measurements in 
flywheel squats. PLoS ONE, 15(2), e0243090. https://doi.org/10.1371/journal.pone.0243090 
Spudić, D., Smajla, D., & Šarabon, N. (2020b). Validity and reliability of force–velocity outcome parameters in 
flywheel squats. Journal of Biomechanics, 107, 109824. 
https://doi.org/https://doi.org/10.1016/j.jbiomech.2020.109824 
Strojnik, V., & Komi, P. V. (1998). Neuromuscular fatigue after maximal stretch-shortening cycle exercise. 
Journal of Applied Physiology, 84(1), 344–350. https://doi.org/10.1152/jappl.1998.84.1.344 
Suter, E., & Herzog, W. (2001). Effect of number of stimuli and timing of twitch application on variability in 
interpolated twitch torque. Journal of Applied Physiology, 90(3), 1036–1040. 
https://doi.org/10.1152/jappl.2001.90.3.1036 
Tesch, P. A., Ekberg, A., Lindquist, D., & Trieschmann, J. (2004). Muscle hypertrophy following 5-week 
resistance training using a non-gravity-dependent exercise system. Acta Physiologica Scandinavica, 180(1), 89–
98. 
Van Cutsem, M., & Duchateau, J. (2005). Preceding muscle activity influences motor unit discharge and rate of 
torque development during ballistic contractions in humans. Journal of Physiology, 562(2), 635–644. 
https://doi.org/10.1113/jphysiol.2004.074567 
Viitasalo, J. T. (1982). Effects of pretension on isometric force production. International Journal of Sports 
Medicine, 3(3), 149–152. https://doi.org/10.1055/s-2008-1026079 
Walter, C. B. (1989). Voluntary control of agonist premotor silence preceding limb movements of maximal effort. 
Perceptual and Motor Skills, 69, 819–826. 

Kinesiologia Slovenica, 29, 3, 5-25 (2023), ISSN 1318-2269   Flywheel Resistance Training   25 
Warren, G. L., Ingalls, C. P., Lowe, D. A., & Armstrong, R. B. (2002). What mechanisms contribute to the strength 
loss that occurs during and in the recovery from skeletal muscle injury? Journal of Orthopaedic and Sports 
Physical Therapy, 32(2), 58–64. https://doi.org/10.2519/jospt.2002.32.2.58 
Weng, Y., Liu, H., Ruan, T., Yang, W., Wei, H., Cui, Y., Ho, I. M. K., & Li, Q. (2022). Effects of flywheel 
resistance training on the running economy of young male well-trained distance runners. Frontiers in Physiology, 
13(December), 1–10. https://doi.org/10.3389/fphys.2022.1060640 
Wilson, G. J., & Murphy, A. J. (1996). The use of isometric tests of muscular function in athletic assessment. 
Sports Medicine, 22(1), 19–37. https://doi.org/10.2165/00007256-199622010-00003