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The effect of individualised sprint training in elite
female team sport athletes: A pilot study
Individualised sprint training
Elvir Rakovic1, Gøran Paulsen2, Christian Helland2, Ola Eriksrud3, Thomas Haugen2
1) Department of Food and Nutrition, and Sport Science, University of Gothenburg,
Sweden
2) Norwegian Olympic Federation, Oslo, Norway
3) Department of Physical Performance, Norwegian School of Sport Sciences, Oslo,
Norway
Keywords: physical training; physical performance; acceleration; horizontal power
production, handball.
Abstract
This study aimed to evaluate whether an individualised sprint-training program was
more effective in improving sprint performance in elite team-sport players compared
to a generalised sprint-training program. Seventeen elite female handball players (23
3 y, 177 7 cm, 73 6 kg) performed two weekly sprint training sessions over eight
weeks in addition to their regular handball practice. An individualised training group
(ITG, n = 9) performed a targeted sprint-training program based on their horizontal
force-velocity profile from the pre-training test. Within ITG, players displaying the
lowest, highest and mid-level force-velocity slope values relative to body mass were
assigned to a resisted, an assisted or a mixed sprint-training program (resisted sprinting
in the first half and assisted sprinting in the second half of the intervention period),
respectively. A control group (CG, n = 8) performed a generalised sprint-training
program. Both groups improved 30-m sprint performance by ~ 1% (small effect) and
maximal velocity sprinting by ~ 2% (moderate effect). Trivial or small effect
magnitudes were observed for mechanical outputs related to horizontal force- or power
production. All between-group differences were trivial. In conclusion, individualised
sprint-training was no more effective in improving sprint performance than a
generalised sprint-training program.
Introduction
Accelerated sprinting is a fundamental part of the motor skill requirements in team
sports to win duels, defend or create goal-scoring opportunities. Sprint performance
becomes more resistant to training enhancement with increasing performance level,
age and training status (Vescovi, Rupf, Brown, & Marques, 2011; Haugen, Tønnessen,
& Seiler, 2012 and 2013; Tønnessen, Svendsen, Olsen, Guttormsen, & Haugen, 2015).
However, previous studies have shown that professional players are generally faster
than semi-professional and amateur players, and professional players have become
faster over time, indicating that the importance of well-developed sprinting skills has
increased in modern team sports (Haugen et al., 2012 and 2013; Haugen, Tønnessen,
Hisdal, & Seiler, 2014). Previously published intervention studies have typically been
performed on young and/or amateur players and limited to investigating whether
certain training methods are more effective than others. Although the principle of
specificity is clearly present, assisted or resisted sprint training have so far not provided
superior effects on accelerated sprinting capability in team sport players compared to
sprinting under normal conditions (Haugen et al., 2014; Petrakos, Morin, & Egan,
2016; Rumpf, Lockie, Cronin, & Jalilvand, 2016).
An increasing number of studies pay attention to underlying mechanical determinants
for sprint performance, as such variables provide insights into individual
biomechanical limitations (Morin et al., 2012; Buchheit et al., 2014; Rabita et al.,
2015). Recently, a French research group presented a field method to calculate
mechanical outputs and develop horizontal profiles of accelerated sprinting (Samozino
et al., 2016; Morin & Samozino, 2016). Theoretical maximal velocity (V0), horizontal
force (F0), horizontal power (P0) and force-velocity profile can be calculated from the
modelling by derivation of the speed-time curve that leads to horizontal acceleration
data. The promising aspect of this approach is an individualised diagnosing and
development of training programs that target the major limiting factors (Morin &
Samozino, 2016). It has recently been reported that an individualised training program
based on vertical force-velocity profiling was more effective in improving jumping
performance than traditional strength/power training common to all participants
(Jiménez-Reyes, Samozino, Brughelli, & Morin, 2017A; Jiménez-Reyes et al., 2017B).
A similar approach based on horizontal force-velocity profiling remains to be explored
for sprint running performance purposes. This can be achieved by comparing the
relative strengths and weaknesses in each player’s profile to the rest of the team (Morin
& Samozino, 2016). Accordingly, athletes with horizontal force deficits should be
given more horizontal strength work (e.g., resisted sprint), while athletes with velocity
deficits should prioritize maximal velocity sprinting (e.g., assisted sprinting).
Therefore, the aim of the current study was to evaluate whether an individualised
training program based on horizontal force-velocity profiling was more effective on
accelerated and maximal velocity sprinting performance in elite team sport players
compared to a generalised sprint-training program. We hypothesised that
individualised sprint training would provide better effects on accelerated and maximal
velocity sprinting performance.
Methods
Design
In this randomised controlled trial, participants (n=21) were allocated pairwise
according to their horizontal force-velocity profile (force-velocity slope relative to
body mass) from pre-training tests and then randomly assigned to one of two treatment
conditions. The randomisation process was performed by a co-author not directly
involved in testing or the training intervention. The individualised training group (ITG,
n=11) performed a targeted and individualised sprint-training program, while the
control group (CG, n=10) performed a generalised sprint-training program that was
the same for all the participants. Three subgroups within ITG were established. Here,
the players displaying the lowest, highest and mid-level force-velocity slope values
relative to body mass were assigned to a resisted (ITG1 = 3), an assisted (overspeed)
(ITG2 = 4) and a mixed sprint-training program (ITG3 = 4) (resisted sprinting in the
first half and assisted sprinting in the second half of the intervention period),
respectively (Figure 1). The intervention included sprint training twice a week for an
8-week period for both groups. Participants were required to complete at least 14 out
of 16 intervention-training sessions (87.5%) and all pre- and post-training tests in order
to be included. Both ITG and CG completed, on average, 93% of the total sprint
training sessions. Session rating of perceived exertion (session RPE) and perceived
recovery status (PRS) were registered throughout the intervention period based on
previously published guidelines (Foster, 2001; Laurent et al., 2011).
***Figure 1 about here***
Participants
Twenty-one professional or semi-professional female handball players in the national
upper league volunteered to participate and underwent the pre-training tests. Four
players dropped out immediately prior to or during the intervention, including one
(from CG) who sustained a hamstring injury during one of the sprint training sessions.
Overall, 17 participants completed the study with the following sample sizes: ITG = 9
(age 23 3 y, height 177 7 cm, body mass 73 6 kg) and CG = 8 (age 23 3 y,
height 176 6 cm, body mass 72 5 kg). Training characteristics for both groups are
presented in Table 1.
***Table 1 about here***
Each participant had a minimum of 10 years of handball-specific training experience.
Four of the participants played for the national team while eleven players participated
in the Champions League tournament during the current season. During the
intervention period, participants were requested to refrain from performing any other
heavy and/or high intensity off-field physical training regimes in the form of maximum
strength training, high-intensity interval running or plyometric training. Regular
handball training sessions typically commenced with warm-up activities like running
in different directions and specific warm-up for upper and lower extremities, followed
by progressive passing drills and goalkeeper warm-up. The main part of the handball
practices during this period consisted of tactical-oriented and match-preparing sessions
with low to moderate intensity.
The study was reviewed by the Regional Ethics Committee and approved by the
Norwegian Data Protection Authority. All subjects signed an informed consent form
before the study and were made aware that they could withdraw at any point without
providing an explanation. The study was conducted in accordance with the Declaration
of Helsinki.
Testing procedures
The pre- and post-training tests were conducted in the same handball arena. All
participants completed the tests in the same order and at the same time of day.
Regarding nutrition, hydration, sleep and physical activity, participants were
instructed to prepare as they would for a regular handball match, including no high-
intensity training the last day prior to testing. They were also instructed to use identical
footwear and kit for each of the tests. All participants were familiarised with sprint
testing. Body mass was assessed half an hour prior to testing on each testing day.
Participants then completed a 20 min standardised warm-up consisting of a general
warm-up (jogging at ~60-75% of age-predicted maximal heart rate), ”local” muscle
warm-up (lunges, hip lift, ballistic hamstring- and hip mobility in supine and prone),
specific running drills (high knees skipping, butt-kicks, straight leg pulls) and finally
3-4 runs over 30-40 m with progressively increasing speed.
After the warm-up, participants completed two maximal 30-m sprints. Best 30-m
time was included for analysis. Recovery time between trials was 3-4 min. All sprints
were commenced from a standing split stance position with the toe of the front foot
placed at the start line. After a ready signal was given by the test operator, athletes
started on their own initiative. Musclelab (Ergotest AS, Porsgrunn, Norway) timing
system was used for sprint performance assessments. An infrared contact mat
covered the start line. Timing was initiated by the infrared contact at the time of front
foot lift-off. Post-processing timing gates were placed at 5,10,15,20 and 30 m (120
cm above floor level), and the start of the longest photocell break was used as a
trigger criterion (the torso will always produce a longer break than an arm). The
present timing setup provided sufficient data points for mechanical output
computations (Samozino et al., 2016; Morin & Samozino, 2016) performed by a
purpose-built software integrated in the Musclelab system. Typical error (TE) and
coefficient of variation (CV) were 0.03 s and 1.0% for 0-30 m sprint time, 0.08 m∙s-1
and 1.4% for V0, 20 W and 2.6% for P0, 0.30 W∙kg-1 and 2.7% for P0∙kg-1, 10 N and
2.7% for F0, 0.14 N∙kg-1 and 2.7% for F0∙kg-1, and 1.7 (N∙(m∙s-1)-1 and 3.4% for FV
slope, based on sprint trial 1 and 2 from the pre-training tests.
Intervention
The sprint training intervention took place from the middle of January to the middle
of March, corresponding to the late middle of the handball season for the participants.
All sprint-training sessions were supervised and completed at the same time of day for
both groups during the entire intervention. There was a minimum of 48 h between each
sprint-training session. Identical warm-up procedures as for the pre-and post-training
tests were performed prior to each sprint training. The intervention protocol was
periodised with a gradual increase in the number of weekly-performed sprints during
the first half of the intervention, followed by a corresponding decrease in sprint
repetitions (for tapering purposes) the last three weeks prior to the post-training test
(Table 2). Each sprint training session followed a stepwise change (increase/decrease)
in resistance/assistance, to ensure a gradual and smooth progression. The number of
sprints was equal for all participants during all sprint-training sessions, and recovery
between each sprint was 3-4 min. The players were encouraged to perform all sprints
with maximal effort.
***Table 2 about here***
CG performed 30-m sprints (sprinting under normal conditions, no assistance or
resistance) during the entire intervention. 1080 Sprint (1080 Motion AB, Stockholm,
Sweden), a portable resistance/overspeed training device that uses a servo motor (2000
RPM OMRON G5 Series Motor, OMRON Corporation, Kyoto, Japan), was used by
ITG during all sprint sessions. The cord from the motor was attached to the sprinting
athlete with a belt around the waist. The resistance/assistance load (Table 2) was
determined and controlled by the Quantum computer application (1080 Motion,
Lidingö, Sweden). Gear 1 and isotonic resistance mode were used for the winch
system. For the resisted 30-m sprints, the players started 5 m in front of and ran away
from the machine. The variable resistance mode was used the last three weeks (i.e., the
tapering phase) for ITG1 and in two training sessions in the middle of the intervention
for ITG3 to ensure a smooth transition from resisted to assisted sprinting. In this mode,
the resistance drops linearly from 9 kg at start to 1 kg when achieving a certain speed
(corresponding to each individual´s documented peak velocity at running with 9 kg
resistance, assessed by the 1080 device). For the assisted 25-m sprints, the subjects
started 45 m in front and ran towards the machine. The assisted sprints were slightly
shorter to ensure sufficient braking distance. During the assisted sprints, participants
were advised to focus on high step frequency when they approached their maximal
velocity, as previously recommended (Mero & Komi, 1986; Cissik, 2005). No other
technical instructions were provided. Overall, sprinting with 5, 8 and 11 kg resistance
induced 11, 18 and 25% reduction in maximal sprint velocity on average, based on
assessments of the sprint training sessions. Similarly, sprinting with 0.3, 1.3, 2.2 and
3.2 kg assistance induced 1, 6, 11 and 14% higher maximal velocity. All the stated
resistance/assistance values are averaged over the entire step cycle. The variability for
each assistance/resistance load was very low (CV < 1%, calculated from 201 runs),
indicating high reliability.
Statistics
Shapiro Wilks tests revealed that none of the variables deviated statistically from
distribution of normality. Data from pre- and post-training tests are presented as mean
±SD. Magnitudes of between-group differences were assessed by standardisation
(mean difference divided by the harmonic mean of the SD of the compared groups).
The thresholds for assessing the observed difference in means were 0.2, 0.6 and 1.2
for small, moderate and large, respectively (Hopkins, Marshall, Batterham, & Hanin,
2009). To make inferences about true values of effects, we used non-clinical
magnitude-based inference rather than null-hypothesis significance testing (Hopkins
et al., 2009). Magnitudes were evaluated mechanistically: if the confidence interval
overlapped substantial positive and negative values, the effect was deemed unclear;
otherwise effects were deemed clear and shown with the probability that the true effect
was substantial or trivial (whichever was greater) using the following scale: 25-75%,
possibly; 75-95%, likely; 95-99.5%, very likely; > 99.5%, most likely (Hopkins et al.,
2009).
Results
***Table 3 about here***
Sprint performance and mechanical outputs between and within groups from pre- to
post-training test are shown in Table 3. Both groups improved their 30-m sprint
performance by 0.05-0.06 s on average (~ 1%; small effect). Both groups improved
V0 by ~ 2% (moderate effect), while only trivial or small effect magnitudes were
observed for the other mechanical outputs. All between-group differences observed
from pre- to post-training test were trivial and unclear.
***Figure 2 about here***
Figure 2 shows the changes in 30-m sprint time and V0 from pre- to post-training tests
on an individual level. No clear trends between treatment conditions and performance
enhancements were observed.
Discussion
To the authors’ knowledge, this is the first study to evaluate the effect of an
individualised sprint-training program based on horizontal force-velocity profiling.
Our main finding was that the individualised training was no more effective than a
generalised sprint training program (control) in elite female handball players. Hence,
all between-group differences were trivial. Both the individualised training group and
the control group displayed moderate improvements in maximal velocity (V0) and
small enhancements in 0-30 m sprint times. Only trivial or small effect magnitudes
were observed for variables related to horizontal force- and power production within
both groups.
Individualised training is generally more challenging to organise (i.e., time
consuming) for team-sport staff than common training sessions where “one size fits
all.” Consequently, many coaches perform similar training for most players on the
team, despite considerable potential variances in capacity profiles. Interestingly, even
though applying individualised training is theoretically and scientifically sound
(Haugen et al., 2014; Morin & Samozino, 2016; Jiménez-Reyes et al., 2017A), the lack
of substantial between-group differences observed in the present study do not support
individualised sprint training. However, several considerations must be taken into
account and discussed in order to avoid a potential type II error conclusion.
When optimally evaluating an intervention, it is important to consider (i) the actual
change in performance (the signal), (ii) the noise associated with that particular
assessment, and (iii) the smallest practical or meaningful change (SWC) (Hopkins,
2004). SWC for team sport athletes is 1% for 10- to 40-m sprints and 2% for maximal
velocity sprinting (Haugen & Buchheit, 2016). Considering that the actual change in
performance (~ 1% for both groups over 30-m sprint) was practically identical with
the measurement noise observed (1% CV for 0-30-m sprint time) and SWC for team
sport athletes, the usefulness of the sprint training programs performed by both groups
was relatively poor. However, we did observe large variations in individual responses
(Figure 2). Ten out of 17 athletes (five from ITG and five from CG) improved their
30-m times by more than 1% (SWC), indicating that the intervention was useful for
these players, while three athletes (all from ITG) worsened their 30-m sprint times
correspondingly by more than 1%. Similarly, nine players (five from ITG and 4 from
CG) displayed advances in V0 greater than 2% (SWC), while one player decreased V0
by more than 2%. The reasons for these variations remain unclear. No meaningful
differences were observed between the groups in terms of sprint performance level,
total training- or match-load characteristics during the intervention period. Moreover,
a visual inspection of the present individual results revealed no clear trends in favour
of any playing position.
Both groups displayed larger enhancements for maximal velocity sprinting than for
accelerated sprint performance. Similar to our findings, Tønnessen, Shalfawi, Haugen,
& Enoksen (2011) observed unaltered accelerated sprint performance and improved
maximal velocity as a result of weekly repeated 40-m sprints in young male elite soccer
players. A recent review by Rumpf et al. (2016) showed that training effects (in terms
of effect size) increased with increasing sprint distance. Collectively, this suggests that
team sport players respond most strongly to somewhat longer and less team-sport
specific sprint distances. Indeed, team sport players perform a high number of brief
accelerations (~ 5-10 m) during training and games, while longer sprints (> 30 m)
rarely occur (Vigne, Gaudino, Rogowski, Alloatti, & Hautier, 2010; Michalsik,
Madsen, & Aagaard, 2014; Suarez-Arrones et al., 2014). Therefore, we speculate that
most well-trained players have largely maximized their accelerated sprint performance
potential (at least when compared to maximal velocity sprinting) during regular team-
sport training.
Performance in sprint is determined by a complex interaction of technical and
physiological variables (Morin, Edouard, & Samozino, 2011; Haugen et al., 2017A;
Haugen, Paulsen, Seiler, & Sandbakk, 2017B). In the context of this study, it is
important to keep in mind that ineffective sprinting (e.g., too much upper body raise
during initial acceleration) may influence the mechanical outputs. That is, horizontal
force- and power production may be underestimated for powerful athletes with poor
running technique. Morin et al. (2011) have developed a model to calculate ratio of
force and force application technique, but these computations require force data from
instrumented treadmills or multiple force plates in series, equipment that the vast
majority of athletes do not have access to.
The categorisation criteria that formed the basis for the present individualised sprint
training need to be further discussed. Recently, Jimenez-Reyes et al. (2017) performed
an intervention with a similar approach to enhance vertical jump performance, and
their allocation to the different training protocols was based on percentage deviation
from the theoretically optimal FV profile. As no such reference values exist for
horizontal sprinting, a relative allocation model was chosen for the present study. Due
to the strong relationship between FV slope and body mass (we observed a 0.80
correlation between these variables based on pre-training tests), it is crucial to
normalise FV slope to body mass prior to group allocation, as performed in this study.
However, it remains unclear whether the participants conducted an optimal training
protocol based on the principle of targeting their least developed capacity (e.g. force-
deficit or velocity-deficit). Morin & Samozino (2016) suggested that individual
training programs should be based on comparisons of the relative strengths and
weaknesses in each player’s horizontal profile compared to the rest of the team.
However, a limitation of using this approach is that it is directly affected by group
homogeneity. Theoretically, the players included in this study might be clustered
around a smaller part of the entire spectrum of mechanical sprint running profiles,
leading to the possibility that the prescribed individualised training was too
differentiated.
Due to the varying natures and specificities across team sports, the importance of
sprint-specific mechanical outputs will vary. Giroux, Rabita, Chollet, & Guilhem
(2016) observed that the chronic practice of an activity leads to differently balanced
force-velocity profiles in squat jumping. Further research should therefore aim to
establish the requirements of sprint-specific mechanical outputs across a broad range
of sports disciplines and playing positions in order to provide a holistic picture of the
capacity profile continuum. Differences in force-velocity profiles raise potential
sources of performance improvement in elite athletes. As such, it is reasonable to
assume that the effect of individualised training increases with athlete heterogeneity.
Despite some potential methodological weaknesses associated with the current
individualisation of sprint training, no indications in favour of either resisted or
assisted sprint training were observed (Figure 2). The hypothesis behind assisted sprint
running is that supramaximal sprinting can lead to higher stride-frequency, shorter
ground contact times and higher angle velocities (Cissik, 2005). Comparisons of
assisted sprint-training protocols across studies are even more challenging than for
resisted sprinting, due to fewer scientific publications and even greater variations in
methods and devices (e.g., downhill running, treadmill, elastic cord devices, etc.).
Clark et al. (2009) suggested that a towing load corresponding to ~4% of body weight
decreases ground contact times without any negative effects on other kinematic
parameters. In the present study, pulling forces in the range 0.3-3.2 kg (i.e., 0.7-4.4%
of mean body mass) were used, inducing 1-14% increase in maximal sprint velocity.
Because no kinematic recordings of test-runs were performed, the possible influence
of the overspeed load on sprint kinematics remains unclear.
The horizontal resistances applied in the current study were 5, 8 and 11 kg, leading to
a reduction in maximal velocity in the range 11-25%. According to the classifications
outlined by Petrakos et al. (2015), this reduction in running velocity corresponds to
moderate to heavy resistance. According to Cross, Brughelli, Samozino, Brown, &
Morin (2017), the optimal loading for maximising power during sled-resisted sprinting
is a resistance that reduces the maximal velocity by ⁓50%. However, Morin et al.
(2016) tested the use of very heavy sleds and observed a substantial, increased
horizontal force production when compared to non-resisted sprinting. Still, only trivial
between-group differences were observed for power output and sprint velocity. Future
studies should therefore investigate the effect of heavier or lighter loads after
individualisation of force-velocity profiles.
Intervention studies involving high-level athletes are typically shaped by training-
related constraints within the overall training program. Such constraints are an
important aspect of assessing the practical efficacy of training interventions in team
sports. This intervention was performed in-season, and it is possible that the results
would have been different if the study was undertaken off-season or pre-season.
However, the present results add further support to the notion that sprinting skills over
short distances are hard to improve within the constraints of overall team sport training
(Tønnessen et al., 2011 and 2015; Haugen et al., 2015, Los Arcos & Martins, 2018).
If the primary goal for well-trained players is to improve their sprinting skills, future
investigations should explore whether it is more effective to restructure the players`
weekly team sport training rather than introducing an additional physical training
regime.
Considering both the present findings and previous research (Haugen et al., 2014;
Petrakos et al., 2016; Rumpf et al., 2016), no specific sprint training methods have so
far emerged as superior. However, there are many parameters left that need to be
explored within the individualised FV-profile approach (e.g., other volume/load,
proportions of assisted/resisted sprinting relative to normal sprinting, categorisation
criteria for FV-profiling of athletes, sprint training at other season times, etc.).
Therefore, the current findings must be interpreted with caution.
Conclusion
In the present study, elite female handball players were followed over 8 weeks in
season. An individualised sprint-training program, based on horizontal force-velocity
profiling, was found to be no more effective than a generalised sprint-training program
in improving accelerated and maximal velocity sprinting performance. The moderate
sample sizes may mask possible significant outcomes within the groups, but based on
the trivial or small effect magnitudes observed, it is not likely that larger sample sizes
would provide significant between-group differences. However, several other
considerations must be taken into account and addressed in future studies before the
hypothesis can be rejected, the most important being the development of sport-specific
categorisation criteria based on FV-profiling of athletes. Although the present
investigation must be considered a pilot, it provides a point of departure for future
studies.
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Table 1. Weekly training characteristics for the participants during the intervention
period
Specific
(n)
Non-specific
(n)
Games per
week (n)
Total training
volume (h·w-1)
Session
RPE
PRS
ITG
3.1±0.5
1.0±0.2
1.3±0.3
9.3±0.7
5.6±0.7
6.2±0.5
CG
2.7±0.4
1.1±0.4
1.4±0.3
8.8±0.8
5.8±0.5
6.7±1.1
Values are mean ± SD. Specific = handball-specific training on court. Non-specific =
non-specific handball training off-court (e.g., upper-body work, core stability,
recovery training, etc.). Session RPE = session rated perceived exertion. PRS =
perceived recovery status. All between-group differences were small or moderate.
Table 2. Sprint training intervention protocol
Sprint
session
n
Resistance (kg) during 30-m
sprints for ITG1
Assistance (kg) during 25-m sprints for
ITG2
Resistance (session 1-8) and assistance
(session 9-16) for ITG3 (kg)
1
6
5-5-8-11-8-5
0-0-0.3-1.3-0.3-0
5-5-8-11-8-5
2
6
0-5-8-11-8-5
0-0-0.3-1.3-0.3-0.3
0-5-8-11-8-5
3
7
0-5-5-8-11-8-5
0-0.3-0.3-1.3-0.3-1.3-0.3
0-5-5-8-11-8-5
4
8
5-5-8-8-11-11-8-5
0-0-0.3-0.3-1.3-2.2-1.3-0.3
5-5-8-8-11-11-8-5
5
10
5-5-8-8-8-11-11-8-8-5
0-0-0.3-0.3-1.3-1.3-2.2-3.2-2.2-0
5-5-8-8-8-11-11-8-8-5
6
8
0-5-8-8-11-11-8-5
0-0-0.3-1.3-2.2-1.3-0.3-0
0-5-8-8-11-11-8-5
7
11
5-5-8-8-8-11-11-8-8-8-5
0-0-0.3-0.3-1.3-1.3-2.2-2.2-3.2-2.2-0
0-5-8-8-8-8-11-11-11-11-11
8
8
0-5-8-8-11-11-8-5
0-0-0.3-1.3-2.2-1.3-0.3-0
0-5-8-8-11-11-11-11
9
12
5-5-8-8-8-11-11-11-8-8-8-5
0-0-0.3-0.3-1.3-1.3-2.2-2.2-3.2-2.2-1.3-0
0-0-0-0.3-0.3-1.3-1.3-2.2-1.3-0-0-0
10
8
0-5-8-8-11-11-8-5
0-0-0.3-1.3-2.2-1.3-0.3-0
0-0-0.3-1.3-2.2-1.3-0.3-0
11
12
0-5-8-8-8-11-11-11-11-11-11-11
0-0-0.3-0.3-1.3-1.3-2.2-2.2-3.2-2.2-1.3-0
0-0-0.3-0.3-1.3-1.3-2.2-3.2-2.2-1.3-0-0
12
8
0-0-5-8-11-11-11-11
0-0-0.3-1.3-2.2-1.3-0.3-0
0-0-0.3-1.3-2.2-1.3-0.3-0
13
10
0-0-5-8-8-11-11-11-11-11
0-0-0.3-1.3-2.2-3.2-3.2-2.2-1.3-0
0-0-0.3-1.3-2.2-3.2-3.2-2.2-1.3-0
14
8
0-0-5-5-8-11-11-11
0-0-0.3-1.3-2.2-3.2-2.2-0
0-0-0.3-1.3-2.2-3.2-2.2-0
15
8
0-0-0-5-8-11-11-11
0-0-0.3-1.3-2.2-3.2-2.2-0
0-0-0.3-1.3-2.2-3.2-2.2-0
16
6
0-0-0-0-5-11
0-0-0.3-1.3-0-0
0-0-0.3-1.3-0-0
n = sprint repetitions (for CG and ITG). Underlined numbers denote sprints with variable resistance (linearly falling from 11 to 3 kg at a
running velocity that corresponds to an individual peak velocity with 11 kg resistance). All stated resistance/assistance values are averaged
over the entire step cycle.
Table 3. Sprint performance and mechanical outputs within and between groups from pre- to post-training test.
Variable
Individualised training group (ITG)
Control group (CG)
Between-group difference
Pre
Post
Δ
Pre
Post
Δ
Mean, ±90%CL; effect
0-30 m sprint (s)
4.38 ± 0.17
4.33 ± 0.09
-0.05 ± 0.11
4.40 ± 0.11
4.35 ± 0.11
-0.05 ± 0.05
-0.01, ±0.04; trivial
V0 (m∙s-1)
8.1 ± 0.4
8.2 ± 0.3
0.10 ± 0.3
8.0 ± 0.3
8.2 ± 0.3
0.2 ± 0.1
0.0, ±0.1; trivial
P0 (W)
1076 ± 122
1100 ± 72
24 ± 70
1050 ± 92
1061 ± 117
11 ± 43
16, ±35; trivial
P0∙kg-1 (W∙kg-1)
14.7 ± 1.5
15.0 ± 0.9
0.3 ± 0.9
14.6 ± 0.8
14.8 ± 0.9
0.2 ± 0.6
0.1, ±0.5; trivial
F0 (N)
534 ± 47
539 ± 43
5 ± 27
527 ± 41
522 ± 51
-4 ± 24
10, ±22; trivial
F0∙kg-1 (N∙kg-1)
7.3 ± 0.5
7.4 ± 0.4
0.0 ± 0.4
7.3 ± 0.3
7.3 ± 0.3
0.0 ± 0.3
0.1, ±0.3; trivial
FV-profile (slope∙kg-1)
0.90 ± 0.06
0.89 ± 0.07
-0.01 ± 0.05
0.91 ± 0.04
0.89 ± 0.04
-0.02 ± 0.05
0.01, ±0.04; trivial
V0 = theoretical maximal velocity, P0 = maximal horizontal power, F0 = maximal horizontal force, FV-profile = force-velocity profile.
All inferences for between-group differences were unclear.
Figure 1. Schematic overview of the study process
Figure 2. Individual relative changes in 30-m sprint time (Panel A) and theoretical
1
maximal velocity (V0) (Panel B) from pre- to post-training tests. Striped bars = CG,
2
black bars = ITG1, grey bars = ITG2, white bars = ITG3. Dotted lines denote smallest
3
worthwhile change.
4
5
-4
-3
-2
-1
0
1
2
3
% change 0-30 m sprint time
A.
-3
-2
-1
0
1
2
3
4
5
6
7
% change V0
B.
6
