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sports
Article
Sled-Pull Load–Velocity Profiling and Implications
for Sprint Training Prescription in Young
Male Athletes
Micheál J. Cahill 1, 2, * , Jon L. Oliver 2,3 , John B. Cronin 2, Kenneth P. Clark 4, Matt R. Cross 2,5
and Rhodri S. Lloyd 2,3,6
1Athlete Training and Health, Plano, TX 75024, USA
2Sports Performance Research Institute New Zealand, Auckland University of Technology, 0632 Auckland,
New Zealand; joliver@cardiffmet.ac.uk (J.L.O.); john.cronin@aut.ac.nz (J.B.C.);
cross.matt.r@gmail.com (M.R.C.); rlloyd@cardiffmet.ac.uk (R.S.L.)
3CardiffSchool of Sport, CardiffMetropolitan University, Wales CF23 6XD, UK
4Department of Kinesiology, West Chester University, West Chester, PA 19383, USA; kclark@wcupa.edu
5Laboratoire Interuniversitaire de Biologie de la Motricité, University Savoie Mont Blanc, 73000
Chambéry, France
6Center for Sport Science and Human Performance, Waikato Institute of Technology, 3200 Hamilton,
New Zealand
*Correspondence: mcahill@athleteth.com
Received: 29 April 2019; Accepted: 17 May 2019; Published: 20 May 2019
Abstract:
The purpose of this study was to examine the usefulness of individual load–velocity
profiles and the between-athlete variation using the decrement in maximal velocity (Vdec) approach
to prescribe training loads in resisted sled pulling in young athletes. Seventy high school, team
sport, male athletes (age 16.7
±
0.8 years) were recruited for the study. All participants performed
one un-resisted and four resisted sled-pull sprints with incremental resistance of 20% BM. Maximal
velocity was measured with a radar gun during each sprint and the load–velocity relationship
established for each participant. A subset of 15 participants was used to examine the reliability of sled
pulling on three separate occasions. For all individual participants, the load–velocity relationship
was highly linear (r>0.95). The slope of the load–velocity relationship was found to be reliable
(coefficient of variation (CV) =3.1%), with the loads that caused a decrement in velocity of 10, 25,
50, and 75% also found to be reliable (CVs = <5%). However, there was a large between-participant
variation (95% confidence intervals (CIs)) in the load that caused a given Vdec, with loads of 14–21%
body mass (% BM) causing a Vdec of 10%, 36–53% BM causing a Vdec of 25%, 71–107% BM causing
a Vdec of 50%, and 107–160% BM causing a Vdec of 75%. The Vdec method can be reliably used
to prescribe sled-pulling loads in young athletes, but practitioners should be aware that the load
required to cause a given Vdec is highly individualized.
Keywords: resisted sled sprinting; acceleration; horizontal strength training; reliability
1. Introduction
The majority of sprint training research has examined the utility of resistance training and
plyometrics as methods to enhance sprinting capability [
1
–
3
] rather than sprint-specific training.
Sprint-specific training can be defined as training that is specific to the movement patterns and
direction of sprinting and it is likely to be more successful than non-specific training in improving
speed [
4
]. A popular method of sprint-specific training is to add resistance while moving in a horizontal
plane of motion, commonly referred to as resisted sprint. Recently, researchers have focused on resisted
Sports 2019,7, 119; doi:10.3390/sports7050119 www.mdpi.com/journal/sports
Sports 2019,7, 119 2 of 10
sled sprinting, specifically sled pulling, as a popular and effective method of sprint training [
5
,
6
]. As
with traditional resistance training, the resistive load used during sled pulling needs to be appropriately
prescribed to cause the desired training adaptations. The majority of previous sled pulling research
has been studied in adult populations and has prescribed lighter loads (<30% body mass) with the
emphasis on ensuring minimal disruption in sprint mechanics and small acute reductions in speed [
7
,
8
].
The reliability of resisted sled sprinting has been studied in adult populations [
5
]. However, the
reliability across multiple loads from light to heavy has not been examined in young athletes. More
recently, researchers have used heavier loads (>30% body mass) with the intention of improving
horizontal force application [
9
–
11
]. A review of available research in adults demonstrated that heavier
loads have been shown to provide greater increase in initial acceleration when compared to lighter
loads during resisted sled pulling [
12
]. However, there is a paucity of research at loads greater than
20 percent body mass (% BM) in young athletes [
13
]. Thus, limited insights and practical applications
for coaches regarding the effects of sled-pull loading are available for young athletes.
Traditionally, the load applied during sled pulling has been prescribed as a % BM [
12
].
However, due to differences in size, sex, strength, and training history across athletes, this may
be inappropriate [
13
]. The effects on growth and maturation during adolescence can lead to increased
variability in response to resisted sprinting [
14
]. This is particularly the case in athletes where loading
by a given % BM has been shown to slow immature boys by 50% more than mature boys [
15
].
Consequently, prescribing resistance solely as a % BM is likely to provide an even greater varied
training stimulus across young athletes in comparison to adults, providing a limited approach which
may lead to adaptations which are not necessarily intended. Given the linear relationship between
load and decrement in maximal velocity (Vdec), the Vdec approach has been suggested as a more
appropriate way to prescribe resistive sprint loads in comparison to % BM [
16
]. This method has been
assessed through multiple and single sprint trial methods of sled load prescription with both methods
proving to be effective in calculating the load that optimizes power (Lopt) during sled pulling [
9
,
16
].
As per recommendation by Cross et al. [
9
], a practical application for coaches is to use a combination of
both multiple-trial and single-trial methods. Athletes are assessed performing one single maximum
sprint and multiple sled sprints across a range of loads, with data then used to establish individual
load–velocity profiles. Training can then be prescribed by identifying the load for each individual that
causes a given decrement in velocity. This would be particularly useful in young athletes given the
increased variability of sprinting kinematics and kinetics associated with maturation [
17
]. However,
there is limited research using this approach, and to the authors’ knowledge, there has been very little
research describing the responses of young athletes to resisted sprinting.
Using individual load–velocity profiles to prescribe training with a load that causes a given Vdec
will provide practitioners with a simple method to standardize the training stimulus across individuals,
with different training goals expressed relative to Vdec. The linear load–velocity relationship during
resisted pulling leads to a parabolic power relationship. It has been demonstrated that a Vdec of 50%
maximizes power output during sled pulling, and suggested athletes should train with loads that
cause this reduction in velocity if the goal is to maximize power gains during sprinting [
16
]. The
recommended loads, however, are far greater than any load ever studied in young athletes. The study
also confirmed the linearity of the load–velocity relationship for a range of individuals (r
2
>0.97) and
showed that there was large between-participant variation in the load that corresponded to a Vdec of
50% (69–96% BM). While these methods have been verified in adult athletes, it is unknown whether
this would be the same for youth athletes given that they undergo anatomical, physiological, and
biological variations due to the maturation process [
18
]. It is possible that the variability may exist to
an even greater extent in resisted sled pulling as load increases in young athletes due to the differences
in maturity, size, and strength [15].
While the load that optimizes power during sled pulling has been established, other optimization
strategies may be needed to achieve different training goals. Extending on the work of Cross et al. [
16
],
different percentages of Vdec may represent training zones for either more speed or force orientated
Sports 2019,7, 119 3 of 10
training. Other researchers [
7
,
8
] have suggested limiting Vdec to <10% as the load to optimize
the maintenance of kinematics while providing a resistive stimulus. More recently, it has been
suggested that prescribing a Vdec <35% or >65% may target speed–strength and strength–speed
qualities, respectively [
13
]. Theoretically, it is clear that Vdec can be used to prescribe different training
intensities during resisted sprinting, but to date, no research has examined the ability of individual
load–velocity profiles to identify optimal loads across a range of training zones in young athletes. The
aims of the study are to examine the usefulness of individual load–velocity profiles and the amount of
between-athlete variation associated with the Vdec approach to prescribe training loads during sled
pulling in young athletes. The authors hypothesize that the Vdec approach is a reliable, effective, and
precise way of prescribing sled load to young athletes.
2. Materials and Methods
2.1. Subjects
Seventy male high school team sport athletes from two sports, rugby and lacrosse (16.7
±
0.9 years;
height, 1.77
±
6.9 cm; weight, 75.6
±
10.9 kg; post-peak height velocity 1.8
±
0.8 years and Vmax;
8.08
±
0.49 m/s) were recruited to participate in this study. All subjects’ biological maturity was
established as post-peak height velocity (PHV) using a non-invasive method with reliability within
0.5 years of calculating the age at PHV according to Mirwald et al. [
19
]. All subjects had a minimum
of one-year resistance training experience and were healthy and injury free at the time of testing.
Written consent was obtained from a parent/guardian and assent from each subject before participation.
Experimental procedures were approved by the West Chester University institutional ethics committee.
The study was conducted according to the Declaration of Helsinki.
2.2. Study Design
To determine the load–velocity relationship of un-resisted sprinting and sled pulling in youth
athletes, seventy male subjects performed one un-resisted and three resisted sprints during a
familiarization and the subsequent data collection session. A subset of participants (n=15) was used
to examine the reliability of sled pulling, repeating the protocol on three separate occasions separated
by seven days. Resisted sprints were completed with a range of loads to allow the load–velocity
relationship to be modelled. The maximum velocity attained (Vmax) during each sprint was measured
via radar gun. Using Vmax individual load–velocity relationships were then established for each
subject and used to identify loads that corresponded to a Vdec of 10, 25, 50, and 75%.
2.3. Procedures
All subjects reported one week prior to the first data collection, where they were familiarized
with the equipment and testing procedures. Testing procedures were completed in dry conditions
and on an outdoor 4G artificial turf field with sprint lanes set-up at a cross wind. A randomized
counter balance design was implemented during data collection. Subjects were required to abstain
from high-intensity training in the 24 h prior to the testing session. Subjects wore running shoes and
comfortable clothing. A radar device (Model: Stalker ATS II, Applied Concepts, Dallas, TX, USA) was
positioned 10 m directly behind the starting position and at a vertical height of 1 m to approximately
align with the subject’s center of mass as per the recommendation of Simperingham et al. [20].
Subjects started from a standing split stance position and sprinted in a straight line for a recorded
distance of 30 m with maximal effort for un-resisted efforts and 20 m for resisted efforts. A set of
cones was placed 2 m in front of each 30 and 20 m markers to ensure maximal effort and achievement
of maximal velocity during the sprint. Distances were estimated from pilot testing to ensure Vmax
was achieved without inducing additional fatigue. In all sessions, subjects performed a standardized
dynamic warm up consisting of sprint mechanics, dynamic stenches, and body weight exercises
followed by two submaximal effort sprints (70% and 90% of self-determined maximal intensity) before
Sports 2019,7, 119 4 of 10
completing maximal effort sprints. A minimum of four minutes and a maximum of six minutes of
passive recovery was given between each sprint (un-resisted and resisted). Maximum velocity was
gathered from the radar gun for all trials. Software provided by the radar device manufacturer (STATs
software, Stalker ATS II Version 5.0.2.1, Applied Concepts Dallas, Dallas, TX, USA) was used to collect
raw velocity data throughout each sprint.
2.3.1. Un-Resisted Sprinting Protocol
Subjects were instructed to approach the start line and stand in a split stance with their preferred
foot to jump offin front and kicking dominant foot behind. Subjects were instructed to sprint through
a set of cones placed at 32 m.
2.3.2. Resisted Sled-Pulling Protocol
Subjects received the same identical setup, instructions, and cues as per the un-resisted sprints.
The heavy-duty custom-made pull sled (8.7 kg) was placed 3.3 m behind the subject attached to a waist
harness by a non-elastic nylon tether. Subjects were instructed to take up all the slack in the tether to
ensure no bouncing or jerking as they initiated the sprint. An example of this setup is illustrated in
Figure 1. Participants were instructed to sprint through a set of cones placed at 22 m. The first resisted
trial used an absolute load of 27 kg including the weight of the sled, participants then completed
sprints with a minimum of three additional loads increasing in increments of 20% BM (+20, 40, and
60% BM). The load range was based on pilot testing, which determined the range of loads that reduced
an athlete’s velocity by values above and below 50% of un-resisted Vmax and would allow individual
load–velocity relationships to be calculated. Loads were selected to fall within the desired velocity
decrement thresholds above and below 50% Vmax but not to induce unnecessary fatigue during
maximal efforts.
Sports 2019, 7, x FOR PEER REVIEW 4 of 10
(STATs software, Stalker ATS II Version 5.0.2.1, Applied Concepts Dallas, Dallas, TX, USA) was used
to collect raw velocity data throughout each sprint.
2.3.1. Un-Resisted Sprinting Protocol
Subjects were instructed to approach the start line and stand in a split stance with their preferred
foot to jump off in front and kicking dominant foot behind. Subjects were instructed to sprint through
a set of cones placed at 32 m.
2.3.2. Resisted Sled-Pulling Protocol
Subjects received the same identical setup, instructions, and cues as per the un-resisted sprints.
The heavy-duty custom-made pull sled (8.7 kg) was placed 3.3 m behind the subject attached to a
waist harness by a non-elastic nylon tether. Subjects were instructed to take up all the slack in the
tether to ensure no bouncing or jerking as they initiated the sprint. An example of this setup is
illustrated in Figure 1. Participants were instructed to sprint through a set of cones placed at 22 m.
The first resisted trial used an absolute load of 27 kg including the weight of the sled, participants
then completed sprints with a minimum of three additional loads increasing in increments of 20%
BM (+20, 40, and 60% BM). The load range was based on pilot testing, which determined the range of
loads that reduced an athlete’s velocity by values above and below 50% of un-resisted Vmax and
would allow individual load–velocity relationships to be calculated. Loads were selected to fall
within the desired velocity decrement thresholds above and below 50% Vmax but not to induce
unnecessary fatigue during maximal efforts.
Figure 1. An example of the athletes starting stance and setup for resisted sled pulling.
2.4. Load–Velocity Relationship and Load Optimization
Maximum sprint velocity was obtained for each un-resisted and resisted trial. The individual
load–velocity (LV) relationship was established for each participant and checked for linearity. The
linear regression of the load–velocity relationship was then used to establish the load that
corresponded to a velocity decrement of 10% (L
10
), 25% (L
25
), 50% (L
50
), and 75% (L
75
), with the slope
of the line explaining the relationship between load and velocity. An example of this is illustrated in
Figure 2.
Figure 1. An example of the athletes starting stance and setup for resisted sled pulling.
2.4. Load–Velocity Relationship and Load Optimization
Maximum sprint velocity was obtained for each un-resisted and resisted trial. The individual
load–velocity (LV) relationship was established for each participant and checked for linearity. The
linear regression of the load–velocity relationship was then used to establish the load that corresponded
to a velocity decrement of 10% (L
10
), 25% (L
25
), 50% (L
50
), and 75% (L
75
), with the slope of the line
explaining the relationship between load and velocity. An example of this is illustrated in Figure 2.
Sports 2019,7, 119 5 of 10
Sports 2019, 7, x FOR PEER REVIEW 5 of 10
Figure 2. An example of the load–velocity relationship for one subject. The raw data () shows the
maximum velocity (Vmax) collected during resisted and un-resisted sprints. Using the linear
relationship between load and velocity, the plotted Vdec () shows the calculated loads
corresponding to a 10, 25, 50, 75, and 100% decrement in velocity.
2.5. Statistical Analysis
Raw data was filtered through custom-made LabVIEW software to determine the maximum
velocity of each participant during each sprint. Data were reported as means and standard deviation
(SD) to represent the centrality and spread of the data. In the smaller subset of participants (n = 15),
reliability of Vmax and Vdec were examined across the three different trials by calculating the change
in the mean to examine systematic bias. Random variation was then investigated by establishing the
relative reliability using an intra-class correlation coefficient (ICC) and absolute reliability using the
coefficient of variation (CV). Between-day pairwise analysis of reliability was assessed using
Hopkins’ online Excel spreadsheet [21]. Simperingham et al. [20] have suggested thresholds for
establishing the reliability of sprints using a radar gun as a CV < 10% and ICC > 0.70. The load–
velocity relationship of youth athletes was described using statistics from the larger sample of n = 70.
The strength of linearity of the load–velocity relationship was established for each participant and a
one-way repeated measures ANOVA with Bonferroni post-hoc test used to confirm whether
differences in Vmax occurred with increased loading. The relationships between variables were
determined using Pearson’s correlation coefficients. The alpha level was set as p < 0.05 with analysis
performed in SPSS (Version 23.0). The mean Vdec across all participants at each load was calculated
and between-subject variability calculated using 95% confidence intervals.
3. Results
The reliability of the variables of interest for the sled pull can be observed in Table 1. No
consistent pattern of change in the mean was evident across Vmax, Vdec or the slope of the load–
velocity relationship across the three trials. The coefficient of variation for Vmax was always <10%,
while for the slope of the LV relationship and Lopt it was always <5%. The ICCs ranged from 0.60 to
0.92, with the lowest ICCs associated with Lopt and acceptable relative reliability for the slope of the
LV relationship and Vmax. However, when Lopt was expressed in absolute load (kg), very high
relative reliability (<0.90) was reported. Pairwise analysis indicated that both relative and absolute
random variation were stable across trials.
Figure 2.
An example of the load–velocity relationship for one subject. The raw data (
N
) shows
the maximum velocity (Vmax) collected during resisted and un-resisted sprints. Using the linear
relationship between load and velocity, the plotted Vdec (
) shows the calculated loads corresponding
to a 10, 25, 50, 75, and 100% decrement in velocity.
2.5. Statistical Analysis
Raw data was filtered through custom-made LabVIEW software to determine the maximum
velocity of each participant during each sprint. Data were reported as means and standard deviation
(SD) to represent the centrality and spread of the data. In the smaller subset of participants (n=15),
reliability of Vmax and Vdec were examined across the three different trials by calculating the change
in the mean to examine systematic bias. Random variation was then investigated by establishing the
relative reliability using an intra-class correlation coefficient (ICC) and absolute reliability using the
coefficient of variation (CV). Between-day pairwise analysis of reliability was assessed using Hopkins’
online Excel spreadsheet [
21
]. Simperingham et al. [
20
] have suggested thresholds for establishing the
reliability of sprints using a radar gun as a CV <10% and ICC >0.70. The load–velocity relationship of
youth athletes was described using statistics from the larger sample of n=70. The strength of linearity
of the load–velocity relationship was established for each participant and a one-way repeated measures
ANOVA with Bonferroni post-hoc test used to confirm whether differences in Vmax occurred with
increased loading. The relationships between variables were determined using Pearson’s correlation
coefficients. The alpha level was set as p<0.05 with analysis performed in SPSS (Version 23.0). The
mean Vdec across all participants at each load was calculated and between-subject variability calculated
using 95% confidence intervals.
3. Results
The reliability of the variables of interest for the sled pull can be observed in Table 1. No consistent
pattern of change in the mean was evident across Vmax, Vdec or the slope of the load–velocity
relationship across the three trials. The coefficient of variation for Vmax was always <10%, while
for the slope of the LV relationship and Lopt it was always <5%. The ICCs ranged from 0.60 to 0.92,
with the lowest ICCs associated with Lopt and acceptable relative reliability for the slope of the LV
relationship and Vmax. However, when Lopt was expressed in absolute load (kg), very high relative
reliability (<0.90) was reported. Pairwise analysis indicated that both relative and absolute random
variation were stable across trials.
Sports 2019,7, 119 6 of 10
Table 1.
The reliability of maximal velocity (Vmax), the load corresponding to given decrements in
velocity (Lopt), and the slope of the load–velocity relationship during resisted sled pulling. Results are
shown as mean
±
SD and reliability statistics (95% CI). CV—coefficient of variation; ICC—intra-class
correlation; Vmax—maximum velocity; Lopt—optimal load.
Reliability of Sprint
Variables
Mean Change in Mean (%) CV (%) ICC
Trial 1 Trial 2 Trial 3 Trial 1–2 Trial 2–3 Trial 1–2 Trial 2–3 Trial 1–2 Trial 2–3
Vmax
(m/s)
Un-resisted 7.9 ±0.5 8.0 ±0.4 7.9 ±0.5 1.0 −1.5 2.8 2.1 0.84 0.88
(−1.1–3.1) (−3.1–0.0) (2.1–4.4) (1.6–3.3) (0.64–0.95) (0.68–0.96)
27 kg 6.1 ±0.8 6.1 ±0.8 6.1 ±0.7 −0.6 −0.7 4.9 3.1 0.84 0.92
(−4.1–3.0) (−1.7–3.2) (3.6–7.6) (2.3–5.0) (0.60–0.94) (0.79–0.97)
+20% BM 5.2 ±0.5 5.2 ±0.5 5.1 ±0.6 −1.1 −1.0 3.4 4.2 0.91 0.87
(−3.5–1.4) (−4.1–2.3) (2.5–5.2) (3.1–6.7) (0.75–0.97) (0.66–0.95)
+40% BM 4.4 ±0.6 4.1 ±0.4 4.4±0.6 −5.7 6.2 7.1 6.7 0.72 0.77
(−10.5–−0.7) (1.2–11.5) (5.2–11.3) (4.9–10.5) (0.36–0.89) (0.45–0.91)
+60% BM 3.7 ±0.6 3.5 ±0.5 3.8 ±0.6 −7.1 8.0 8.6 9.0 0.69 0.73
(−13.4–−0.4) (0.7–15.8) (6.1–14.6) (6.4–14.8) (0.24–0.90) (0.34–0.90)
Lopt
(% BM)
10% Vdec 17 ±1 17 ±1 17 ±1−1.5 0.1 3.2 3.3 0.71 0.65
(−4.3–1.4) (−2.8–3.0) (2.3–5.6) (2.3–5.6) (0.26–0.91) (0.15–0.88)
25% Vdec 42 ±443 ±342 ±21.2 −0.7 4.8 3.7 0.60 0.60
(−3.0–5.6) (−3.9–2.6) (3.4–8.3) (2.6–6.4) (0.07–0.87) (0.08–0.87)
50% Vdec 84 ±7 85 ±5 85 ±41.3 −0.6 4.6 3.5 0.63 0.64
(−2.7–5.5) (−3.7–2.5) (3.3–8.0) (2.5–6.1) (0.12–0.88) (0.13–0.88)
75% Vdec 125 ±11 128 ±8 127 ±51.4 −0.9 4.8 3.7 0.60 0.60
(−2.8–5.8) (−4.1–2.4) (3.4–8.3) (2.6–6.4) (0.07–0.87) (0.07–0.87)
Slope
Load–Velocity
−1.72 ±
0.15
−1.72 ±
0.08
−1.72 ±
0.06
−0.7 0.4 4.0 2.2 0.71 0.75
(−4.1–2.9) (−1.5–2.4) (2.8–6.8) (1.6–3.8) (0.23–0.91) (0.30–0.92)
Load–Velocity Profiling Results
In the large population of young athletes, the average Vmax achieved in un-resisted sprinting and
with mean loads of 55 ±3% BM, 75 ±7% BM, 95 ±10% BM, and 115 ±14% BM were 8.1 m/s±0.59 s,
5.61 m/s
±
0.56 s, 4.47 m/s
±
0.54 s, and 3.74 m/s
±
0.47 s, respectively. Analysis revealed that Vmax
at each load were significantly different to one another (p<0.001). For all subjects, the load–velocity
relationship was highly linear (r>0.95), as was the case for the mean data across the group (r=0.99).
The mean load–velocity profile together with loads that correspond to a Vdec of 10, 25, 50, and 75% for
a large group of youth athletes can be observed in Figure 3. Based on the individual load–velocity
relationships, the Lopt that corresponded to a Vdec of 10, 25, 50. and 75% (95% CI) were 18 (14–21),
45 (36–53), 89 (71–107), and 133% (107–160) BM. Pearson’s correlation coefficients did not demonstrate
a significant relationship between Lopt expressed as % BM and variables such as maturity, weight
or Vmax.
Sports 2019, 7, x FOR PEER REVIEW 6 of 10
Table 1. The reliability of maximal velocity (Vmax), the load corresponding to given decrements in
velocity (Lopt), and the slope of the load–velocity relationship during resisted sled pulling. Results
are shown as mean ± SD and reliability statistics (95% CI). CV—coefficient of variation; ICC—intra-
class correlation; Vmax—maximum velocity; Lopt—optimal load.
Reliability of
Sprint Variables
Mean Change in Mean (%) CV (%) ICC
Trial 1 Trial 2 Trial 3 Trial 1–2 Trial 2–3 Trial 1–2 Trial 2–3 Trial 1–2 Trial 2–3
Vmax
(m/s)
Un-
resisted 7.9 ± 0.5 8.0 ± 0.4 7.9 ± 0.5 1.0
(−1.1–3.1)
−1.5
(−3.1–0.0)
2.8
(2.1–4.4)
2.1
(1.6–3.3)
0.84
(0.64–0.95)
0.88
(0.68–0.96)
27 kg 6.1 ± 0.8 6.1 ± 0.8 6.1 ± 0.7 −0.6
(−4.1–3.0)
−0.7
(−1.7–3.2)
4.9
(3.6–7.6)
3.1
(2.3–5.0)
0.84
(0.60–0.94)
0.92
(0.79–0.97)
+20% BM 5.2 ± 0.5 5.2 ± 0.5 5.1 ± 0.6 −1.1
(−3.5–1.4)
−1.0
(−4.1–2.3)
3.4
(2.5–5.2)
4.2
(3.1–6.7)
0.91
(0.75–0.97)
0.87
(0.66–0.95)
+40% BM 4.4 ± 0.6 4.1 ± 0.4 4.4± 0.6 −5.7
(−10.5–−0.7)
6.2
(1.2–11.5)
7.1
(5.2–11.3)
6.7
(4.9–10.5)
0.72
(0.36–0.89)
0.77
(0.45–0.91)
+60% BM 3.7 ± 0.6 3.5 ± 0.5 3.8 ± 0.6 −7.1
(−13.4–−0.4)
8.0
(0.7–15.8)
8.6
(6.1–14.6)
9.0
(6.4–14.8)
0.69
(0.24–0.90)
0.73
(0.34–0.90)
Lopt
(%
BM)
10% Vdec 17 ± 1 17 ± 1 17 ± 1 −1.5
(−4.3–1.4)
0.1
(−2.8–3.0)
3.2
(2.3–5.6)
3.3
(2.3–5.6)
0.71
(0.26–0.91)
0.65
(0.15–0.88)
25% Vdec 42 ± 4 43 ± 3 42 ± 2 1.2
(−3.0–5.6)
−0.7
(−3.9–2.6)
4.8
(3.4–8.3)
3.7
(2.6–6.4)
0.60
(0.07–0.87)
0.60
(0.08–0.87)
50% Vdec 84 ± 7 85 ± 5 85 ± 4 1.3
(−2.7–5.5)
−0.6
(−3.7–2.5)
4.6
(3.3–8.0)
3.5
(2.5–6.1)
0.63
(0.12–0.88)
0.64
(0.13–0.88)
75% Vdec 125 ± 11 128 ± 8 127 ± 5 1.4
(−2.8–5.8)
−0.9
(−4.1–2.4)
4.8
(3.4–8.3)
3.7
(2.6–6.4)
0.60
(0.07–0.87)
0.60
(0.07–0.87)
Slope
Load–
Velocity
−1.72 ±
0.15
−1.72 ±
0.08
−1.72 ±
0.06
−0.7
(−4.1–2.9)
0.4
(−1.5–2.4)
4.0
(2.8–6.8)
2.2
(1.6–3.8)
0.71
(0.23–0.91)
0.75
(0.30–0.92)
3.1. Load–Velocity Profiling Results
In the large population of young athletes, the average Vmax achieved in un-resisted sprinting
and with mean loads of 55 ± 3% BM, 75 ± 7% BM, 95 ± 10% BM, and 115 ± 14% BM were 8.1 m/s ± 0.59
s, 5.61 m/s ± 0.56 s, 4.47 m/s ± 0.54 s, and 3.74 m/s ± 0.47 s, respectively. Analysis revealed that Vmax
at each load were significantly different to one another (p < 0.001). For all subjects, the load–velocity
relationship was highly linear (r > 0.95), as was the case for the mean data across the group (r = 0.99).
The mean load–velocity profile together with loads that correspond to a Vdec of 10, 25, 50, and 75%
for a large group of youth athletes can be observed in Figure 3. Based on the individual load–velocity
relationships, the Lopt that corresponded to a Vdec of 10, 25, 50. and 75% (95% CI) were 18 (14–21),
45 (36–53), 89 (71–107), and 133% (107–160) BM. Pearson’s correlation coefficients did not
demonstrate a significant relationship between Lopt expressed as % BM and variables such as
maturity, weight or Vmax.
Figure 3. The linear mean load–velocity relationship for a group of n = 70 youth athletes with the
loads that correspond to a decrement in velocity of 10, 25, 50, and 75 representing technical
competency, speed–strength, power and strength–speed training zones.
Figure 3.
The linear mean load–velocity relationship for a group of n=70 youth athletes with the loads
that correspond to a decrement in velocity of 10, 25, 50, and 75 representing technical competency,
speed–strength, power and strength–speed training zones.
Sports 2019,7, 119 7 of 10
4. Discussion
The purpose of this study was to examine the usefulness of load–velocity profiling and the
between-athlete variation associated with load prescription during resisted sled pulling in young
athletes. The highly linear nature of all individual load–velocity profiles confirms the validity of the
approach. The study also established that optimized loads could be reliably identified for different
decrements in velocity, suggesting the process can be used to consistently prescribe loads specific to
a variety of training outcomes. Importantly, the study also highlights that there is relatively large
between-subject variation in the loads that cause a given amount of Vdec. For example, the load
that optimizes power, causing a Vdec of 50%, had a confidence interval spanning 71–107%. This
individual variability is in agreement with previous research [
16
] and confirms that prescribing load
simply as a given % BM for all individuals would be an invalid approach to prescribe training load in
young athletes.
Reliability analysis demonstrated no systematic bias in any of the variables, suggesting the absence
of any learning effects, which is in agreement with previous research in adult populations [
5
,
9
,
16
]. This
is the first study to examine the reliability of resisted sled pulling in young athletes. When examining
the CV across multiple loads for Vmax, it was found to demonstrate acceptable absolute reliability
<10%. Optimizing load might be considered the variable of most interest for resisted sled training
prescription, and this had low random variation with CVs <5%. Intra-class correlation coefficients
were acceptable (
≥
0.70) for nearly all Vmax comparisons. Although ICCs were lower for Lopt, when
expressed in absolute loads they demonstrated very high levels (<0.90) of relative reliability. This
finding reflects the more homogenous nature of Lopt when expressed relative to body mass versus
the more heterogenous nature of Lopt when expressed as absolute load. The high reliability of the
optimized loads for each training zone was due to the consistency of the load–velocity profile, with the
slope of the individual relationships found to be reliable. Specific conditions of <10, 25, 50, and 75% of
Vdec to correspond within zones of technical competency, speed–strength, power and strength–speed
have been suggested in this study. However, based on the reliability of the load–velocity slope,
researchers and practitioners could identify optimized loads that correspond to alternative target
decrements in velocity. Specific Lopts could be reliably prescribed to young athlete’s dependent on
the phase of the season such as heavier strength–speed zones during pre-season phases and lighter
speed–strength zones closer to or within competition.
The high degree of reliability shown in the current study are congruent with previous research
examining sled load prescription [
5
,
16
]. The lack of systematic bias and stable random variation across
trials suggests there were no improvements in reliability across trials, which may be partly due to the
familiarization to sled pulling prior to data collection. The results of the current study suggest that
individual load–velocity profiles can be reliably used to identify optimized loads across a range of
velocities. It is difficult to compare the data of the current study to previous research, due to the lack of
research that has used sprint LV profiling in youth athletes. However, force–velocity and load–velocity
profiling in other forms of resistance exercises in youth have been shown to be reliable (CV 0.7–6.8;
ICC–0.94) [
22
]. The results of the current study suggest the method can be applied to youth athletes to
provide an individualized approach to sled-load training prescription.
Resisted sprint training is a popular method of providing a sprint-specific resistive stimulus.
Consequently, resisted sled pulling is a common training method examined by researchers [
6
,
12
,
13
].
However, little uniformity exists for sled-load training prescription. Unsurprisingly, the addition of
greater load caused significant reductions in sprint velocity, allowing the load–velocity relationship to
be modelled. The validity of the method is supported given the linear relationship between load and
velocity; the current study demonstrated all individuals had a highly linear profile (r>0.95) suggesting
the approach can be applied to a large range of athletes. The loads corresponding to a Vdec of 10, 25,
50, and 75% were 18, 45, 89, and 133% BM, respectively. These loads are considerably higher than the
majority of the literature previously examining sled pulling and far greater than loads considered
heavy (20–30% BM) and very heavy (30+% BM) in a review by Petrakos et al. [
12
]. Based on the current
Sports 2019,7, 119 8 of 10
findings, loads of 20–30% BM would only be likely to cause modest decrements in velocity (<20%), and
what are considered “heavy” loads may need to be reconsidered by both researchers and practitioners.
In agreement with recent research [
16
], there was a large amount of between-subject variation in
Lopt for a given training outcome. Cross et al. [
16
] reported a range in load of 69–96% BM to cause a
Vdec of 50% to optimize power. Similarly, the current study found that a Vdec of 50% resulted in loads
ranging from 71–107% BM across a large group of youth athletes and this level of between-athlete
variability was consistent across training zones. Although large variability was found between athletes,
the Lopt expressed as % BM was not significantly related to weight, PHV or Vmax. Rumpf et al. [
15
]
found significant differences on the effect of loading between pre- and post-PHV athletes; however,
the current study found no significant relationship between levels of maturity and Lopt within a
cohort of post-PHV athletes. Further research such as the assessment of strength and fat-free mass is
needed to better explain the variability between athletes within a group of post-PHV. The findings of
this study have major implications for sled-load training prescription for youth populations. While
practitioners and previous research have traditionally prescribed loads based on % BM [
15
,
23
,
24
], this
approach appears invalid. Based on the current findings, a given load prescribed as a set % BM could
reduce the speed of one athlete by up to 50% more than that of another athlete. This would expose
athletes to very different stimuli and would potentially lead to different chronic training adaptations.
Prescribing training using individual load–velocity profiles provides a method to reliably target a given
decrement in velocity within a desired zone of training such as technical competency, speed–strength,
power and strength–speed. Furthermore, matching the training zone to the athlete’s force–velocity
characteristics could potentially yield better training results than simply applying the same resistive
load for all athletes [
25
]. However, further research is needed to better explain the between athlete
variation and understand the chronic adaptations when undertaking this approach to sled-pull training
in young athletes.
The majority of resisted sprint training research has primarily focused on the high-velocity
end of the load–velocity relationship [
7
,
8
], ensuring minimal disruption to sprint mechanics by
keeping velocity at >90% of the maximum. In the current paper, this has been termed the technical
competency zone. This zone may be more applicable to sprinters who want to add a resistive stimulus
while still achieving high velocities without affecting sprint mechanics closer to competition. With
respect to maturation, technical competency zone training could be best utilized during pre-PHV
in young athletes when technical acquisition of sprint mechanics is a priority due to the central
nervous system development [
26
]. Alternatively, athletes of post-PHV who are undergoing increases
in androgenic hormones and greater muscle cross-sectional area at the onset of puberty will benefit
more with greater resistive loads to stimulate the ability to produce high amounts of horizontal force
and impulse [
10
,
15
,
27
]. A recent review by Lesinski et al. [
28
] suggested that practitioners should
emphasize higher intensities and force dominant capabilities of young athletes. Therefore, heavier
resistive sled loads may be viewed as an extension of traditional resistance training, but applied
horizontally rather than vertically. Recent research has begun to examine the use of heavier sled
loads in adults [
10
,
11
,
25
], although apart from the current study only loads of up to 20% BM have
previously been used with youth athletes [
14
,
29
]. More research is needed to understand chronic
training adaptations to heavier sled loads, particularly when prescribed to cause a target decrement
in velocity.
5. Conclusions
In conclusion, the findings of the current study confirm our hypothesis that the load–velocity
relationship is linear during sled pulling in young athletes. The slope and Vdec approach to sled-pulling
load prescription were found to be reliable also. However, the load associated with a given Vdec
varies across young athletes. The highly linear relationship between load and velocity and acceptable
reliability of variables derived from individual load–velocity profiles allow for consistent sled-load
training prescription in young athletes during a time in which development of speed is critical. The
Sports 2019,7, 119 9 of 10
large variability in the amount of loading required to cause a target decrement in velocity further
reinforces the need to adopt an individual approach to sled loading, particularly where the goal is to
provide a consistent training stimulus across young athletes of varying size, strength, and training
histories. Optimized loads for different training zones were reported in the current study and found to
be reliable for technical, speed–strength, power and strength–speed zones. These zones can be used to
help coaches periodize sled-loading parameters across a season, such as utilizing strength–speed zones
during the off-season and speed–strength zones as competition approaches. Most importantly, the
load–velocity relationship was found to be reliable, which means practitioners could reliably prescribe
training for any given decrement in velocity. This would allow coaches to qualitatively prescribe
individual sled loads and zones of training based on the force–velocity characteristics of the individual
athlete. Given the maturational differences across young athletes, sled types and surface practitioners
should determine individual load–velocity profiles for athletes in their training environments to better
target the desired training adaptation.
Author Contributions:
Conceptualization, M.J.C., J.L.O. and J.B.C.; Data curation, K.P.C.; Formal analysis, M.J.C.,
J.L.O. and K.P.C.; Investigation, M.J.C.; Methodology, M.J.C., J.L.O., J.B.C., K.P.C., M.R.C. and R.S.L.; Project
administration, M.J.C., J.L.O. and J.B.C.; Resources, M.J.C.; Software, M.R.C.; Supervision, J.L.O., J.B.C., K.P.C. and
R.S.L.; Validation, M.J.C. and M.R.C.; Writing—original draft, M.J.C. and J.L.O.; Writing—review and editing,
J.L.O., J.B.C., K.P.C., M.R.C. and R.S.L.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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