## Force-velocity Profiling in Sled-resisted Sprint Running: Determining the Optimal Conditions for Maximizing Power

Cross, Matthew Rex

##### Abstract

The measurement of power-velocity and force-velocity relationships offers valuable insight
into athletic capabilities. The qualities underlying maximum power (i.e. optimal loading
conditions) are of particular interest in individualized training prescription and the enhanced
development of explosive performance. While research has examined these themes using cycle
ergometers and specialized treadmills, the conditions for optimal loading during over-ground
sprint running have not been quantified. This thesis aimed to assess whether force-velocitypower
relationships and optimal loading conditions could be profiled using a sled-resisted
multiple-trial method overground, if these characteristics differentiate between recreational
athletes and highly-trained sprinters, and whether conditions for optimal loading could be
determined from a single sprint. Consequently, this required understanding of the friction
characteristics underlying sled-resisted sprint kinetics. Chapter 3 presents a method of
assessing these characteristics by dragging an instrumented sled at varying velocities and
masses to find the conversion of normal force to friction force (coefficient of friction).
Methods were reliable (intraclass correlation [ICC]>0.99; coefficient of variation
[CV]<4.3%) and showed the coefficient of friction was dependent on sled towing velocity,
rather than normal load. The ‘coefficient of friction-velocity’ relationship was plotted by a
2nd order polynomial regression (R²=0.999; P<0.001), with the subsequent equation
presented for application in sled-resisted sprinting. Chapter 4 implements these findings,
using multiple trials (6-7) of sled-resisted sprints to generate individual force-velocity and
power-velocity relationships for recreational athletes (N=12) and sprinters (N=15). Data were
very well fitted with linear and quadratic equations, respectively (R²=0.977-0.997; P<0.001),
with all associated variables reliable (effect size [ES]=0.05-0.50; ICC=0.73-0.97; CV=1.0-
5.4%). The normal loads that maximized power (mean±SD) were 78±6 and 82±8% of bodymass,
representing an optimal force of 279±46 and 283±32 N at 4.19±0.19 and 4.90±0.18
m.s-1, for recreational and sprint athletes respectively. Sprinters demonstrated greater absolute
and relative maximal power (17.2-26.5%; ES=0.97-2.13; P<0.02; likely), with much greater
velocity production (maximum theoretical velocity, 16.8%; ES=3.66; P<0.001; most likely).
Optimal force and normal loading did not clearly differentiate between groups (unclear and
likely small differences; P>0.05), and sprinters developed maximal power at much higher
velocities (16.9%; ES=3.73; P<0.001; most likely). The optimal loading conditions for
maximizing power appear individualized (range=69-96% of body-mass), and represent much
greater resistance than current guidelines. Chapter 5 investigated the ability of a single sprint
to predict optimal sled loading, using identical methods to Chapter 4 and a recently validated
profiling technique using a single unloaded sprint. Power and maximal force were strongly
correlated (r=0.71-0.86), albeit with moderate to large error scores (standardized typical error
estimate [TEE]=0.53-0.71). Similar trends were observed in relative and absolute optimal
force (r=0.50-0.72; TEE=0.71-0.88), with estimated optimal normal loading practically
incomparable (bias=0.78-5.42 kg; r=0.70; TEE=0.73). However optimal velocity, and
associated maximal velocity, were well matched between the methods (r=0.99; bias=0.4-1.4%
or 0.00-0.04 m.s-1; TEE=0.12); highlighting a single sprint could conceivably be used to
calculate the velocity for maximizing horizontal power in sled sprinting. Given the prevalence
of resisted sprinting, practitioners and researchers should consider adopting these methods
for individualized prescription of training loads for improved horizontal power and
subsequent sprinting performance.