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Quantifying variation in discrete force–time characteristics during the sprint start: Considerations
for monitoring training adaptations
by
Lindsay Musalem
A thesis submitted in conformity with the requirements for the degree of Master of Science
Department of Exercise Sciences University of Toronto
© Copyright by Lindsay Musalem 2016
ii
Quantifying variation in discrete force–time characteristics during the sprint start: Considerations for monitoring training adaptations
Lindsay Musalem
Master of Science Department of Exercise Sciences
University of Toronto 2016
Abstract
Biomechanical quantities during the sprint start have been correlated with race
time. “Typical” training variation in these quantities must be understood to
interpret changes. Purposes were to: (1) describe variation; and (2) compare
variation between-‐session. Using force plate-‐instrumented starting blocks, foot-‐
block interactive forces were measured from four starts in two training sessions
(n=10). Pre-‐tension (PT), reaction time (RT), block time (BT), rate of force
development (RFD), peak force (PF), impulse, time of force application (TFA), time
to peak (TTP), peak force offset (PFTO), and force ‘off’ offset (FOTO) were derived
trial-‐by-‐trial. Variable-‐specific coefficients of variation (CoVs) were calculated
within-‐session, and compared between-‐session. PT, BT, RFD and TTP exhibited
within-‐session CoVs ≤ 10%. TTP CoVs were significantly different (p < 0.05)
between-‐session. Until individuals’ typical variability is established, quantities with
larger (> 20%) within-‐session CoVs (RT, RFD, TTP, TFA) and those with significant
differences between-‐session may not be appropriate for monitoring.
iii
Table of Contents List of Figures ........................................................................................................................... iv
List of Tables ............................................................................................................................. v
List of Appendices ................................................................................................................... vi 1 Introduction ....................................................................................................................... 1
2 Review of Literature ........................................................................................................ 5 2.1 Pre-‐tension (PT) ..................................................................................................................... 5 2.2 Reaction Time (RT) ............................................................................................................... 6 2.3 Block Time (BT) ..................................................................................................................... 8 2.4 Rate of Force Development (RFD) .................................................................................... 9 2.5 Peak Force (PF) .................................................................................................................... 10 2.6 Impulse .................................................................................................................................... 11 2.7 Other Force–Time Characteristics ................................................................................. 12 2.8 Summary ................................................................................................................................. 12
3 Methods ............................................................................................................................. 14 3.1 Study Overview ..................................................................................................................... 14 3.2 Participants ........................................................................................................................... 14 3.3 Data Collection ...................................................................................................................... 15 3.4 Signal Conditioning and Processing .............................................................................. 17 3.5 Data Analyses ........................................................................................................................ 19 3.6 Statistics .................................................................................................................................. 23
4 Results ............................................................................................................................... 24 4.1 Pre-‐Tension ........................................................................................................................... 24 4.2 Reaction Time ....................................................................................................................... 26 4.3 Block Time ............................................................................................................................. 26 4.4 Rate of Force Development (RFD) .................................................................................. 27 4.5 Peak Force .............................................................................................................................. 30 4.6 Impulse .................................................................................................................................... 31 4.7 Time to Peak (TTP) ............................................................................................................. 33 4.8 Time of Force Application ................................................................................................. 35 4.9 Peak Force Time Offset ...................................................................................................... 36 4.10 Force ‘Off’ Time Offset ...................................................................................................... 37
5 Discussion ........................................................................................................................ 39 5.2 Study Limitations ................................................................................................................. 44
6 Conclusion ........................................................................................................................ 48
iv
List of Figures Figure 1. Starting Block Components ............................................................................................... 2 Figure 2. Adjustable block settings: plate angles (θF, θR) and front foot to start line
(F_SL) and rear foot to start line (R_SL) distances. ......................................................... 16 Figure 3. Residual Analyses of RES_3 signal for two male (a) (b) and two female (c)
(d) participants ............................................................................................................................... 19 Figure 4. Graphical depiction of signal events ............................................................................ 20 Figure 5. Signal-‐specific variables .................................................................................................... 21 Figure 6. Between-‐feet and overall variables .............................................................................. 22 Figure 7. Pre-‐tension resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy &
R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. ............................................ 25
Figure 8. Reaction times compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. .................... 26
Figure 9. Block times compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. * indicates that mean values were significantly different inter-‐session (p < 0.05). ................ 27
Figure 10. RFD from ‘Go’ resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. .................... 28
Figure 11. RFD from force onset resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. ... 29
Figure 12. Peak Force resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. ............................................ 31
Figure 13. Impulse of resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. * Significant inter-‐session difference in group mean (p < 0.05). .................................................................... 32
Figure 14. TTP from ‘Go’ resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. .................... 34
Figure 15. TTP from force onset resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. ... 35
Figure 16. Time of force application resultant and component (Front, Rear) forces compared inter-‐session. Mean values across all athletes (N = 10) are presented; error bars represent the standard deviation. .................................................................... 36
Figure 17. Peak Force Time Offset compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. ... 37
Figure 18. Force ‘Off’ Time Offset compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. ... 38
v
List of Tables Table 1. Participant demographics and anthropometrics ..................................................... 15 Table 2. Participant block configurations ..................................................................................... 16 Table 3. Derived signals for analysis ............................................................................................... 18 Table 4. Signal Events ............................................................................................................................ 20 Table 5. Signal-‐specific variables ...................................................................................................... 21 Table 6. Between-‐feet variables ........................................................................................................ 22 Table 7. Overall variables ..................................................................................................................... 22 Table 8. Mean (SD) sessional (1&2) CoV (%) and p values for pre-‐tension resultant
(RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10). ......................................................................................................... 25
Table 9. Mean (SD) sessional (1&2) CoV (%) and p values for RFD from ‘Go’ resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10). ............................................................................. 28
Table 10. Mean (SD) sessional (1&2) CoV (%) and p values for RFD from force onset resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10). ............................................................................. 29
Table 11. Mean (SD) sessional (1&2) CoV (%) and p values for peak force resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10). ......................................................................................................... 30
Table 12. Mean (SD) sessional (1&2) CoV (%) and p values for Force Impulse resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10). ............................................................................. 32
Table 13. Mean (SD) sessional (1&2) CoV (%) and p values for TTP from ‘Go’ resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10). ............................................................................. 33
Table 14. Mean (SD) sessional (1&2) CoV (%) and p values for TTP from force onset resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10) .............................................................................. 34
Table 15. Mean (SD) sessional (1&2) CoV (%) and p values for Time of Force Application resultant and component (Front & Rear) forces inter-‐session across all athletes (N=10) ......................................................................................................................... 35
Table 16. Force–time characteristic CoV (%) ranges from literature and current study .................................................................................................................................................... 40
vi
List of Appendices 7 Appendices ......................................................................................................................................... 52 7.1 Appendix A ................................................................................................................................. 52 7.2 Appendix B ................................................................................................................................. 57 7.2.1 Par-‐Q Form ......................................................................................................................... 57 7.2.2 Informed Consent Form ................................................................................................ 58
7.1 Appendix C: Subject-‐specific data .................................................................................... 60 7.1.1 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean pre-‐tension magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions .............................................................................................................................................. 60 7.1.2 M01-‐M05 & S01-‐S05 single-‐start and mean reaction time magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions 66 7.1.3 M01-‐M05 & S01-‐S05 single-‐start and mean block time magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions 67 7.1.4 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean RFD from ‘Go’ magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions .............................................................................................................................................. 68 7.1.5 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean RFD from onset magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions ................................................................................................................................... 74 7.1.7 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean peak force magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions .............................................................................................................................................. 80 7.1.8 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean impulse magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions .............................................................................................................................................. 86 7.1.10 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean TTP from ‘Go’ magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions ................................................................................................................................... 92 7.1.12 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean TTP from onset magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions ................................................................................................................................... 98 7.1.14 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean time of force application magnitudes, unfilled black circles represent starts from session 1;
vii
filled black circles represent starts from session 2; red circle represents means from both sessions ..................................................................................................................... 104 7.1.16 M01-‐M05 & S01-‐S05 single-‐start and mean force ‘Off’ time offset magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions ........................................................................................................................................... 110 7.1.18 M01-‐M05 & S01-‐S05 single-‐start and mean peak force time offset magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions ........................................................................................................................................... 111
1
1 Introduction
A primary performance objective of any race is to cover the required distance
in the time needed to win or place (qualify). A sprint race is made up of four
component phases: reaction time phase; block start phase; acceleration phase; and
maintenance phase (Tellez, 1984). Each phase is dependent on performance in the
phase immediately preceding it, as the prior phase dictates the athlete’s starting
body configuration in the subsequent phase. Force–time characteristics associated
with ‘good’ performance in each of these phases have been extensively researched
and debated (Harland & Steele, 1997; Majumdar & Robergs, 2011), none more so
than those of the block start phase.
Five percent of total 100m race time can be accounted for by the block start
phase duration; however, due to its influence on the subsequent acceleration phase,
64% of total race time is dependent on block start duration (Tellez, 1984). The
International Association of Athletics Federation (IAAF) mandates that an official
provides two verbal cues (“on your mark” and “set”), followed by a loud gunshot
from a starting pistol, to indicate the start of a race (IAAF, 2014-‐2015). Three
quantities characterize the duration of the block start: reaction time; movement
time; and response time. Reaction time (RT) begins when the gunshot sounds, and
ends when movement is initiated. Movement time begins when movement is
initiated, and ends when the athlete is no longer in contact with the blocks.
Response time consists of both reaction and movement times combined (Eikenberry
et al., 2008). Minimizing these times is important to the success of the start (Brown,
Kenwell, Maraj, & Collins, 2008). However, due to the influence of the start on the
rest of the race, an athlete’s actions during the start, and more importantly how the
athlete interacts with the blocks, are considered vital to a successful race outcome
(Mero, Kuitunen, Harland, Kyrolainen, & Komi, 2006).
During the start phase, athletes make use of starting blocks (Figure 1). As per
IAAF (2014-‐2015) rules,
2
The starting blocks shall consist of two foot plates, against which the athlete’s
feet are pressed in the starting position. The foot plates shall be mounted on
a rigid frame […]. The foot plates shall be sloped to suit the starting position
of the athlete, and may be flat or slightly concave. The surface of the foot
plates shall accommodate the spikes in the athlete’s shoes, either by using
slots or recesses in the face of the foot plate or by covering the surface of the
foot plate with suitable material permitting the use of spiked shoes. (p. 154)
Athletes exert forces against the foot plates to accelerate the whole-‐body
centre-‐of-‐mass upward and forward. Most elite level sprinters today use a standing
staggered start position, with a moderate to long anteroposterior distance (i.e., at
least one lower leg [shank] length apart between foot plates (Menely & Rosemier,
1968)). This allows athletes to continue to exert force on the front foot plate after
their rear foot has left the blocks. This produces a longer duration of force
application, and thus a greater change in velocity (Mero et al., 2006; Salo & Bezodis,
2004).
Figure 1. Starting Block Components
3
Numerous characteristics of the interactive forces between the feet and
blocks have been compared across sprinters to identify potential performance
determinants. Based on 100m personal best times, athletes have been ranked and
categorized as world-‐class vs. fast vs. slow (Willwacher, Herrmann, Heinrich, &
Bruggemann, 2013), elite vs. sub-‐elite (Fortier, Basset, Mbourou, & Faverial, 2005),
elite vs. well-‐trained (Slawinski et al., 2010), top vs. middle vs. lower class sprinters
(Baumann, 1976) and skilled vs. less-‐skilled (Gagnon, 1978). If significant between-‐
group differences in force–time characteristics are found, it is reasoned that these
characteristics are important for performance. Fortier et al. (2005) found that there
was a significant difference between reaction times between elite
(RT=10.46±0.11ms) and sub-‐elite (RT=11.07±0.3ms) athletes. However, these
findings are not consistent with results of other research, wherein reaction times
were not different between faster and slower sprinters (Baumann, 1976; Coh, Jost,
Skof, Tomazin, & Dolenec, 1998; Slawinski et al., 2010; Willwacher et al., 2013).
Similar discrepancies are noted in comparisons made between faster and slower
sprinters in block time (BT), impulse, and rate of force development (RFD), time-‐to-‐
peak force (TTP), time of force application (TFA), and time between right and left
foot peak forces and time between right and left force offset (Baumann, 1976; Coh et
al., 1998; Fortier et al., 2005; Gagnon, 1978; Mero, Luhtanen, & Komi, 1983;
Slawinski et al., 2010; Willwacher et al., 2013). Despite mechanical rationale for
monitoring these characteristics, findings suggest that there is not a single block
start force–time characteristic that determines ranking across all athletes and races
(Bezodis, Salo, & Trewartha, 2010). Further, in other locomotion tasks, it has been
recognized that analyses of discrete variables does not necessarily capture the
complexity of biomechanical waveforms (Bartlett, Wheat, & Robins, 2007).
Disparate findings regarding kinetic characteristics of the start suggest a similar
notion: it is more likely that the relative importance of any single force–time
characteristic to total race time is athlete-‐ and context-‐dependent; thus variation in
such characteristics could be expected.
4
Although there is mechanical rationale to support the importance of
consistent force production in the sprint start (Chow & Knudson, 2011), intra-‐
individual variability in force–time characteristics is expected given that sprint
starts are complex movements involving many biomechanical degrees-‐of-‐freedom.
It is perhaps the variable nature of the transition from a static to dynamic state
during the sprint start that affords elite performers the flexibility and adaptability
necessary to consistently achieve high outcome standards across various conditions
(Glazier & Davids, 2009). To date, there has been no description of the typical
amount of variation in the abovementioned force–time characteristics during the
block start phase of the sprint. Through quantifying the variability in force–time
characteristics of the block start phase, athletes, coaches, and scientists alike will be
better able to detect and interpret repetition-‐to-‐repetition and day-‐to-‐day changes
in force–time characteristics, as it provides the information necessary to calculate
effect sizes, conduct statistical power analyses, design studies, etc.. The objective of
this thesis was to quantify the within-‐athlete variation of force–time characteristics
in the block start phase that have been linked to sprint performance (i.e., total race
time). A secondary objective was to compare these characteristics between training
sessions. It was hypothesized that athletes would exhibit variability within-‐session,
with the same amount of variability from session-‐to-‐session in force–time
characteristics that are related discriminative between groups of differently-‐skilled
athletes.
5
2 Review of Literature
Sprint start performance is typically defined/ranked based on personal best
(PB) times in order to identify differentiating characteristics between groups. A
nearly infinite number of characteristics are quantifiable from a continuous force–
time curve. Discrete force–time measures offer a convenient summary of a complex
signal. Although few studies have repeatedly identified the same discrete force–time
variables as differing between groups, some have identified significant differences
between groups in various force–time characteristics. These characteristics will be
considered in this section, in order to narrow the scope of variables deemed
important to the race. Inconsistent findings at the group level pose a problem in
assessing the scope of a meaningful improvement in these characteristics on the
individual level during training. This section will briefly review the force–time
characteristics that have been found to be discriminative indicators of performance
in the sprint start and consider the implications and assumptions of each, informing
the selection and evaluation of variables analyzed in this thesis. Appendix A
provides additional details and results of the studies reviewed below, as well has
how athletes were categorized in each.
2.1 Pre-‐tension (PT)
Pre-‐tension has been defined as the amount of horizontal force, with respect
to the ground, applied on the blocks in the set position (Van Coppenolle, Delecluse,
Goris, Bohets, & Vanden Eynde, 1989). Mechanically, it’s regarded as a way to
preload the lower extremity extensor muscles prior to action to produce a more
forceful concentric contraction (Guissard, Duchateau, & Hainaut, 1992). Van
Coppenolle et al. (1989) reported pre-‐tension values of 20-‐88N for the front foot,
and 80-‐120N for the rear. Pre-‐tension forces are thought to contribute to the forces
produced after the ‘Go’ cue. Similarly, Baumann (1976) defined ‘spring tension’ as
horizontal forces applied by the hands and feet in the ‘set’ position. Athletes
6
producing high pre-‐tension values are thought to be in better position,
kinematically, to produce higher forces exiting the blocks.
A discrepancy exists however among these studies as to whether horizontal
hand forces are included in pre-‐tension calculations. Although multiple authors have
considered pre-‐tension to be an important factor in the start, no research to-‐date
has correlated pre-‐tension to race finish times. Further, some authors have included
hand forces in its calculation, while others have not. Pre-‐tension seems to be an
important consideration in the sprint start; however, not enough is known about the
impact of this characteristic, including intra-‐individual variability, to make definite
conclusions. Quantifying the day-‐to-‐day variability could provide some insight into
how to quantify a change in performance in response to monitor training
adaptations for example.
2.2 Reaction Time (RT)
Reaction time has been extensively studied as a function of the sprint start.
Generally, it is defined as the time it takes to initiate a response to the given
stimulus. In the sprint start, RT can be calculated as the time between the first
change in force and the sound of the start gun (Majumdar & Robergs, 2011). Fortier
et al. (2005) found a significant difference in average reaction times between elite
(RT = 0.1046±0.0001s) and sub-‐elite (RT = 0.1107±0.0003s) groups of athletes.
Slawinski et al. (2010) found the average RT of sprinters did not significantly differ
between elite (RT = 0.151±0.016s) and well-‐trained (RT = 0.158±0.033s) sprinters.
Others found that there was no significant difference in reaction times between any
of the following groups: world-‐class men (RT = 0.16±0.09s); fast men (RT =
0.18±0.04s); slow men (RT = 0.19±0.04s); fast women (RT = 0.2±0.02s); and slow
women (RT = 0.21±0.04s) (Willwacher et al., 2013). Baumann (1976) found no
difference in reaction time among three groups divided based on 100m PB times
(Group 1: 0.101±0.018s; Group 2: 0.099±0.015s; Group 3: 0.113±0.014s). Coh
(1998) also found a significant inverse correlation among men between RT and time
to 20m and 30m, as well as time to 10m in females.
7
Several factors play into calculating RT, many of which have not been
standardized across studies. The agreement among authors is that RT should be
minimized to improve performance. However, a false start is called when the
reaction time is less than 0.100 seconds (IAAF, 2014-‐2015) despite the fact that Pain
& Hibbs (2007) demonstrated that neuromuscular-‐physiological components of
simple auditory reaction times can be under 85ms and that EMG latencies can be
under 60ms. In IAAF (2014-‐2015) races, the starter is positioned closest to lane 1.
Research has indicated differences in reaction time as a function of the distance
between the athlete and the starting gun exist (Brown et al., 2008). Even when a gun
is rigged to avoid this issue and trigger loudspeakers that are positioned behind
each athlete’s starting blocks, athletes still react to the sound of the gun rather than
that of the loudspeaker (Lennart Julin & Dapena, 2003). In studies that considered
RT as a differentiator between athletes of different skill levels, the method of
starting was often not specified. Further, how a ‘reaction’ is discerned is also often
unspecified (Fortier et al., 2005; Willwacher et al., 2013). A common force
‘threshold’ value could lead to the same reaction yielding different reaction times
based on an individual athlete’s mass or pre-‐tension values (Pain & Hibbs, 2007).
Coh et al. (1998) defined a reaction as force exceeding 10% of maximal force
attained by a given athlete. Baumann (1976) also reported that a significant
difference between the rear and the front foot reaction time exists in sprinters of
many expertise levels. Thus, it is important to mind these specifications when
considering RT variation.
Another important factor in calculating RT is the method for detecting the
reaction. Again, it is often unreported. In some studies, Microgate technology was
used, with block sensors detecting reaction times (Slawinski et al., 2010). In others,
it is clear it was done using some sort of pressure sensor or force plate (Baumann,
1976; Fortier et al., 2005). For example, Fortier et al. (2005) defined reaction time
as the time from the gun signal to the first detectable change of force in the
instrumented blocks. However, in many studies, this information is not provided
and thus the calculation method of RT is unclear, leading to potential
(non)significant differences being detected in RT. Thus, due to the inconsistent
8
methods in calculating RT between studies, it is not unexpected that findings
pertaining to the significance of RT are varying.
As such, inconsistencies in reaction time measurement could also lead to
discrepancies in other force–time measures that include reaction time in their
derivation (i.e., block time) (Willwacher et al., 2013).
2.3 Block Time (BT)
Block time was found by Willwacher et al. (2013) to be significantly different
between world-‐class males (BT = 0.34±0.02s) and other males (fast: BT =
0.39±0.03s; slow: BT = 0.4±0.02s) and female (fast: BT = 0.39±0.03s; slow: BT =
0.43±0.03s) counterparts. Fortier et al. (2005) also found block times to be
significantly different between groups of differing ability (elite: BT = 0.37±0.018s;
sub-‐elite: BT = 0.405±0.04s). However, Baumann (1976) (group 1: BT =
0.47±0.036s; group 2: BT = 0.468±0.02s; group 3: BT = 0.504±0.032s), Mero et al.
(1983) (group A: BT = 0.361±0.027s; group B: BT = 0.36±0.023s; group C:
0.368±0.037s) and Slawinski et al. (2010) (elite: BT = 0.352±0.018s; well-‐trained:
BT = 0.351±0.02s) did not. This start characteristic ends when the athlete exits the
blocks. However, the onset of block time measurement is inconsistent among
authors. Some consider block time to begin when the ‘go’ signal is triggered, while
others consider it to begin at force onset, in which case RT is included in block time
(Willwacher et al., 2013). RT notwithstanding, ‘force onset’ is defined differently
among studies. Slawinski et al. (2010) delimited block time from the first movement
in the set position to block clearance; however, it is unclear whether the first
movement was detected by visual inspection of video data, force data, or through
Microgate technology. Mero et al. (1983) instead calculated duration of force
production providing a more specific method of calculation. In either case, a block
time ‘initiation’ threshold could also provide more insight to why discrepancies in
findings between studies exist. Though block time has been found to be a factor in
race success at the group level, it is unknown how consistently achievable this
characteristics is on the individual level.
9
2.4 Rate of Force Development (RFD)
Rate of force development has been cited as an important variable associated
with the sprint start (Buhrle, Schmidtbleicher, & Ressel, 1983). Values of up to
15505N/s have been found among elite sprinters, while well-‐trained sprinters
achieved average rates of force development of about 8459N/s. It has been
determined to be a variable of interest, as elite athletes achieved significantly higher
values as compared to well-‐trained athletes (Slawinski et al., 2010). Mass
normalized RFD in the front foot has also been found to be higher in world-‐class
male sprinters (RFD = 237.37±75.31N/kg/s) as compared to their male and female
fast (male RFD = 137.48±47.72N/kg/s; female RFD = 132.21±41.29N/kg/s) and
slow (male RFD=122.64±41.73N/kg/s; female RFD = 109.38±45.61N/kg/s)
counterparts (Willwacher et al., 2013). Coh (1998) also found absolute and relative
front foot RFD to be strongly inversely correlated to time to 20m (r = -‐0.71, p < 0.05)
and 30m (r ≥ -‐0.76, p < 0.05), in elite male sprinters. Thus, RFD seems to be an
important predictor of success in a race.
Interestingly, the within-‐group coefficient of variation among these
published RFD values is anywhere between 28% and 54%. RFD is a force–time
characteristic made up of components, often delimited differently between
researchers. Among authors, it is defined as a change of force in a unit of time (Coh
et al., 1998). How that time is defined, however, differs amongst authors. Further,
authors often use different force component signals to calculate RFD. Willwacher
(2013) considered the body-‐mass normalized resultant (3-‐dimensional) force from
the rear and the front foot separately. Although unreported, Slawinski (2010)
seemingly calculated RFD from the absolute resultant force from both feet
combined. Coh (1998) also did not report the source of his RFD calculation. Thus,
although RFD seems to be an important sprint start performance characteristic, it is
unclear how it can be routinely monitored to assess something like training
adaptations if its calculation is so inherently variable. For this reason, this study will
calculate RFD in two ways: from the ‘Go’ signal; as well as from force onset.
10
Component forces in the horizontal and vertical directions, as well as two-‐ and
three-‐dimensional resultant forces will be analyzed both uni-‐ and bi-‐laterally from
the front and rear foot blocks.
2.5 Peak Force (PF)
Another characteristic of the sprint start that has been heavily researched is
peak force magnitude. It is primarily considered to be the maximal instantaneous
force value produced by an athlete during the block phase (Fortier et al., 2005).
Mechanically, producing a high peak (reaction) force in the horizontal direction
would afford the athlete with a large instantaneous horizontal acceleration. Skilled
sprinters have been reported to produce almost 65% more force in their rear foot
than their less skilled counterparts, and 69% more in their front foot (Gagnon,
1978). This supports the abovementioned finding of a significant correlation
between peak front foot force and time to 20 and 30m in elite male sprinters (Coh et
al., 1998). Baumann (1976) and Mero et al. (1983) also found that faster athletes
produced a larger peak horizontal acceleration (PHA) (group 1: PHA = 15.4±2m/s2;
group 2: PHA = 13.2±1.7m/s2; group 3: PHA = 12.2±2.4m/s2) and force (group A: PF
= 1186±260N; group B: PF = 1154±170N; group C: PF = 898±203N), respectively,
out of the blocks than slower athletes. Other studies have reported significant
differences between elite (PFrear = 1430±431N) and sub-‐elite (PFrear = 940±255N)
sprinters as well as men (PFfront = 16.14±1.45N/kg; PFrear = 15.98±2.57N/kg) and
women (PFfront = 14.46±2.68N/kg; PFrear = 11.36±0.29N/kg) in resultant peak forces
in the front and the rear (Fortier et al., 2005; Willwacher et al., 2013). Overall most
studies have concluded that peak force production is important, with some studies
focusing on peak horizontal force as a marker of performance potential. However,
findings have not been unanimous among studies.
11
2.6 Impulse
Impulse is the integral of the force–time curve, made up of the product of the
applied force on the blocks and the duration of application of the force (Coh et al.,
1998; Slawinski et al., 2010). Mechanically, an athlete producing a large impulse
will, in accordance with Newton’s second law of motion, experience a greater
change in momentum, and thus have a larger velocity when leaving the blocks.
Significant correlations between absolute and body-‐mass normalized front foot
impulse and time to 20m and 30m have been reported (Coh et al., 1998), while
others have found that on average, skilled sprinters produce larger impulses than
less-‐skilled sprinters in the rear foot (77.7Ns; 51.5Ns) and overall (168.7Ns;
135.2Ns), with a smaller difference between groups in the front foot (86.33Ns;
83.68Ns) (Gagnon, 1978). Mero et al. (1983) found some differences between faster
sprinters and their slower counterparts in both vertical (group A: Impulse =
231±31Ns; group B: Impulse = 221±55Ns; group C: Impulse = 178±43Ns) and
horizontal (group A: Impulse = 234±15Ns; group B: Impulse = 226±31Ns; group C:
Impulse = 195±23Ns) impulse in the blocks. Considering resultant forces, Slawinski
(2010) also found a significant difference between elite (Impulse = 276.2±36Ns) and
well-‐trained (Impulse = 215.4±28.5Ns) sprinters’ block impulses. And, although
Baumann (1976) found faster athletes (Impulse = 263±22Ns) to produce larger
horizontal impulses than their slower counterparts (group 2: Impulse = 223±20Ns;
group 3: Impulse = 214±20Ns) on average, no significant relationship was found.
Once again, although the trend seems to be toward block impulse being an
important consideration for performance, the strength of the relationship is
unknown with disagreement between studies as to which foot or force direction is
important. Further, as faster sprinters have been purported to produce shorter
block times, the balance between block time and force magnitude is not considered
when measuring force impulse as a performance characteristic. Thus, monitoring
only this characteristic in training might lead to valuable information about the start
being missed.
12
2.7 Other Force–Time Characteristics
Other temporal characteristics like time to peak force (TTP), delay between
rear and front force peaks (PFO), onsets and offsets (FOTO) have been studied (Coh
et al., 1998; Fortier et al., 2005; Mero et al., 1983), with significant differences found
between elite (100mPB=10.46±0.11s) and sub-‐elite (100mPB=11.07±0.30s)
sprinters in time to rear peak force (elite = 124±17ms; sub-‐elite = 119±20ms), delay
between end of rear and front force offsets (elite = 140±26ms; sub-‐elite =
173±23ms) (Fortier et al., 2005). Coh et al. (1998) also found a correlation between
time to peak force in the front and rear feet and time to various distances, while
others found no relationship between 100m PB times and time to peak force (Mero
et al., 1983). Inconsistent findings exist yet again for these other force–time
characteristics, not only due to differing populations but also due to discrepancies in
methods.
2.8 Summary
Although differences in skill-‐level group division and methods of force–time
characteristic calculations could account for inconsistencies in findings, upon
calculating coefficients of variation for group averages (Appendix A), it is unknown
how much individual variation in performance can account for variability in
findings. Bezodis et al. (2010) studied how block start characteristics correlated to
times and velocities at varying distances. Even with body size crudely accounted for
in normalized power data, the authors found that none of the 10 measures ranked
all of the sprinters in the same order. They concluded that although the block start
does influence the rest of the race, “…performance should ideally be quantified
during just the phase over which technique is analyzed, allowing the observed
performance levels to be directly attributed to the observed techniques.”(p. 266)
From the above review, it is clear that certain force–time characteristics hold
important consideration for sprint athletes looking to improve their start. However,
no characteristic has unanimously been found to correlate with improved sprint
race outcomes. When monitoring these characteristics during training, an analysis
13
of an athlete’s variability could help coaches understand the importance of block
start force–time characteristics in relation to one another within the start. With the
aim of quantifying the within-‐athlete variability of force–time characteristics in the
block start that have been linked to performance in sprint athletes, it is possible that
although athletes are achieving values associated with ‘good’ performance in many
of these characteristics, the interactivity between them may not be identical every
start, especially considering the biomechanical complexity of the sprint start task.
The abovementioned characteristics generally have statistical importance to
the race. Mechanically, the importance of these characteristics are also deducible
from Newton’s Second Law of Motion. In order to maximize horizontal velocity at
block exit, one must apply a high magnitude horizontal force. However, a vertical
force is also necessary to prevent the body from collapsing. Theoretically, net lateral
(side-‐to-‐side) forces should be minimized to propel the body up and forward when
exiting the blocks. Thus, in this study, discrete force–time characteristics from the
horizontal (Fy) and vertical (Fz) component forces, as well as their two-‐dimensional
resultant (2F), and the three-‐dimensional resultant (3F). Further, each foot will be
analyzed individually, and two-‐ and three-‐dimensional forces will be analyzed from
both the rear and the foot combined (Table 3).
14
3 Methods
3.1 Study Overview
In order to obtain ecologically valid training data, all data collections took
place at scheduled team technical training sessions1 under coach supervision, and
alongside teammates. Athletes performed 4 maximal sprint starts using force plate-‐
instrumented (FP-‐instrumented) start blocks at 2 different training sessions, 1 week
apart. Training sessions were administered with as little interference as possible in
that the primary investigator did not alter or interfere with the coach’s scheduled
training plans. The sessions were coach-‐led with the exception of ‘Go’ cues, which
were provided by the primary investigator. Starts were performed with teammates
starting alongside participants, as dictated by the coach. The blocks used were, from
the athlete’s perspective, identical to those used in daily training sessions (i.e., all
the same block configuration adjustments could be made, see Figure 2).
3.2 Participants
Ten intercollegiate athletes (5 men, 5 women) were recruited with coach’s
permission during their “pre-‐competition”2 phase of training. Athletes were
between 19-‐30 years of age (Table 1). Prior to participating in the study, athletes
were required to complete the physical activity readiness questionnaire (PAR-‐Q) to
ensure that they were free of any health issues that could have altered performance
or been exacerbated as a result of participation in the study (Appendix B, section
9.2.1) as well as an informed consent form (Appendix B, section 9.2.2). This study
received approval from the University of Toronto Research Ethics Board for
research involving human participants. Participant demographics, anthropometrics,
1 Technical training sessions are those in which an athlete’s technique is practiced under the
2 Pre-‐competition phase is defined as Phase 3 by Warden (1988) involving development of competition-‐specific conditioning, early competitions including possible selection for major competition performance, and evaluation of early competition performance.
15
and season-‐specific time to 30m testing data (FreeLap USA, Pleasanton, CA) are
listed in Table 1.
Table 1. Participant demographics and anthropometrics n Age (yrs) Mass (kg) Height (cm) Time to 30m
(s) Men 5 21.8±1.9 76.5±7.1 181±7.1 3.1±<0.1
Women 5 23.4±4.2 61.9±4.1 169±5.6 3.6±0.1 Overall 10 22.6±3.2 69.2±9.4 175±8.6 3.34±0.2
3.3 Data Collection
Upon arrival to technical training sessions, athletes performed their coach-‐led
warm-‐up as they would at typical training sessions. Athletes were then instructed
by the primary investigator to adjust their blocks to their personal preference
settings. Adjustable settings included front foot to start line (F_SL) and rear foot to
start line (R_SL) distances as well as foot plate angles (θF, θR) (Figure 2). Once
athletes felt comfortable with their configurations, these athlete-‐specific settings
were recorded (Table 2) and remained unchanged for the duration of the study.
Each athlete was provided with at least one familiarization trial to ensure
satisfaction with his or her block settings and to become acquainted with the ‘go’
cue. The primary investigator called the ‘on your mark’ and ‘set’ cues. The ‘go’ cue
came in the form of an audio file that was played and temporally synched to force
data through a digital switch (operated by the principal investigator). Athletes
performed 4 maximal starts at 2 separate training sessions, 1 week apart, for a total
of eight starts to 30m. Rest between starts was coach-‐determined (3-‐5 minutes
between starts), as to not interfere with the daily training schedule.
16
Figure 2. Adjustable block settings: plate angles (θF, θR) and front foot to start line (F_SL) and rear foot to start line (R_SL) distances.
Table 2. Participant block configurations Subject Foot Plate Angle Block Position
θF θR F_SL R_SL Front Foot
(deg) (deg) (cm) (cm) (R,L) M01 50 60 54.7 84.5 R M02 50 60 53.6 78.1 L M03 50 50 54.7 84.5 R M04 40 50 54.7 84.5 R M05 50 50 55.2 81.0 L S01 50 60 41.5 59.5 L S02 50 60 51.0 71.5 L S03 50 50 48.4 64.1 L S04 50 50 52.6 77.1 R S05 40 50 53.1 79.0 L
17
Force plates (AM600 series, Bertec Corporation, Columbus, OH) mounted into
custom-‐built start blocks (for Own The Podium3, Vancouver, Canada) were used for
the duration of the study. Using the Bertec pin-‐out, ±5 V full-‐scale calibrated analog
output per rated load range for each of the six force plate channels output from each
plate (Fx, Fy, Fz, Mx, My, Mz) were digitized using an internal digital preamplifier,
which digitized the analog signal from the transducer strain gauges, and conditioned
it through oversampling, preliminary amplification, and filtering. The output of the
force plate is a 16-‐bit digital signal using RS-‐485 format. Pre-‐conditioned signals
were then imported via an analog-‐to-‐digital converter (NI USB-‐6210, National
Instruments, Austin, TX) sampling at 1000Hz into to a custom-‐written LabView
(National Instruments, Austin, TX) program. Force plates were hardware zeroed
prior to each trial. A digital switch was also instrumented to temporally synchronize
an audible ‘go’ cue with the digitized force platform data. Approximately 45% of the
voltage range (2.2V/5V) was used throughout collections.
3.4 Signal Conditioning and Processing
Data were conditioned and processed using Visual3D™ software (V5, C-‐Motion
Inc., Germantown, MD). Voltage outputs were converted to forces (N) by multiplying
by 600N/V (as per Bertec specifications), and then by -‐1 to obtain foot-‐block
reaction forces. Using vector algebra, signals were combined to create resultant
forces in two dimensions (planar: antero-‐posterior [Y] and vertical [Z] directions)
and three dimensions (spatial: medio-‐lateral [X], [Y] and [Z] directions) for both the
front and the rear feet, as well as both feet combined. Signal nomenclature is
specified in Table 3.
3 Form more information about the organization, please visit: http://ownthepodium.org/
18
Table 3. Derived signals for analysis Foot Signal Nomenclature Calculation
Rear Fx R_Fx N/A Fy R_Fy N/A Fz R_Fz N/A Fy, Fz R2F 𝑅_𝐹!! + 𝑅_𝐹!!
Fx,, Fy, Fz R3F 𝑅_𝐹!! + 𝑅_𝐹!! + 𝑅_𝐹!!
Front Fx F_Fx N/A Fy F_Fy N/A Fz F_Fz N/A Fy, Fz F2F 𝐹_𝐹!! + 𝐹_𝐹!!
Fx,, Fy, Fz F3F 𝐹_𝐹!! + 𝐹_𝐹!! + 𝐹_𝐹!!
Rear & Front
Fy, Fz RES_2 (𝑅_𝐹! + 𝐹_𝐹!)! + (𝑅_𝐹! + 𝐹_𝐹!)!
Fx,, Fy, Fz RES_3 (𝑅_𝐹! + 𝐹_𝐹!)! + (𝑅_𝐹! + 𝐹_𝐹!)! + (𝑅_𝐹! + 𝐹_𝐹!)!
In order to decide on a low-‐pass filter cut-‐off frequency, a residual analysis
(Winter, 2009) was performed on data collected from 2 male and 2 female randomly
sampled athletes. End-‐points of R_Fy, F_Fy R2F, L3F and RES_3 signals were padded
using the reflection method outlined by Smith (1989) and signals were
subsequently reflected to create a periodic waveform. Signals were then low-‐pass
filtered using a zero-‐lag fourth-‐order (second-‐order dual-‐pass) Butterworth filter at
effective cutoff frequencies of 0 to 50Hz (at 5Hz increments) and then by increments
of 10Hz from 50 to 100Hz. Residuals were plotted. RES_3 was determined to have
the highest residual values and was used for the subsequent residual analysis.
Residuals were plotted and the optimal regression line was determined by fitting
linear regression lines at each incremental cutoff frequency. An optimal regression
line was determined when the change in R2 value fell below 0.01. The y-‐intercept
was then regressed back to the curve and the cut-‐off frequency value for that point
was determined as optimal. The four participants’ data all fell within 20-‐30Hz as
19
‘optimal’ cut-‐off frequencies (Figure 3). Thus, in order to remain conservative and
consistent, force data were low-‐pass filtered with an effective cutoff frequency of
50Hz.
(a)
(b)
(c)
(d)
Figure 3. Residual Analyses of RES_3 signal for two male (a) (b) and two female (c) (d) participants
3.5 Data Analyses
Once data were low-‐pass filtered at 50Hz, events were defined for each of the
following signals: R_Fy; R_FZ; R2F; R3F; F_Fy; F_FZ; F2F; F3F; RES_2; and RES_3
(Table 4, Figure 4).
20
Table 4. Signal Events
Event Definition Go Defined as the ‘switch’ channel exceeding 4.5V. At this point, the ‘Go’
cue was played through speakers. Pre-‐Go Defined as 0.5 seconds before the ‘Go’ event. Created in order to
establish the magnitude of fluctuations in force application between the ‘Set’ and ‘Go’ cues. This was later used to define force onset.
Peak Defined as the global peak force in the signal between ‘Go’ and ‘Force Offset’.
Force Onset Defined as the first increase in force exceeding 3 standard deviations of the mean force magnitude applied between ‘Pre-‐Go’ and ‘Go’.
Force Offset
Defined as the first instance at which forces returned to 0N, after ‘Peak’.
Block Exit Defined as front foot ‘Force Offset’. At this point, athlete had left the blocks.
Figure 4. Graphical depiction of signal events
Events were defined for the purpose of calculating various temporal and
kinetic variables in the sprint start. Signal-‐specific variables were calculated for each
of the following signals: R_Fy; R_FZ; R2F; R3F; F_Fy; F_FZ; F2F; F3F; RES_2; and RES_3
(Table 5, Figure 5). Following this, between-‐feet variables were calculated between
front and rear feet for the following signals: Fy; Fz; 2F; and 3F (Table 6, Figure 6).
Finally, overall variables were calculated from data describing the entire start
(Table 7, Figure 7).
21
Figure 5. Signal-‐specific variables
Table 5. Signal-‐specific variables Variable Definition Formula
Pre-‐Tension Average force between ‘Pre-‐go’ and ‘Go’
𝐹!"!"#!!"
𝑡!" − 𝑡!"#!!"
Peak Force Magnitude of force at ‘peak’ event
N/A
RFD_GO Linear derivative of the force–time curve from ‘Go’ to ‘peak’ events
𝐹!"#$ − 𝐹!"𝑡!"#$ − 𝑡!"
RFD_ON Linear derivative of the force–time curve from ‘force onset’ to ‘peak’ events
𝐹!"#$ − 𝐹!"#$%𝑡!"#$ − 𝑡!"#$%
Time of Force Application
Time between ‘force onset’ to ‘force offset’
𝑡!""#$% − 𝑡!"#$%
TTP_GO Time between ‘Go’ to ‘peak’ events
𝑡!"#$ − 𝑡!"
TTP_ON Time between ‘force onset’ to ‘peak’ events
𝑡!"#$ − 𝑡!"#$%
Impulse Area under the force time curve from ‘force onset’ to ‘force offset’ events (trapezoidal rule)
𝐹 𝑥 𝑑𝑥!!!"#$
!"#$%
≈ 𝑡!""#$% − 𝑡!"#$% [𝐹 𝑜𝑛𝑠𝑒𝑡 + 𝐹(𝑜𝑓𝑓𝑠𝑒𝑡)
2]
22
Figure 6. Between-‐feet and overall variables
Table 6. Between-‐feet variables Variable Definition Formula
Peak Force time Offset
Time between front and rear foot ‘peak’ forces
𝑡!_!!"#$ − 𝑡!_!!"#$
Force Off time Offset
Time between front and rear foot ‘force offsets’
𝑡!_!!""#$% − 𝑡!_!!""#$%
Table 7. Overall variables Variable Definition Formula
Reaction Time Time between ‘Go’ and ‘force onset’
𝑡!"#$% − 𝑡!"
Block Time Time in the blocks (i.e., time between ‘Go’ and ‘force offset’)
𝑡!""#$% − 𝑡!"
23
3.6 Statistics
Considering the first study objective, athlete’s intra-‐session means and
standard deviations were computed for each force signal’s force–time variable (4
starts per session per athlete). Using these values, sessional coefficients of variation
(CoV) for each variable were then derived by dividing the standard deviation by the
mean, and multiplying by 100%. CoVs were then averaged across athletes to
compare relative variation between characteristics. Following this, for the second
objective, force–time variables’ means and CoVs were compared inter-‐session, using
general linear model analyses of variance (ANOVAs) with one within-‐participant
factor (session). Alpha levels were set a priori at 0.05; p-‐values less than 0.05 were
considered to be statistically significant. All statistical analyses were done using SAS
system software (version 9.3.1, TS1M2; SAS Institute Inc, Cary, NC, USA).
24
4 Results
Considerable variation was observed in the magnitudes of variables associated
with all ten force–time characteristics analyzed, both within and between sessions
(within athletes). Of the ten force–time characteristics, only three had means that
were not significantly different (p < 0.05) inter-‐session: pre-‐tension; reaction time;
and force ‘Off’ time offset. At least one athletes exhibited intra-‐session CoVs of
greater than 50% in variables associated with seven of the ten force–time
characteristics. Only block time and force ‘off’ time offset were found to have no
CoVs above 50% at the individual level. Larger CoVs were less frequently found at
the individual level in force impulse, peak force and pre-‐tension, while more
frequently found in rate of force development, time of force application, time to
peak, and reaction time. Specific results of analyses are reported below. Data for
individual athletes are included in Appendix C.
4.1 Pre-‐Tension
Resultant pre-‐tension forces (RES_2 & RES_3) were qualitatively more stable
(6.2% ≤ CoV ≤ 8.4%) than were their components (F_Fy, F_Fz, R_Fy & R_Fz) (12.4%
≤ CoV ≤ 62.3%) (Table 8). There was considerable variation in pre-‐tension forces
across individual athletes (Appendix C), but there were no inter-‐session differences
in any of the pre-‐tension force means (p ≥ 0.1486) (Figure 7) or their CoVs (p ≥
0.1040) at the group level.
25
Table 8. Mean (SD) sessional (1&2) CoV (%) and p values for pre-‐tension resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10).
Signal Session 1 Session 2 p F_Fy 36.9 (38.2) 25.6 (19.0) 0.3527 F_Fz 17.4 (14.2) 12.4 (11.2) 0.1040 F2F 16.9 (14.6) 11.9 (11.4) 0.1957 F3F 17.0 (15.0) 11.9 (11.4) 0.211 R_Fy 33.3 (41.5) 62.3 (111.0) 0.2526 R_Fz 12.6 (5.0) 11.5 (6.3) 0.6157 R2F 13.0 (4.8) 12.3 (5.9) 0.7732 R3F 12.0 (4.2) 12.5 (5.8) 0.8193 RES_2 6.2 (2.9) 8.3 (6.0) 0.3352 RES_3 6.2 (2.9) 8.4 (5.9) 0.3083
Figure 7. Pre-‐tension resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation.
26
4.2 Reaction Time
Reaction times (standard deviation) CoVs were between 22.3 (8.5)% and 33.9
(5.5)%, with four of ten athletes producing more consistent intra-‐session reaction
times (CoV ≤ 50%) than others. Five of 10 athletes had less than a 5% inter-‐session
change in CoVs. No significant inter-‐session differences were found in mean
reaction times (p = 0.5274) or CoVs (p = 0.2488) (Figure 8).
Figure 8. Reaction times (s) compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation.
4.3 Block Time
Block times achieved were stable, with mean (standard deviation) intra-‐
session CoVs being 3.8 (1.8)% for session 1 and 6.3 (7.6)% for session 2; only one
athlete’s CoV exceeded 10% intra-‐session. No significant differences were found in
inter-‐session CoVs (p = 0.3887). Although athletes exhibited low levels of variation
in block times intra-‐session, block times were significantly different inter-‐session (p
= 0.044), with athletes achieving different ranges of BTs at both sessions (Figure 9).
27
Figure 9. Block times (s) compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. * indicates that mean values were significantly different inter-‐session (p < 0.05).
4.4 Rate of Force Development (RFD)
RFD CoVs from ‘Go’ (Table 9) as well as from force onset (Table 10) are
presented below. CoVs were significantly different (p < 0.05) inter-‐session for
multiple force signals, regardless of the method used to calculate RFD (Tables 9 and
10). However, the two methods of RFD calculations yielded RFD values that were
different in magnitudes (Figures 10 and 11) and in CoVs (Tables 9 and 10). When
reaction time was included in RFD calculations, mean (standard deviation) intra-‐
session CoVs between 6.9 (5.4)% and 19.1 (20.2)% were found, whereas when RT
was excluded, CoVs were between 14.0 (7.8)% and 51.4 (33.2)%. Thus, more
variance was detected when considering RFD from force onset (one athlete’s intra-‐
session CoV>50%), as compared to RFD from ‘Go’ (seven athletes’ intra-‐session
28
CoV>50%). No significant inter-‐session difference in mean RFD was found for either
method of RFD calculation (p ≤ 0.1615).
Table 9. Mean (SD) sessional (1&2) CoV (%) and p values for RFD from ‘Go’ resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10).
Signal Session 1 Session 2 p F_Fy 16.3 (18.4) 6.9 (5.4) 0.0764 F_Fz 16.8 (14.9) 9.7 (6.2) 0.1541 F2F** 19.0 (20.3) 10.0 (10.8) 0.0462 F3F** 19.1 (20.2) 10.0 (10.8) 0.0457 R_Fy** 16.5 (7.6) 10.2 (6.4) 0.0053 R_Fz 17.0 (11.4) 14.0 (7.8) 0.1498 R2F** 16.7 (9.5) 11.7 (7.0) 0.0167 R3F** 16.7 (9.5) 11.7 (7.0) 0.0176 RES_2 10.2 (4.5) 9.3 (6.0) 0.7537 RES_3 10.2 (4.5) 9.3 (6.0) 0.7606
** Significant inter-‐session difference in group-‐level CoV (p < 0.05)
Figure 10. RFD from ‘Go’ resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation.
29
Table 10. Mean (SD) sessional (1&2) CoV (%) and p values for RFD from force onset resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10).
Signal Session 1 Session 2 p F_Fy** 40.3 (22.0) 21.4 (14.4) 0.0159 F_Fz 17.0 (11.4) 14.0 (7.8) 0.1388 F2F** 42.1 (23.9) 21.5 (14.3) 0.0146 F3F** 42.2 (23.8) 21.5 (14.3) 0.0145 R_Fy 33.1 (31.1) 26.6 (31.1) 0.6372 R_Fz 38.1 (32.2) 29.5 (30.0) 0.5316 R2F 34.3 (31.9) 28.7 (29.9) 0.6696 R3F 34.3 (31.9) 28.6 (29.9) 0.6687 RES_2 51.4 (33.2) 34.5 (31.9) 0.1619 RES_3 51.4 (33.2) 34.5 (31.8) 0.1623
** Significant inter-‐session difference in group-‐level CoV (p < 0.05)
Figure 11. RFD from force onset resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation.
30
4.5 Peak Force
Mean (standard deviation) intra-‐session peak force CoVs between 5.1 (5.1)%
and 14.5 (18.9)% were found (Table 11), with only one athlete exhibiting an intra-‐
session CoV greater than 50%. A significant difference in inter-‐session CoVs was
found in rear foot horizontal force (p = 0.0233) (Table 11). No inter-‐session
significant differences (p > 0.1083) in mean peak force values were found (Figure
12).
Table 11. Mean (SD) sessional (1&2) CoV (%) and p values for peak force resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10).
Signal Session 1 Session 2 p F_Fy 12.7 (17.4) 5.1 (5.1) 0.1159 F_Fz 12.4 (12.3) 7.7 (6.1) 0.2309 F2F 14.5 (18.9) 8.2 (12.6) 0.0772 F3F 14.5 (18.8) 8.3 (12.5) 0.0790
R_Fy** 14.1 (7.5) 10.4 (7.9) 0.0233 R_Fz 13.1 (9.8) 12.2 (9.2) 0.6642 R2F 13.0 (8.5) 10.8 (8.8) 0.1855 R3F 13.0 (8.5) 10.8 (8.8) 0.2009 RES_2 6.7 (2.7) 8.6 (6.7) 0.3872 RES_3 6.8 (2.6) 8.6 (6.7) 0.3939
** Significant inter-‐session difference in group-‐level CoV (p < 0.05)
31
Figure 12. Peak Force resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation.
4.6 Impulse
Likely due, in part, to increased front foot block time, impulse magnitudes
were on average larger in the front foot than the rear foot (Figure 13). Mean
(standard deviation) intra-‐session CoVs were between 6.7 (3.9)% to 16.8 (8.3)%
(Table 12), with only one athlete exhibiting intra-‐session CoVs greater than 50%.
Significant inter-‐session differences in mean force impulses were found in the front
foot (F_Fz [p = 0.0140]; F2F [p = 0.0284]; F3F [p = 0.0306]) and the resultant of
horizontal and vertical impulse (RES_2 [p = 0.0490]) (Figure 13). A significant
difference in inter-‐session CoVs was found in the rear foot horizontal force (Table
12).
32
Table 12. Mean (SD) sessional (1&2) CoV (%) and p values for Force Impulse resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10).
Signal Session 1 Session 2 p F_Fy 10.0 (7.1) 10.8 (19.8) 0.9219 F_Fz 16.8 (8.3) 14.5 (18.0) 0.7128 F2F 14.2 (7.2) 12.3 (18.5) 0.7715 F3F 14.1 (7.2) 12.3 (18.5) 0.7728
R_Fy** 15.1 (10.7) 6.7 (3.9) 0.0118 R_Fz 15.9 (16.8) 9.7 (3.8) 0.2477 R2F 14.3 (13.2) 7.5 (3.7) 0.1172 R3F 14.3 (13.2) 7.5 (3.8) 0.1142 RES_2 13.2 (8.8) 12.8 (17.1) 0.9513 RES_3 13.2 (8.7) 12.8 (17.1) 0.9496
** Significant inter-‐session difference in group-‐level CoV (p < 0.05)
Figure 13. Impulse of resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation. * Significant inter-‐session difference in group mean (p < 0.05).
33
4.7 Time to Peak (TTP)
Differences between methods of calculation were apparent in mean (standard
deviation) CoVs with TTP from ‘Go’ CoVs found between 4.2 (1.6)% and 6.2 (5.9)%
(Table 13) and TTP from force onset CoVs between 20.0 (18.2)% and 68.9 (46.9)%
(Table 14). No inter-‐session significant differences (p > 0.05) in mean TTP or TTP
CoVs were found when calculating time to peak from ‘Go’ (Table 13, Figure 14).
However, significant inter-‐session differences in CoVs in front foot (F_Fy [p =
0.0043]; F_Fz [p = 0.0103]; F2F [p =0.0023]; F3F [p =0.0022]) as well as resultant
force (RES_2 [p =0.0297]; RES_3 [p = 0.0297]) (Table 14) time to peak from force
onset were found. When calculating TTP from force onset, no significant inter-‐
session differences in mean TTP were found (p ≥ 0.5829) (Figure 15).
Table 13. Mean (SD) sessional (1&2) CoV (%) and p values for TTP from ‘Go’ resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10).
Signal Session 1 Session 2 p F_Fy 5.8 (5.0) 4.3 (1.8) 0.4183 F_Fz 4.8 (2.1) 4.2 (1.6) 0.4919 F2F 6.2 (5.9) 5.0 (2.4) 0.4434 F3F 6.2 (5.9) 5.0 (2.4) 0.4353 R_Fy 5.6 (3.0) 5.5 (2.2) 0.9873 R_Fz 5.6 (2.7) 5.9 (2.4) 0.8148 R2F 5.5 (3.0) 5.7 (2.3) 0.9072 R3F 5.5 (3.0) 5.7 (2.3) 0.9037 RES_2 5.9 (3.1) 5.8 (2.1) 0.9669 RES_3 5.9 (3.1) 5.8 (2.1) 0.9687
34
Figure 14. TTP from ‘Go’ resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation.
Table 14. Mean (SD) sessional (1&2) CoV (%) and p values for TTP from force onset resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces inter-‐session across all athletes (N=10)
Signal Session 1 Session 2 p F_Fy** 47.2 (24.2) 20.0 (18.2) 0.0043 F_Fz** 46.6 (24.9) 22.7 (23.2) 0.0103 F2F** 50.8 (24.5) 23.0 (18.7) 0.0023 F3F** 50.8 (24.5) 23.0 (18.7) 0.0022 R_Fy 34.4 (33.8) 20.7 (18.2) 0.2001 R_Fz 37.7 (33.5) 22.2 (18.4) 0.1683 R2F 35.0 (34.2) 22.1 (17.9) 0.2374 R3F 35.0 (34.2) 22.1 (17.9) 0.2377
RES_2** 68.9 (46.9) 33.6 (32.9) 0.0297 RES_3** 68.9 (46.9) 33.6 (32.9) 0.0297
** Significant inter-‐session difference in group CoV (p < 0.05)
35
Figure 15. TTP from force onset resultant (RES_2 & RES_3) and component (F_Fy, F_Fz, R_Fy & R_Fz) forces compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation.
4.8 Time of Force Application
Mean (standard deviation) time of force application CoVs between 14.6
(10.5)% and 37.7 (20.8)% were found, with significant inter-‐session differences (p =
0.0131) in CoVs found in the front foot (Table 15). No significant inter-‐session
differences (p ≥ 0.3270) in mean force application were found (Figure 16).
Table 15. Mean (SD) sessional (1&2) CoV (%) and p values for Time of Force Application resultant and component (Front & Rear) forces inter-‐session across all athletes (N=10)
Signal Session 1 Session 2 p Front** 37.7 (20.8) 19.6 (18.2) 0.0131 Rear 23.6 (22.1) 14.6 (10.5) 0.1951
Resultant 28.4 (22.6) 17.2 (18.7) 0.1053 ** Significant inter-‐session difference in group-‐level CoV (p < 0.05)
36
Figure 16. Time of force application resultant and component (Front, Rear) forces compared inter-‐session. Mean values across all athletes (N = 10) are presented; error bars represent the standard deviation.
4.9 Peak Force Time Offset
Very small mean (standard deviation) intra-‐session CoVs were observed and
were not different between session 1 [0.1 (0.3)%] and session 2 [0.1 (0.2)%]. No
significant inter-‐session differences in means (p = 0.7119) or CoVs (p = 0.3000)
were found (Figure 17).
37
Figure 17. Peak Force Time Offset compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation.
4.10 Force ‘Off’ Time Offset
Mean (standard deviation) intra-‐session force ‘Off’ time offset CoVs from 7.7
(5.5)% to 11.4 (8.5)% were found. No significant inter-‐session differences in means
(p = 0.4070) or CoVs (p = 0.1291) were found.
38
Figure 18. Force ‘Off’ Time Offset compared inter-‐session. Mean values across all athletes (N=10) are presented; error bars represent the standard deviation.
39
5 Discussion
Previous research and mechanical rationale were used to identify block start
force–time characteristics that have (or could) discriminate between sprinters of
varying skill and ability levels. Results of this study extend this knowledge by
probing the evaluative properties of these characteristics. Although between-‐athlete
variation in force–time characteristics was examined herein via the CoV as a
secondary objective, the primary focus was on the quantification and description of
the amount of within-‐athlete variation using the CoV. While significant inter-‐session
differences in means also provide important information about between-‐session
variability, differences in CoVs inter-‐session demonstrate that athletes are not
consistently variable from session to session; the bandwidth of variability exhibited
by an athlete may not be consistent from one training session to the next.
Of the ten variables analyzed, only three were not found to be significantly
different (p<0.05) in both mean and CoV across all force signals between training
sessions: Pre-‐tension, RT, and force ‘Off’ time offset. Although no significant
differences in variation between sessions existed, individual athletes still
demonstrated variability within a training session in many of these measures.
Further, six of ten measures were not only variable within athletes (between starts)
but were also different depending on the components of the force signal analyzed.
Individual athletes were variable within and between training sessions in these
measures and in many cases, the variation wasn’t necessarily consistent between
athletes for the same measures. CoV ranges for force–time characteristics were
comparable to previous studies (Table 16), with some characteristics covering a
larger range of CoVs (RT, RFD), others covering a smaller range, and some achieved
more consistency (BT, peak force time offset).
40
Table 16. Force–time characteristic CoV (%) ranges from literature and current study
As was found in previous studies, results varied depending on the method of
calculation of select variables. For example, rate of force development, calculated
from ‘Go’ and from force onset showed discrepancies in RFD magnitude, as well as
within-‐ and between-‐day variation. When calculated from ‘Go’, significant
differences (p < 0.05) between sessions were found in planar (X & Y) and 3-‐
dimensional (X, Y & Z) rear and front foot CoVs. This differed from RFD from force
‘onset’ where significant differences were found in front foot horizontal, planar (X &
Y) and 3-‐dimensional (X, Y & Z) front foot CoVs. A similar phenomenon was
observed as well in time to peak measures calculated from ‘Go’ and force ‘onset’. It
was expected that TTP magnitudes would differ based on the calculation
delimitations. As such, between-‐session significant differences were observed in
TTP from ‘Go’ CoVs, whereas TTP from force ‘onset’ was significantly different
between sessions in the front foot, as well as in the resultant planar (X & Y) and 3-‐
dimensional (X, Y & Z) rear and front foot combined CoVs. It has been suggested that
discrepancies in definitions of discrete parameters have led to inconsistent findings
in biomechanics literature (O'Connor & Bottum, 2009). TTP and RFD variation were
sensitive to changes in calculation methods. It is therefore important to consider the
sprint start quantities being targeted by these two measures before appropriating a
calculation method. When comparing an athlete’s rate of force development to a
measure of power for example, a more appropriate time frame for RFD calculation
would be from force ‘onset’; whereas including RT in RFD calculations may give
more insight into the whole movement initiation process from a motor control
perspective. However, in deciding which approach is more appropriate, one must
!
!! Pre%
tension!RT! BT! RFD! Peak!
Force!Impulse! TTP! Time!of!
Force!App.!
Peak!Time!Offset!
‘Off’!Time!Offset!
Current!Study! 6"62$ 22"34$ 4"6$ 7"42$ 5"15$ 8"17$ 4"69$ 15"38$ 0.1$ 8"11$Willwacher!(2013)! N/A$ 10"56$ 5"8$ 28"54$ 3"27$ N/A$ N/A$ N/A$ N/A$ N/A$Fortier!(2005)! N/A$ 13"17$ 5"8$ N/A$ 19"30$ N/A$ 14"19$ 5"22$ N/A$ 13"19$Slawinski!(2010)! N/A$ 11"21$ 5"6$ 35"45$ N/A$ 13$ N/A$ 11"19$ N/A$ N/A$Mero!(1983)! N/A$ N/A$ N/A$ N/A$ 13"23$ 6"25$ 20"32$ 6"10$ N/A$ N/A$
Baumann!(1976)! N/A$ 12"18$ 4"8$ N/A$ 13"20$ 8"9$ N/A$ N/A$ N/A$ N/A$
41
account that the latter approach will inherently exhibit variability as a result of RT.
Further, to mitigate the effects of inducing further non-‐biological variability into
measurements used for tracking, maintaining a consistent measurement
environment is imperative. For instance, when measuring RT, the ‘Go’ cue should
come from the same source location, at the same volume, otherwise, the
measurement of RT may not be comparable from start to start.
This study examined discrete force–time characteristics of the sprint start
that have been linked empirically or theoretically (based on mechanical rationale)
with sprint race performance. It has been well researched that the transition from a
static to a dynamic state of human movement is inherently and predictably variable
by nature both within and between individuals (Diedrich & Warren, 1995; Kelso,
Scholz, & Schoner, 1986; Kelso, Schoner, Scholz, & Haken, 1987). Thus, a single
(common) sprint start strategy is unlikely to afford elite performers the flexibility
and adaptability necessary to consistently achieve high outcome standards across
various conditions (Glazier & Davids, 2009). An assumption is often made that there
is an ‘isomorphic’ common optimal pattern of movement based on the notion that
there is a single most effective and efficient way of performing a movement (Brisson
& Alain, 1996). Traditionally, skilled motor performance has been characterized by
low variability in outcome measures (Anderson & Pitcairn, 1986) and thus, the
assumption is made that these outcomes are the result of highly consistent patterns
of movement coordination and control. Based on these assumptions, a ‘champion’s
model’ approach is taken wherein highly skilled performers’ movement solutions
are widely accepted as optimized (Hatze, 1983) and thereby imitated by less skilled
performers (Newell, 1986). However the sprint start is a complex task with multiple
challenges such as horizontally and vertically accelerating from the blocks while at
the same time situating oneself for the subsequent acceleration phase (Tellez,
1984), which leads to a rather inconsistent and large selection of variables to be
optimized (Latash, 2012). Thus, although many discrete force–time characteristics
have been found to discriminate between performers of varying skill and ability
levels (as evidenced in the literature review), the evaluative properties of these
characteristics depend, in part, on the variation of their across repetitions and
42
sessions within a given athlete. From the results of this study, athletes exhibit
different levels of variability depending on the characteristic analyzed both within-‐
and between-‐session. Through characterization of their within-‐ and between-‐
session variation, a bandwidth of variability for each individual provides a
framework for evaluation of athletes in the daily training environment, regardless of
their skill level. More specifically, results of this research can be used to calculate
effect sizes, conduct statistical power analyses, and to thus aid in both group and
single-‐subject experimental designs. The information required for these
aforementioned purposes was largely lacking in the literature, and was the key
motivation for this thesis.
Many of the discrete measures studied have the potential to interact with one
another. Specifically, when considering multiple force–time characteristics of the
sprint start, interdependence of these measures is inevitable. For example, rate of
force development is derived using two other features that have been found to be
determinants of high performance (time to peak as well as peak force). Further,
impulse is a function of time of force application and force magnitudes. Many
studies have correlated combinations of the same measures to outcome such as total
race time. Given the numerous measures in this study presents, a more complete
description of the sprint start waveform resulted with respect to information
currently available in the literature. Although these measures provide a convenient
summary of the start for the purpose of monitoring progress, it must still be
acknowledged that attempts to collapse start data into discrete measures could
result in a loss of other potentially valuable information from the waveforms
themselves. But, by analyzing a greater number and variety of discrete force–time
characteristics, the risk of overlooking important information could be reduced. For
example, based on Newton’s Second Law of Motion, creating a large horizontal
impulse in the blocks will result in high block exit velocity. Biomechanically
however, although producing a small force over a long period of time will result in a
large impulse, the effectiveness of that impulse in producing a large horizontal
velocity is lost in an explosive activity like the sprint start due to the mechanical
behaviour of muscle-‐tendon complexes (Knudsen, 2009). Thus, knowing the
43
magnitude of time of force application of the impulse would be more informative in
drawing conclusions about the success of the sprint start than impulse alone.
Although results from this study may not be generalizable in uncovering a
fundamental principle of the sprint start, perhaps the ‘champion’s model’ (Hatze,
1983) that uses information such as discrete characteristics about elite athletes to
develop and evaluate training interventions for other less-‐skilled athletes (Fortier et
al., 2005; Mendoza & Schollhorn, 1993; Myer, Ford, Brent, Divine, & Hewett, 2007),
is not appropriate when considering such a complex movement task. It has been
suggested that individuals’ performances should be verified for similarities or
trends in the data before being grouped for analysis (Bates, Dufek, & Davis, 1992).
Despite being regarded as an important consideration for sport biomechanists,
variability within an athlete and between athletes has not been thoroughly
researched (Bartlett et al., 2007); analysis of discrete measures is often found to be
an unsatisfactory approach due to reductions in and discarded data (Dona, Preatoni,
Cobelli, Rodano, & Harrison, 2009; Donoghue, Harrison, Coffey, & Hayes, 2008).
Consequently, one quantity cannot provide a complete description of a complex
movement like the sprint start. Using a non-‐invasive instrument such as FP-‐
instrumented start blocks provides the means to create a variability profile for
individual athletes’ discrete characteristics to provide a more complete description
of their performance in the blocks. In addition, despite the convenience of discrete
measures in applied sport science, future studies may look to consider a more
comprehensive biomechanical waveform analysis such as principal component
analysis to better understand the interactivity of discrete force time characteristics.
PCA converts a number of correlated variables into a smaller number of
uncorrelated, independent variables (Deluzio, Harrison, Coffey, & Caldwell, 2014),
potentially providing athletes, coaches, and sport scientists with a more complete
force–time profile informing the nature of the variability both inter-‐ and intra-‐
individual.
44
5.2 Study Limitations
There were several (de)limitations that must be acknowledged. This study
was designed to primarily to quantify and describe the variation in force–time
characteristics in the daily training environment and did not afford opportunities to
control a number of factors. First, data were collected during coach-‐led team
training sessions. As a result, although the number of trials was dictated by the
coach and informed by the athlete’s condition that day, start data was a reflection of
the training environment. Second, no uncharacteristic data were removed from
analyses. Starts were deemed ‘successful’ if the athlete completed the 30m sprint
following the start. These starts were part of the bandwidth representative of
training data, despite being potential statistical ‘outliers’. Third, the number of
potential participants was constrained based on the fact that volunteers from a
small population of intercollegiate sprinters who met inclusion criteria were
recruited during a single training phase. Finally, the expertise level of athletes was
not homogeneous, with some athletes just entering the training program, and others
achieving national qualification standards. Consequently, although there is limited
generalizability of group data, this study provides rationale and information
applicable for the design and execution of case studies. Despite these
(de)limitations, in-‐field training data were acquired within one training phase which
will help coaches and scientists identify and interpret discrete force–time
characteristics in the daily training environment, without the effects of extraneous
changes in the training cycle. The FP-‐instrumented starting blocks allow for in-‐field
task evaluation, eliminating the use of surrogate testing to evaluate performance.
Further, data presented herein will inform future evaluation and feedback of
outputs from the FP-‐instrumented start blocks at the individual-‐level in the daily
training environment in the pre-‐competition phase and in other phases with further
monitoring. These data could also be used in future research to inform the
bandwidth of inherent variability in discrete force–time characteristics of the sprint
start. As these characteristics are often used as performance measures (i.e., effect of
sprint start performance on total race performance) as well as outcome measures
45
(i.e., effect of strength training protocol on sprint start performance) in research,
these data could be used as means for athlete-‐specific statistical power analyses in
determining the magnitude of change necessary to evaluate a difference in
performance.
To address the above (de)limitations, future studies could look to integrate FP-‐
instrumented starting blocks on a more permanent basis in the evaluation and
monitoring of performance (using discrete measures and waveform analyses). With
this non-‐invasive technology, the integration of FP-‐instrumented start blocks could
lead to more representative data of an athlete’s performance over the course of
training cycles. Once a range of variability is established, future studies could look to
address the validity of the more stable measurements as performance parameters of
the sprint start. Further, if there is mechanical rationale for many of the unstable
force–time measures, coaches and scientists should consider establishing the
magnitude of change required outside of an intra-‐individual’s variability bandwidth
that demonstrates a consequential change in performance. For example, participant
S03 had a 1.5% CoV in impulse while participant S02 had a 17% CoV. A 10%
increase in impulse is might be deemed meaningful for S03 but not for S02 as it lies
within the bandwidth of variability for S02 and outside for S03.
It is also important to note that decisions pertaining to the way that FP signals
were conditioned and analyzed could influence how the findings are interpreted.
First, although force–time characteristics can be calculated using many different
methods, this study only considered one or two methods of calculation per
characteristic. Different methods of calculation could induce or reduce variability
from these measures, as was seen in RFD and TTP from ‘Go’ and force onset.
Including RT in the derivation of these quantities changed the variability of both
force–time characteristics. Thus, the method of calculation of RT had a large impact
on results external to its own measurement, as it was a factor in other
characteristics. Although choice of calculation has an impact on results,
methodological reporting and consistency can help to narrow the scope of
interpretation of characteristic variability in data. Second, data analysis involved the
use of CoV. Although CoV inherently eliminates the notion of an absolute change in
46
magnitude of a force–time characteristic, as a relative measure, it allowed for
comparison between force–time characteristics. This provided a better
understanding of which measures were more stable than others. Subsequently, an
analysis of variance of both mean magnitudes and CoVs produced between-‐session
measures of variability. ANOVAs of mean magnitudes provided a measure of an
athlete’s between-‐session variance of an individual characteristic. ANOVAs of CoVs
informed the between-‐session consistency of within-‐session variability. That is,
whether an athlete was consistently variable session-‐to-‐session.
External to measuring performance and progression or discrete force–time
measures, tracking the type and amount of variability could produce valuable
information about an athlete’s progression or condition. How variability is
interpreted in the sprint start however, has yet to be explored. Some theoretical
constructs including general motor program theory consider some of the variability
to be “noise”, and thus a source of decrement in performance. It is understood in
this that more skilled performers exhibit less variability. Other theories such as
dynamical systems theory or ecological dynamics maintain that some variability is
functional wherein fundamental coordination is “the process of mastering the
redundant [or excess] DoFs,” somewhat characterizing excess DoFs as possible
sources of variability (Bernstein, 1967). Thus, there is potential to further interpret
variability in performance. For example, an anomalous increase in an athlete’s
sessional CoV within a training phase could suggest there is another factor
interfering with performance (i.e., overtraining, injury etc.) or augmenting
performance (i.e., experimenting with different movement solutions as personal
constraints vary [e.g., training-‐induced changes in muscular strength]).
Interpretation of variability may also theoretically have evaluative applicability. In
terms of training phases, athletes may exhibit more variability during technical
training phases, as they explore their biomechanical degrees of freedom (Glazier &
Davids, 2009). Finally, using an athlete’s bandwidth of variability could also provide
a measure of athlete readiness. Unless an athlete is achieving standards within or
above their bandwidth, coaches may consider altering training or warm-‐up to
47
reflect an athlete’s state of readiness. More research is required however to
accurately interpret and evaluate variability.
48
6 Conclusion
Data from this study provides a more complete description of the magnitudes
and variation in discrete force–time characteristics of the block start than currently
exists in the literature. This information is relevant for monitoring and tracking
performance in the daily training environment, especially given that recent
technological advancements have made it feasible to measure foot-‐block forces in
field settings. Although the number and variety of measures presented herein adds
significantly to the knowledge base, future research involving waveform analyses
(e.g., PCA) is warranted to determine whether important information from the
waveforms is lost in the discretization process. The most immediate and direct
application of this research is that the knowledge gained can be used to calculate
effect sizes, conduct statistical power analyses, and therefore, to design future
observational and experimental studies. The lack of information necessary for these
aforementioned purposes was the primary motivation for conducting this
descriptive study.
49
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7 Appendices
7.1 Appendix A Willwacher et al. (2013)
Variables Measures Men Women World-‐class Fast Slow Fast Slow
100m PB (s) mean 10.06bcde 11.08ace 11.69abe 11.37ae 12.48abcd
SD 0.28 0.21 0.14 0.22 0.48 CoV 3% 2% 1% 2% 4%
Block Time (s) r (100m PB) 0.71 mean 0.34 bcde 0.39ae 0.4a 0.39ae 0.43acd
SD 0.02 0.03 0.02 0.03 0.03 CoV 6% 8% 5% 8% 7%
Maximum Resultant Force (front) (N/kg)
r (100m PB) -‐0.32 mean 16.27 16.14e 16.42 15.82 14.46b
SD 2.64 1.45 1.68 3.26 2.68 CoV 16% 9% 10% 21% 19%
Maximum Resultant Force (rear) (N/kg)
r (100m PB) -‐0.5 mean 15.98de 13.59e 13.66 11.36ae 10.86acd
SD 2.57 2.37 3.09 0.293 2.9 CoV 16% 17% 23% 3% 27%
Maximum RFD Resultant Force (front) (N/kg/s)
r (100m PB) -‐0.51 mean 237.37bcde 137.48a 122.64 a 132.21 a 109.38 a SD 75.31 47.72 41.73 41.29 45.61 CoV 32% 35% 34% 31% 42%
Maximum RFD Resultant Force (rear) (N/kg/s)
r (100m PB) -‐0.42 mean 335.27de 247.33 230.51 204.3a 182.91a
SD 95.21 86.28 112.61 82.06 97.89 CoV 28% 35% 49% 40% 54%
Height (cm) mean 182.36de 181.27 de 180.6 de 171.3abc 172.65abc
SD 6.77 5.4 8.4 6.11 4.96 CoV 4% 3% 5% 4% 3%
Mass (kg) mean 80.05bde 73.22ade 72de 60.77abc 62.83 abc SD 6.94 7.26 8.6 4.54 6.49 CoV 9% 10% 12% 7% 10%
Reaction time (s) mean 0.16 0.18 0.19 0.2 0.21 SD 0.09 0.04 0.04 0.02 0.04 CoV 56% 22% 21% 10% 19%
a,b,c,d,e significant difference (p<0.05) to Men World-‐Class Group, Men fast, Men slow, Women fast and Women slow Group, respectively
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Elites1 Sub Elites2 Fortier et al. (2005)
100m PB (s) mean 10.46 11.07 SD 0.11 0.3 CoV 1% 3%
Front Force Duration (ms) mean 370* 405
SD 18 40 CoV 5% 10%
Rear Force Duration (ms) mean 370 268 SD 18 58 CoV 5% 22%
Total Block Time (ms) mean 399* 422 SD 21 33 CoV 5% 8%
Time to Front Peak Force (ms)
mean 216 260 SD 42 39 CoV 19% 15%
Time to Rear Peak Force (ms)
mean 124* 119 SD 17 20 CoV 14% 17%
Front Force at Hands onset (N)
mean 1548 1440 SD 333 118 CoV 22% 8%
Rear Force at Hands onset (N)
mean 1274 1303 SD 108 166 CoV 8% 13%
Front Peak Force (N) mean 1685 1735 SD 490 333 CoV 29% 19%
Rear Peak Force (N) mean 1430* 940 SD 431 255 CoV 30% 27%
Delay between Rear and Front force onset (ms)
mean 26 22
SD 17 34
CoV 65% 155%
Delay between end of Rear and Front force offset (ms)
mean 140* 173
SD 26 23
CoV 19% 13%
Height (m) mean 1.72 1.8
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SD 0.1 0.05 CoV 6% 3%
Mass (kg) mean 74.1 75.2 SD 10.3 8.2 CoV 14% 11%
Reaction time (ms) mean 172* 194 SD 30 26 CoV 17% 13%
1 100m PB<10.70s 2 10.70<100m PB<11.40 *Significantly different, p≤0.05
Elite Well-‐trained Slawkinski et al. (2010)
100m PB (s) mean 10.27* 11.31 SD 0.14 0.28 CoV 1% 2%
Mass (kg) mean 79.5** 66.3 SD 10.5 5.5 CoV 13% 8%
Height (cm) mean 179.2 175.3 SD 6.2 4 CoV 3% 2%
Total Block Time (s) mean 0.352 0.351 SD 0.018 0.02 CoV 5% 6%
Pushing time on rear block (s)
mean 0.154 0.14 SD 0.017 0.026 CoV 11% 19%
Resultant rate of force development (N/s)
mean 15505** 8459 SD 5397 3811 CoV 35% 45%
Resultant Impulse (Ns)
mean 276.2** 215.4 SD 36 28.5 CoV 13% 13%
Reaction time (s) Mean 0.151 0.158 SD 0.016 0.033 CoV 10.6% 20.9%
*p ≤ 0.0001 **p ≤ 0.05
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Group A Group B Group C
Mero et al. (1983)
Horizontal Force
100m PB (s) mean 10.8C 10.8C 11.5A,B SD 0.3 0.4 0.3 CoV 2.78% 3.70% 2.61%
Duration of force production (s)
mean 0.361 0.36 0.368 SD 0.027 0.023 0.037 CoV 7.5% 6.4% 10.1%
Peak force (N) mean 1186c 1154C 898a,B SD 260 170 203 CoV 21.9% 14.7% 22.6%
Time to peak force (s)
mean 0.075 0.081 0.072 SD 0.015 0.019 0.023 CoV 20% 23.5% 31.9%
Mean Force (N) mean 650C 628C 531A,B SD 53 85 38 CoV 8.15% 13.54% 7.16%
Impulse (Ns) mean 234C 226c 195A,b SD 15 31 23 CoV 6.41% 13.72% 11.79%
Power (W) mean 949C 880c 727A,b SD 154 159 172 CoV 16.23% 18.07% 23.66%
Vertical Force
Force production at the time of maximal horizontal force (N)
mean 958C 1036C 683A,B SD 207 180 174 CoV 21.61% 17.37% 25.48%
Mean Force (N) mean 641C 615c 484A,b SD 101 174 121 CoV 15.76% 28.29% 25%
Impulse (Ns) mean 231C 221c 178A,b SD 31 55 43 CoV 13.42% 24.89% 24.16%
Power (W) mean 310c 330c 272a,b SD 35 50 45 CoV 11.29% 15.15% 16.54%
Resultant Force
Absolute (N) mean 1555C 1561C 1175A,B SD 329 205 240 CoV 21.16% 13.13% 20.43%
Relative (N/kg) mean 20.8C 19.9C 15.3A,B SD 3.9 1.9 3.5 CoV 18.75% 9.55% 22.88%
Direction (deg,, from horizontal)
mean 40 42 38 SD 5 5 4 CoV 12.50% 11.90% 10.53%
A,B,CSignificantly different from group A, group B, group C respectively, p<0.01 a,b,cSignificantly different from group A, group B, group C respectively, p<0.05
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Skilled Less-‐skilled Gagnon (1978) Rear Foot Peak Force (N) 438.6 286.5
Time on block (s) 0.257 0.247 Impulse (Ns) 77.7 51.5
Front foot Peak Force (N) 533.3 365.9 Time on block (s) 0.483 0.48 Impulse (Ns) 86.3 83.7
Overall Impulse (Ns) 173.8 135.2 Females only, mean values are displayed. No standard deviation or significance was provided.
Group 1 Group 2 Group 3 Baumann (1976) 100m PB (s) mean 10.35 11.11 11.85
SD 0.012 0.16 0.24 CoV 0.12% 1.44% 2.03%
Reaction time (s) mean 0.101 0.099 0.113 SD 0.018 0.015 0.014 CoV 17.82% 15.15% 12.39%
Block time (s) mean 0.47 0.468 0.504 SD 0.036 0.02 0.032 CoV 7.66% 4.27% 6.35%
Peak horizontal acceleration (m/s2)
mean 15.42,3 13.21,3 12.21,2 SD 2 1.7 2.4 CoV 12.99% 12.88% 19.67%
Horizontal Impulse (Ns) mean 263 223 214 SD 22 20 20 CoV 8.37% 8.97% 9.35%
1,2,3 Significant differences exist between groups 1,2,3 respectively, no p value specified.
Males Females 5m 10m 20m 30m 5m 10m 20m 30m
Coh (1998)
Front Foot RFD (N/s) 0.38 -‐0.3 -‐0.71* -‐0.78* -‐0.06 -‐0.16 -‐0.06 -‐0.13 Front Foot Relative RFD (N/kg/s)
0.37 -‐0.31 -‐0.71* -‐0.76* -‐0.23 -‐0.24 -‐0.1 -‐0.07
Reaction Time (s) 0.43 -‐0.34 -‐0.74* -‐0.66* 0.49 -‐0.58* 0.55 0.3 Front Foot Peak Force (N) 0.46 -‐0.32 -‐0.72* -‐0.83* 0.05 0.03 0.11 -‐0.22 Front Foot Impulse (Ns) 0.49 -‐0.17 -‐0.57* -‐0.71* 0.07 -‐0.01 0 -‐0.34 Relative Front Foot Impulse (Ns/kg)
0.51 -‐0.22 -‐0.63* -‐0.76* -‐0.09 -‐0.07 -‐0.02 -‐0.034
Front Foot Time to Peak (s)
-‐0.69* -‐0.55* -‐0.55* -‐0.66* 0.44 0.48 0.61* 0.56*
Rear Foot Time to Peak (s) -‐0.79* -‐0.41 -‐0.41 -‐0.60* 0.58* 0.59* 0.69* 0.50* Correlation coefficients to time at 5, 10, 20 & 30m * Indicates, correlation is statistically significant (p < 0.05)
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7.2 Appendix B
7.2.1 Par-‐Q Form
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7.2.2 Informed Consent Form
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7.1 Appendix C: Subject-‐specific data
7.1.1 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean pre-‐tension magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.2 M01-‐M05 & S01-‐S05 single-‐start and mean reaction time magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.3 M01-‐M05 & S01-‐S05 single-‐start and mean block time magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.4 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean RFD from ‘Go’ magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.5 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean RFD from onset magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.7 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean peak force magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.8 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean impulse magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.10 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean TTP from ‘Go’ magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.12 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean TTP from onset magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.14 Signal-‐specific M01-‐M05 & S01-‐S05 single-‐start and mean time of force application magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.16 M01-‐M05 & S01-‐S05 single-‐start and mean force ‘Off’ time offset magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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7.1.18 M01-‐M05 & S01-‐S05 single-‐start and mean peak force time offset magnitudes, unfilled black circles represent starts from session 1; filled black circles represent starts from session 2; red circle represents means from both sessions
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