Quantifyingvariationindiscrete!force–time ... · ii! Quantifyingvariationindiscrete!force–time!characteristicsduringthesprintstart: Considerations!for!monitoring!training!adaptations!

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.

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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

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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;

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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

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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,

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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.

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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.

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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


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


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


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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