Linear Kinematics of Sprinting

Linear Kinematics in Sprinting

Introduction

As stated by Dintiman (1975), ‘There is no single greater concern of coaches and athletes in all sports than “how to run faster”.’ Linear kinematics is concerned with the description of motion and involves the study of linear movement through time, including velocity and acceleration (Hall, 2003). Velocity is defined as “the rate at which a body moves from one location to another” (Hay,1993) and linear acceleration is defined as ‘the rate of change in velocity’ (Hall, 2003). In sprinting, factors including step length and step frequency play a major role and previous research has found that world class sprinters have a longer step length and higher step frequency than non-elite sprinters (Kunz and Kaufmann, 1981). This suggests that one or both of these is a factor in optimal sprinting. A relationship exists between step length and step frequency over a certain distance – the longer the step length, the lower the step frequency and the shorter the step length, the higher the step frequency (Donati, 1996).  The 100m world record is held by Usain Bolt at 9.58 s and Beneke and Taylor (2010) have suggested that he has an advantage over other competitors due to his tall height and resulting longer step lengths after the initial acceleration period, meaning he had a lower step frequency than his opponents. Keogh et al (2010) related a strong man sled pull to acceleration phase sprinting and found that in the faster trials greater step lengths and frequencies were observed as well as a shorter ground contact times. This negative interaction between step length and step frequency was suggested to be due to vertical velocity of take off, height of take off and leg length (Hunter et al, 2004).

In a sprint, the athlete’s goal is to reach maximum velocity in the shortest possible time, meaning that acceleration must be as fast as possible abiding by the equation:

Change in Velocity = Acceleration * Time

The purpose of this report is to determine which parameter (stride length or stride frequency) is more responsible for the increased velocity required to produce maximum performance. If this is discovered, training can be adapted to be able to better the favoured parameter and therefore decrease sprint time.

Method

Two 19 year old sport participant males were used as the subjects. They ran four maximal effort 60 m sprints on an outdoor track following a personal warm up and one practice maximal effort sprint. Recovery time was unlimited and the personal choice of the athlete. The lane used had cones positioned every 5 m and Newtest photocells were set up at the 30 m and 60 m points.  They were video recorded using a Sony HC9 camera positioned 50 m away perpendicular to the middle of the 60 m track. The camera was set to a shutter speed of 1/600 s and manual focus was applied after checking there was no tilt or roll. The camera was adjusted to pan to cover 5 m at a time, which was judged using the cones on the track.

The video was then analysed using a video player to determine average velocities, average accelerations, step length and step frequency using the equations below. Step length and step frequency were measured only in the last 30 m. Within this report, one step is defined as the point at which the back foot leaves the ground to the point at which the opposite foot leaves the ground.

v = d/t

a = Δv/Δt

step length = average velocity/step frequency

step frequency = no. of steps/(Δt between 1^st and last step)

Results

It was found that there was not much difference between the average velocities of the two methods of measurement, whether it was calculated over 5 m intervals and video analysed or calculated using photocell times, as seen in Table 1. The photocell velocities will be used for the remainder of this report. Athlete A had a lower average velocity (7.92ms^-1) over the last 30 m than Athlete B (8.51ms^-1). Figure 1 shows that there is an initial increase in velocity up to about 2 s into the sprint, where the velocity begins to steady before decreasing in the last 5 m. Acceleration is the gradient of the line, so as velocity begins to plateau, acceleration is nearly constant. The acceleration in the last 30 m was also negative, implying that both athletes were slowing down towards the end of the race, although not by a great amount as can also be seen in Figure 2. Athlete B’s average step length was greater than Athlete A’s (2.16 m compared to 2.11 m) as well as the average step frequency also being greater (3.93 Hz compared to 3.78 Hz). The longer stride length and higher stride frequency means that Athlete B was able to cover the same distance in a shorter time.

		Athlete A					Athlete B
		1st Run	2nd Run	3rd Run	4th Run	Average	1st Run	2nd Run	3rd Run	4th Run	Average
Average Velocity (m.s-1)	Video Analysed	8.24	7.65	7.94	7.94	7.94	8.67	8.62	8.43	8.52	8.56
	Photocell	8.24	7.61	7.88	7.94	7.92	8.60	8.53	8.40	8.51	8.51
Average Acceleration (m.s2)		-0.14	-0.18	-0.07	-0.13	-0.13	-0.08	0.00	-0.08	-0.17	-0.08
Average Step Length (m)		2.12	2.09	2.07	2.11	2.10	2.19	2.16	2.14	2.16	2.16
Average Step Frequency (Hz)		3.90	3.65	3.80	3.76	3.78	3.92	3.95	3.92	3.95	3.93

Table 1: Table showing average velocities (video analysed and photocell times), average acceleration, average step length and average step frequency over the last 30 m of the sprint.

Figure 1:  Velocity – time graph to show average velocity over 5 m intervals over the last 30 m, of each of the 4 runs, for Athlete A and Athlete B.

Figure 2: Acceleration – time graph to show average acceleration over 5 m intervals over the last 30 m, of each of the 4 runs, for Athlete A and Athlete B.

Discussion

The average step frequency for both athletes is very similar for each of the four runs, so the average for each athlete will be used in the discussion. In relation to the purpose of the study, which was to determine which factor, step length or step frequency, was more important in sprinting, it was found that Athlete B completed the 60 m sprint in a shorter time than Athlete A, whilst having a higher step frequency and longer step length. However, the average step frequencies were very similar, Athlete A being 3.78 Hz and Athlete B being 3.93 Hz. This leads to the belief that even the small difference in step length (0.06 m) is responsible for the increased velocity of Athlete B, as velocity is equal to step length multiplied by step frequency (Maulder et al, 2008; Hay, 2003). This supports previous research of Hunter et al (2004) which suggested that there is a “negative interaction” between step length and step frequency. It is assumed from these results that step length is the determining factor of velocity in the 100 m sprint. However, inferential statistical analysis was not conducted, so it is unsure as to whether this difference in step length is significant or not.

Velocity and acceleration profiles are useful tools for coaches in order to establish which phase of the sprint can be improved, and when paired with step length and step frequency, has the potential to produce a specific training programme.

Acceleration is said to happen in the first 10m of a 100m sprint, with 36-100m being at maximum speed (Delecluse, 1997). This can be seen in Figures 1 and 2 as at about 4 s, both athletes had reached about their maximum velocity and were beginning to keep a constant velocity or decelerate very slightly. The aim of a sprinter is to reach maximal velocity as soon as possible, therefore needing a fast acceleration phase (Maulder et al, 2008). Once maximum velocity is achieved, it is then required to be maintained to complete the sprint in the fastest time possible.

Ito et al (2006) found that world class sprinters tended to take a wider step when accelerating from the blocks, before narrowing the step width when in full stride sprinting, which was recognised in the results as with every one of the four runs, both athletes maintained a similar step length for the final 30 m. This means that there is a shorter step length in the initial acceleration phase, suggesting other reasons as to how maximum velocity is reached. Weyand et al (2000) had previously suggested that faster sprinters were more successful due to the force applied on the ground as opposed to how fast they moved their legs. This implies that power to produce a larger force and resultant larger step length or width is required.

Spinks et al (2007) found that both step frequency and step length can be increased by strengthening the hip and knee extensor muscles to produce more power. This can be done through resistance training, in the form of, for example, plyometric training or running on sand (Costello, 1985). Maulder et al (2008) found that step length and step frequency both decreased when resistance was applied, but when resistance was removed, they increased along with acceleration, meaning maximum velocity was reached faster, therefore maximising the sprint time.

Much of the research suggests that there is a compromise between step length and step frequency, with athletes compromising in order to save energy, thus suggesting that individual preferences can begin to play a role (Cavanagh and Williams,1982; Cavanagh and Kram, 1989). It has also been noticed that increasing the force on the ground not only increases step length, but also step frequency, further implying the difficulty to differentiate which parameter is more important (Young, 2007).

This study focused on step length and frequency, assuming that these were the parameters to alter the ultimate velocity. However, other factors involving individual differences could be the cause of the difference in step length or frequency initially. These could include muscle distribution and muscle type (Mero et al, 1981). In a later study by Mero (1985) it was found that there was a positive correlation between fast twitch fibres and maximal velocity and also between fast twitch fibres and step rate. This implies that velocity is not purely to do with step length or step frequency, but delves deeper into their own production, involving muscle fibres. Due to the fact that no muscle biopsy was conducted, there is no way of telling whether the muscle fibre types in the two participants differed at all.

However, there are potential problems with this study in that only two male participants of similar training were used. This means the results cannot be generalised to larger populations. The step length and step frequency were also only calculated in the last 30 m of the sprint, making it impossible to analyse whether there were differing step lengths and step frequencies in different phases of the sprint. Further studies could be conducted to gain step length and step frequency over the full 60 m of the sprint, enabling the differentiation of the phases of sprinting, and to see whether the step length or step frequency changes dependent on which phase the sprinter is in. This would allow training to be adapted further to determine whether step length or step frequency is important in different phases of the sprint.

In conclusion of this study, step length appears to be the factor determining velocity in a 60 m sprint. Through strength training, power can be increased and therefore step length and resulting velocity can also be increased. 

The Effect of Caffeine on Sports Performance

The Effects of Energy Drinks on Sport Performance

 Sports drinks have become a regular feature in most athletes’ diets as an ergogenic aid, ranging from professional competitors to recreational sports participants.  Energy drinks (such as Monster & Red Bull) are a major sub category of the broad “sport drink” term; they market their energy source as primarily caffeine and carbohydrate, combined with electrolyte solutions.

Conclusions derived from existing research differ on the significant effect of components of energy drinks on performance, despite most concluding significant improvements in performance (Ganio et al, 2010).  Branded drinks have been developed on the basis of findings within such research, which has gone on to provide numerous opportunities for further research. Existing research will be critically analysed to come to a conclusion of the effects on performance of energy drinks.

Research as far back as the 1970s (Burke, 1995) has found that caffeine doses at about 5-6mg.kg^-1 have an effect on stimulation of numerous bodily functions, including muscles and the central nervous system, during exercise to improve sporting performance (Burke, 1995). As much of the existing literature on caffeine effects is not contemporary; it is questionable as to whether research methods are up to date with modern technology. For example, Engels et al (1999) found positive correlations between caffeine ingestion and augmentation of arterial blood pressure, suggesting that this would lead to an increase in substrates being delivered to working muscles.  However these increases were found using auscultation, which is not the most reliable technique. Increases in arterial pressure through caffeine ingestion in Red Bull can have positive effects on both aerobic and anaerobic endurance along with mental performance, such as memory recall and concentration (Alford et al, 2001).   It may be noted that the research design was double blinded and consisted of both preliminary and subsequent studies with additional controls, thus increasing the reliability of this study. Adversely, it may be argued that the highly controlled conditions of the reaction time and concentration task (a psychomotor test battery and number cancelling from a pseudo-random list of characters) reduces external validity.  Although Alford et al (2001) found improvements to ‘aerobic and anaerobic endurance’, in more recent research, it remains unclear as to whether caffeine produces any effect on anaerobic activities; Astorino et al (2008) discovered that caffeine does not have an effect on muscular strength or short, intense exercises (1RM).

Research by Spriet et al (1992) revealed the use of caffeine in fluids helps reduce the amount of muscle glycogenolysis in the first 15 minutes of exercise, thereby sustaining glucose in the muscles and increasing the oxidation of fat.  Costill et al (1978) found similar results but discovered evidence that caffeine increases fat oxidation from the start of exercise by increasing free fatty acids in the plasma; helping to maintain glucose levels to continue high intensity exercise later on.  However, caffeine has been found to improve performance not only through changing substrate utilisation but also by increasing Beta endorphins (Ivy et al, 2009).  Ivy et al (2009) could have shown different results, in terms of substrate utilisation, compared to other studies because trial time and distance were not set to a specific amount; participants had to complete a session equivalent to an hour at 70% VO_2max so all subjects exercised different amounts potentially leading to subjects using different substrates to each other.

Tarnopolsky (2008) concluded that improvement in endurance following caffeine administration is multi-factoral, including improvement in excitation-contraction coupling in skeletal muscle, calcium release from sarcoplasmic reticulum and reductions in pain perception (PP) and perceived effort (RPE). It should be made aware that much of the physiologically assessed research was conducted in lab experiments involving rats, therefore making it difficult to generalise to humans.  The reduction in PP and RPE results in an increase in tolerance to noxious afferent signals experienced during fatiguing contractions (providing an ergogenic aid to performance).  The opinion of Borg (1982) is that RPE is the greatest indicator of physical strain integrating various signals elicited from peripheral musculoskeletal structures, central nervous system, cardiovascular and respiratory functions. RPE scales are very subjective and will differ between individual participants, especially when considering pre-existing pain threshold and perceived effort differences, which may be innate or trained.  A practical application of this assumption is reflected in a study by Cole et al (1996), whereby RPE was measured during 30 minute trials of isokinetic variable resistance cycling, in either caffeine or placebo conditions. Participants cycled at a fixed rating of 9 on the Borg scale and total work performed was significantly higher in the caffeine condition, suggesting that increased performance was resulted by an alteration in the participants’ neural perception of effort.

Rehydration is important in prolonged exercise as the body’s water volume decreases rapidly through its attempts to maintain core body temperature through sweating, therefore leading to dehydration and a decrease in performance (Armstrong et al, 1985). It has been found that although caffeine drinks and water rehydrate performers, carbohydrate electrolyte drinks do so significantly better (P<0.05) (Gonzalez-Alonso et al, 1992). 

Compiling this previous literature provides a conclusion that caffeine certainly has a positive effect on muscular endurance and cognitive functioning, including intermittent activity with prolonged duration, such as that experienced in team sports as found by Goldstein et al (2010).

However many energy drinks, including those containing caffeine, can have side effects.  Various literature exists highlighting adverse effects of the administration of caffeine.  One extreme example is that over consumption of caffeine can result in symptoms of tachycardia leading to hospitalisation (Popke, 2010).

Other side effect of energy drinks is the emergence of Gastro-Intestinal discomfort (G.I.D), including refluxing, nausea, bloating and cramping, which lead to decreased sporting performance (Van Nieuwenhoven, 2005; Sadowska et al, 2009).  Energy drinks can also affect performance by their sweet taste and how much they tend to “fill” the stomach (Beckers et al, 2000).  When trying to avoid these affects the avoidance of hydrating results in G.I.D anyway due to lower blood plasma volumes, leading to reduced blood flow to the intestines because of high blood viscosity (Beckers & Rehrer, 2000).  Therefore it needs to be assessed whether the use of energy drinks as opposed to water produce a net improvement in performance. The appearance of G.I.D usually comes about from longer duration exercise, with Van Nieuwenhoven et al (2003) finding that as many as 67% of marathon runners suffer. G.I.D derived from the use of sports drinks can increase run time by 2.4% whilst the energy element of the drink may only improve performance by 0.3%; therefore leading to a net detrimental affect (Burke et al, 2005).  However, this assessment was only a snap shot of a prolonged run as it took data from 200m of a 21.1Km run.

  

Reliable physiological methods such as catheters have been used to assess the resting values for gastric pH and gastro-oesophageal reflux by revealing no differences in G.I.D for water, energy drink or sports drinks with added caffeine after prolonged cycling trials (Van Nieuwenhoven et al, 2000).  However, other studies used questionnaires to analyse their data but this could be problematic due to other variables affecting participants’ decisions, such as tiredness, or illness (Sadowska et al 2009; Van Nieuwenhoven et al, 2005).  This could be counteracted by using many subjects to normalise results as done by Van Nieuwenhoven et al (2005), who found that sports drinks, with caffeine or not, gave more incidences of nausea than water but not more severely.  However it has been found that G.I.D in prolonged exercise can be reduced by ingesting drinks with two carbohydrate transporters compared to one (Jeukendrup, 2004).  For example, the drink Rehydrate carries glucose and fructose and can increase performance by 7.1% on a treadmill VO_2max test, regardless of any side effects (Snell, 2010). 

On the other hand, a positive side effect of energy drinks, as found by Saunders (2004) is the prevention of muscle damage by adding protein.  Creatine Phosphokinase can be reduced by 83% when adding protein to a carbohydrate drink, showing that muscle damage is lowered and recovery will be faster (Saunders, 2004).

Most caffeine containing energy drinks are also supplemented with other compounds such as amino acids to improve performance (Ganio et al, 2010). Many studies, such as those by Saunders et al (2004), Ivy et al (2003) and Niles et al (2001) have examined the use of adding amino acids to a standard carbohydrate-electrolyte drink to assess the comparison of performance. Trials used in the studies were either exercise to exhaustion tests such as by Saunders (2004) or long endurance activities by Madsen et al (1996).  Many studies, such as Madsen et al (1996) & Saunders et al (2004), masked the appearance and taste of the beverages to create a double blind design to ensure no favourability between trials.  Studies such as one by Niles et al (2001) who did not use a blind design present a reliability issue whether performance is improved due to protein or not.  To find results purely on the effects of energy drinks, subjects need to be fasted so their blood glucose levels aren’t altered from food intake (Porte & Woods, 1981).   Many studies such as those by Saunders (2004); Ivy et al (2003) & Saunders (2009) did include a fasting period prior to entering the laboratory to overcome this potential influence on results.  In contrast, Madsen et al (1996) required the subjects to have a high carbohydrate meal four hours before trials, perhaps showing why there was no significant improvement in performance from carbohydrate or carbohydrate-protein drinks because the subjects already had high carbohydrate levels in their system to avoid glycogen depletion and therefore fatigue.

Differing results occurred in trials regardless of the intensity of exercise.  For example many studies, such as Ivy et al (2003), that used exercise to exhaustion in cycle trials or prolonged trials without reaching exhaustion, such as Saunders (2009), found improvements in performance using protein. Conversely, van Essen & Gibala (2006) used a trial that didn’t require complete fatigue to occur and found no significant improvement in cycle performance from added protein.  The same inconclusiveness arose from using running as the form of exercise as found by Niles et al (2001) and Betts et al (2005) who achieved different results with the latter finding no significant benefit of adding protein whilst Niles et al (2001) found a 21.2% increase in performance.

The only main difference between studies is that with the investigations where protein improved performance it was added to the drink in place of the carbohydrate, whereas with failing improvement studies; the protein was added to the carbohydrate.  For example, Niles et al (2001) had their carbohydrate-protein drink contain 112g of dextrose and maltodextrin and 40.7g of protein and the carbohydrate drink contained 152.7g of dextrose and maltodextrin.  This means both drinks had the same mass of supplement.  However, for the carbohydrate drink in the study by Betts et al (2005) there was 1.2g^.Kg^-1.h^-1 for study A and 0.8g^.Kg^-1.h^-1 for study B.  These substances contained 6.2% glucose and 3.1% fructose.  However, the carbohydrate-protein drink added 1.5% glutamine, therefore increasing the amount of supplement in the drink.  This could explain the results of some studies in not increasing performance as the carbohydrate-protein drink still contained a lot of carbohydrate.  This suggests that protein will help increase performance more than solely carbohydrate but only in large amounts or if carbohydrate is lacking.

In conclusion, combinations of carbohydrate-electrolyte with caffeine and carbohydrate-electrolyte with amino acids drinks are both beneficial, with most studies finding a significant positive effect on performance, due to the increased psychological focus and physiological substrate utilisation (Ganio et al, 2010). Further research can be conducted to determine whether it is amino acids or caffeine that causes most of this positive effect. As technology becomes increasingly advanced, there will be more accurate and easier ways to research this topic.  However, as the existing research is expansive and vast, it is unclear as to whether further conclusions have been made that were not analysed in this review.

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