Use of a global positioning system to assess variation in
velocity during track work in 3-year-old Thoroughbred
horses
Research Project of Veterinary Medicine
Massey University, New Zealand
Drs. C.Y. Verdenius (3051420)
November 2010
Project Supervisors:
Dr. C.W. Rogers (Massey University, New Zealand)
Prof. Dr. P.R. van Weeren (Utrecht University, the Netherlands)
Table of contents
Abstract
2
Introduction
3
Reason for performing study/hypothesis
22
Materials and methods
23
Results
31
Discussion
34
Conclusion
37
References
38
1
1. Abstract
Training is an important variable for determining athletic success. Nonetheless, there has been
limited scientific evaluation of racehorse training programmes. This study aimed to determine the
exact velocity during training, using a global positioning system (GPS) and to see if there’s a
significant difference between the average GPS velocity and the average velocity recorded by the
trainer.
Nineteen Thoroughbred racehorses age 3 years were followed through a traditional training and
racing programme over a 5 month period. Daily GPS data together with the trainer’s timing and
distance data were collected while the horses were trained. Data of all horses in training were
analysed and compared for the months August and December. Parametric data were examined using
a general linear model. Acceleration curves were smoothed using an autoregressive moving average
algorithm.
For canter workouts, the mean velocity timed by the trainer was significantly lower than the mean
GPS velocity (P=0.001) and mean GPS20 velocity (P=0.001). There was no significant difference
between the true gallop work (mean GPS40 velocity) and the mean trainer’s velocity (P=0.169).
The mean acceleration during canter was significantly lower than during gallop (P=0.001). There was
a significant difference in variation in velocity between good and poor performing horses in both
August (P=0.003) and December (P=0.002). A significant decrease of variation over the period of
training was seen within the good performing horses (P=0.001), but not in the poor performing
horses (P=0.059).
The results demonstrated that there is no constant velocity during canter, but a constant
acceleration. Therefore the velocity did not fluctuate around the mean velocity as we expected, but
instead the acceleration fluctuates around the mean acceleration. This means that racehorses are
trained in a different way than the trainer intended.
The current method for measuring velocity during training does not reflect actual velocity for canter,
but does reflect actual velocity for gallop work. During gallop work, the horses only reach racing
velocity for a couple of seconds. Good performing horses have less variation in velocity compared to
poor performing horses. They also have a decreasing variation in velocity over the period of training.
2
2. Introduction
2.1
The horse as an athlete
Evolution into superior athletes
The evolution of horses as grazing animals on the prairies of North America has formed them into an
extraordinary athlete. Speed and endurance were necessary attributes for survival in these open
lands to escape predators and to travel distances in search for food and water. Once domesticated
these characteristics of speed and endurance were enhanced by selective breeding.
Selective and close breeding (breeding related horses to each other) resulted in the formation of a
variety of breeds, with widely varying body type. This is also very apparent among Thoroughbreds, in
which ten founder mares account for 72% of maternal lineages and one founder stallion accounts for
95% of paternal lineages.
All the different types of horses we know today have been bred or adapted to a large variety of uses.
But regardless of their size or use, all horses share an ability to perform physical activities at a level
that surpasses that of most other animals of similar body size (Hinchcliff et al. 2007).
Superior athlete: physiological adaptations
The superior athletic capacity of horses can be attributed to a number of physiological adaptations:
High maximal aerobic capacity
The maximal aerobic capacity (VO2max) of horses is approximately 2.6 times that of similarly sized
cattle and, based on bodyweight, approximately 2-2.5 times that of highly trained men. The large
aerobic capacity of horses is associated with a number of factors, including a larger maximum cardiac
output, a higher haemoglobin concentration and a high amount of capillaries and mitochondria in
muscle.
To be able to perform, muscles need oxygen. Oxygen transport from the lungs to exercising muscle is
achieved by the circulation. Cardiac output = heart rate x stroke volume and determines the delivery
of blood (and hence oxygen) to tissue. During intense exercise, Thoroughbreds can have a cardiac
output as high as 400 L/min. This is not achieved by a higher heart rate though, because maximum
heart rate does not differ between horses, cattle and men. The answer lies in the fact that horses
have proportionally large hearts, with a heart weight of 0.9-1.1 % of bodyweight. As a large heart has
a larger stroke volume, it also has a larger cardiac output. The heart weight of Secretariat was
estimated to be 10 kg, whereas the heart weight of stallions of lesser quality was up to 50% less.
In addition to cardiac output, oxygen delivery is limited by the oxygen-carrying capacity of blood.
Horses have the ability to rapidly increase this oxygen-carrying capacity through splenic contraction.
This results in an increase in circulating erythrocytes, without concomitant increase in plasma
volume. In this way, the oxygen-carrying capacity of arterial blood can increase by up to 50% during
intense exercise.
The final step in the oxygen transport process is the utilization of oxygen in muscle. A high capillary
density in muscle also favours oxygen delivery and aerobic metabolism. Mitochondria provide the
energy for muscle contraction. The greater the quantity of mitochondria per unit of muscle weight,
the greater is the oxidative capacity of the muscle. The concentration of mitochondria in muscle of
horses is approximately twice the concentration in muscle of cattle (Hinchcliff et al. 2007.
Large intramuscular stores of energy substrates (glycogen)
The chemical energy for muscle contraction is provided by the oxidation of CHO and fat, and to a
minor extent protein (amino acids). Glucose is stored as glycogen in liver and skeletal muscle, with
over 90% of total body CHO stored in muscle.
Fat is stored in adipose tissue and muscle, with the majority (>85%) in adipose tissue. Compared to
the CHO reserve, the fat store is considerably larger: there is 50 to 60 times more energy stored as
fat.
The relative contributions of CHO and fat to energy use depend on several factors, including exercise
3
intensity, training state, muscle composition, diet and feeding state, and the duration of exercise.
An increase in energy contribution from CHO (primarily muscle glycogen) oxidation is seen as the
intensity of the exercise increases. At the same time, the contribution from fat oxidation will
decrease. Duration of exercise also influences the pattern of substrate utilization. During prolonged
submaximal exercise there is a progressive increase in the energy contribution from fat oxidation. A
concomitant decrease in CHO oxidation occurs that coincides with a decrease in muscle glycogen
stores.
Horses have larges stores of intramuscular substrate that is directly available for use during exercise.
Of these substrates, glycogen is the most important. Its concentration in horse muscle
(approximately 130-150 mmol/kg wet weight (ww) or 550-650 mmol/kg dry weight (dw)) is
considerably higher compared to sled dogs (70-80 mmol/kg ww or 330-350 mmol/kg dw) or humans
(80-140 mmol/kg ww) (Hinchcliff et al. 2007).
Efficiency of gait
For large animals it is difficult to have an energetically efficient gait, because of their slow rate of
contraction and low power output of their muscles. In the horse, efficiency is achieved by the elastic
storage of energy in muscle and tendon units. The muscular work of galloping is halved this
way(Hinchcliff et al. 2007
In the study of Pfau et al.(2006) craniocaudal, mediolateral and dorsoventral kinetic parameters were
calculated for horses at gallop speeds ranging from 7-17 m/s. Displacement amplitudes of the trunk
generally increased with speed, except for the dorsoventral displacement amplitude, which
decreased with increasing speed.
Displacement in the craniocaudal direction increased from 75 mm (range 62-88 mm) at 7 m/s to 89
(range 82-97 mm) at 17 m/s, levelling towards higher speeds. This means that some of the energy
put into the forward movement of the trunk will be lost correcting for this craniocaudal movement.
Because the craniocaudal displacement increases with speed, the energy loss also increases.
Dorsoventral displacement decreased from 185 mm (range 170-200 mm) at 7 m/s to 83 mm (range
75-91 mm) at 17 m/s. At lower speeds, the trunk has a bigger dorsoventral movement during the
aerial phase. When the horse is running at higher speeds, the horse flattens this out and the result is
a smoother gallop.
When the horse is constantly accelerating and decelerating (instead of running at a constant speed),
energy will be lost due to more dorsoventral displacement of the trunk when slowing down and
more craniocaudal displacement when the horse is accelerating again.
Efficient thermoregulation
Heat production increases 10 to 20-fold above resting metabolic rate during low intensity exercise in
horses.
The four main mechanisms of heat dissipation are
 Conduction: direct transfer of heat between surfaces that are in contact.
A small amount of heat is transferred directly through muscle tissue to overlying skin. During
exercise, the major mechanism of heat transfer (away from the working muscle to skin and
respiratory tract) is by indirect conduction of heat via the circulatory system.
 Convection: the mass transfer of heat to the surrounding layer of air that moves past the
skin.
The convective heat loss is increased by increasing the skin blood flow via vasodilatation.
Specialised vessels, arteriovenous anastomoses, are present in the skin, which contribute to
this process.
 Evaporation: heat loss due to the latent heat of vaporisation of water from the skin surface
or from the respiratory tract.
Evaporation of heat is the most effective route of heat loss during heat exposure or exercise.
 Radiation: the transfer of heat at the skin’s surface due to emanation of energy from or to
surrounding radiant surfaces.
4
Horses can lose 6.5 to 9 litres of sweat per hour in response to moderate exercise, but rates can get
as high as 10 to 15 litres per hour.
Equine sweat has high concentrations of sodium, chloride, potassium and protein, as well as
reasonable amounts of calcium and magnesium. Besides this, it also has an unusually high
concentration of a protein called latherin. Latherin is believed to help promote spreading and
evaporation of sweat, possibly aiding evaporation and cooling (Marlin and Nankervis 2002).
2.2
Physiology of training
Training versus conditioning
Any physical training programme aims to the following goals:





Improve or maintain maximum performance
Delay onset of fatigue
Improve (loco motor) skills
Minimize the incidence of injuries
Maintain willingness and enthusiasm for exercise
Strictly speaking, conditioning refers to the improvement in athletic performance by inducing
changes in response to repetitive exercise, that can be evaluated with objective and scientific
methods (Rogers et al 2007).
Training prepares the equine athlete to compete both effectively and safely. It induces physiological
adaptations that are needed to perform on a high level without the risk of injury. Besides that, it also
induces psychological adaptations: getting the horse used to elements of competing (for example
starting gates) and teaching it desirable behaviour, which are also essential for effective competition.
To induce adaptations that are needed to permit optimal performance, the horse should regularly
perform the type of activity it will perform during competition. However, it is very important that the
activity is done at the right intensity.
The adaptive response
Repetitive exercise induces an adaptive response, which acts to reduce the effect of the strain
induced by the physiologic stressors associated with exercise. For example, when a muscle has to
produce an increasing force, the stress stimulates changes in the muscle’s structure and function that
will act to reduce the stress on individual muscle fibres. While doing this, the overall capacity of the
muscle will also increase.
The effect of training can all be brought back to an increased production of structural and functional
proteins. Studies indicated that cellular adaptations arise from the cumulative effects of changes in
gene transcription occurring during the recovery period, in response to mechanical loading during
each single training session (Rogers et al. 2007). Accumulation of metabolites and waste products
induces an increased transcription of DNA. Encoded in this DNA are the specific proteins (including
enzymes) that control rate limiting functions associated with these metabolites. If the increased
transcription is associated with increased translation of mRNA to protein and appropriate posttranslational events occur, a production of more protein will be the end result. An increased quantity
or activity of the enzymes results in an increase in the maximal rate at which the metabolites can be
processed and waste products can be eliminated. Seen at organ level, these changes result is an
increase in organ function. This is usually associated with increases in organ size (Hinchcliff et al.
2007).
The transcription of proteins is activated for several hours, all genes return to control levels by
approximately 22 hours post-exercise. However, the mRNA content of genes is still elevated at this
5
time. These transient increases in transcription from consecutive bouts result in accumulation of
mRNA, which may represent the basis for the adaptive response. After several training sessions or
weeks of transient increases in transcription, the cumulative effect is likely to produce sufficient
changes in mRNA to promote protein growth (Rogers et al. 2007).
Principles of effective training
In order for training to be effective in inducing the desired conditioning, there must be a degree of
‘over-reaching’. Over-reaching refers to the strain induced by the performance of an activity at a
sufficient intensity and duration. This strain is needed for the conditioning effect to occur.
Training is also task-specific, so it is important that the task is performed for which conditioning is
desired. An endurance horse will be poorly trained for sprint racing, for example.
Because training is so specific for each individual goal, there are three basic principles of training
expressed for human exercise physiology:
1. Repetition
2. Summation
3. Duration
As has been stated earlier, there must be repetition of the training stimulus to induce a training
effect. The number of repetitions depends on the type and intensity of exercise. The intensity (the
levels of stress or speed of the exercise) also needs to be closely monitored in order to avoid injuries.
In general, the higher the exercise intensity, the longer the recovery period needed to allow for
repair of muscle tissue damage. In the initial phase of training, the minimal exercise intensity should
be around 50-60% of VO2max on every second day. This will improve aerobic intensities. Higher
intensities however, are necessary to improve strength (approximately 80% of VO2max) and
anaerobic capacity (up to 165% of VO2max) (Rogers et al. 2007).
Summation refers to the total amount of work performed. Because some degree of over-reaching
has to be achieved, the total amount of work performed must be sufficient to induce some strain. If
recovery is not allowed to occur between the repetitions, the total amount of work needed to
achieve a training response may be lower.
The final requirement to induce a training effect is that the training stimulus must be of sufficient
duration, both on the short term and the long-term (Hinchcliff et al. 2007). During a single training
session at constant speed (intensity), duration is a principal contributor for increasing the aerobic
capacity. On the long term (the length of the training programme), the duration depends on the prior
level of conditioning of the horse and the athletic demands that are to be placed on the horse in
competition. It is generally accepted that the duration of the exercise should be increased gradually
by circa 10% per week over the first 10 weeks to increase the aerobic capacity. Also, the most
relevant (in both nature and magnitude) adaptations occur within the first 10-15 weeks of training.
This is despite the training adaptations that occur immediately after training (Rogers et al. 2007).
Besides these basic principles, some other principles have to be taken into consideration in order to
design a successful training programme.
During a single exercise session over-reaching has to occur (in order for a training effect), which will
lead to fatigue and mild cellular damage. This will result in short-term adaptive responses. During
training, the performance is first conserved, but declines when the horse starts to get tired. After
finishing the training, the horse gets rest and recovers from the training load. This results in a
performance capacity that at first increases to the baseline level but increases even further with rest
(overcompensation). If the recovery period would be extended further though, the horse adapts
again and the performance capacity is reduced back to the baseline (Fig 1).
6
Fig 1: The performance curve (dashed line) of a single training session is shown in comparison with basic performance level
(straight line). During training (training load), performance is conserved but declines when the horse starts to get fatigued.
The horse recovers from the training load during the rest period, resulting in a performance capacity that at first increases to
the baseline level, but after that increases further with rest (overcompensation). If the recovery period is extended even
further, the horse adapts again and the performance capacity is back to baseline. (Rogers et al. 2007).
Because the horse will rapidly adapt to this first training stimulus, it needs to be increased gradually.
When the horse is exercised regularly and at increasing intensity, the adaptation that occurs during
the recovery period of a single training session leads to an overall improvement in performance.
Thus, the basis of any training programme is to continually provide gradually increased levels of
stress to the physiological systems in order to improve performance (Fig 2)(Rogers et al. 2007).
Fig 2: The performance curve (dashed line) during different training strategies. (A) Regular training sessions with the same
load and relative long rest periods do not increase performance. (B) Regular training sessions with increasing training loads
and with sufficient rest periods do increase performance (the overload principle). (Rogers et al. 2007).
However, these principles of training must be used thoughtful and in a planned manner. The art of
training involves the prudential use of various intensities and durations of exercise, in order to induce
the optimal adaptations that will permit successful competition while preventing injury or occurrence
of overtraining. It is important to appreciate that there is an upper limit for these adaptations and
that individual horses will differ in relation to how well they can cope with the stress of the training.
Overtraining
Overtraining is defined as a loss of performance ability (despite the maintenance of or an increase in
training effort), which cannot be explained by any discrete pathology. It is the result of an imbalance
between training and recovery, with exercise exceeding the horse’s capacity, without the horse being
able to handle all the stress (both physically and mentally) involved with training (Fig 3).
7
Fig 3: The performance curve (dashed line) during overtraining: regular training sessions without sufficient rest periods
decrease performance. (Rogers et al. 2007).
Inadequate nutrition may also contribute to the syndrome. Chronic fatigue, lack of training progress
and injuries are common outcomes.
In horses, the syndrome is characterized by decrements in performance and maximal rate of oxygen
consumption, as well as a loss of weight and behavioural changes (irritability), including a reluctance
to exercise. Other signs in horses are nervousness, tachycardia, muscle tremors, sweating, diarrhoea,
and inappetence. Diagnosis is complicated by the absence of any one definitive test, because there
are no single changes in routine haematological or serum biochemical variables that provide
confirmation.
The physiologic features of overtraining are still poorly understood. Depletion of energy stores and
damage to muscle cells are obvious factors, but it could probably also involve an endocrine
imbalance. Hormones are essential for the physiological adaptations during exercise. They influence
the recovery phase by modulating anabolic and catabolic processes. Overtraining is also a wellrecognized syndrome in human athletes. Studies of overtrained human athletes showed disorders of
hormonal regulation at pituitary-hypothalamic level and adrenal exhaustion, leading to a consequent
reduction in blood cortisol. They also showed a down-regulation of peripheral and perhaps central βadrenergic receptors, and also down-regulation of neuromuscular junction. Studies of horses showed
that they have either an increased or decreased adrenocortical responsiveness to ACTH
administration, so there is not a clear conclusion (Hinchcliff et al. 2007).
2.3
Introduction to the Thoroughbred racing industry
The racing industry is a great source of entertainment in New Zealand. Thoroughbred racing is the
largest of the three racing codes, with harness and greyhound racing being smaller (Anonymous
2009b).
Fig 4: Market share by code (based on domestic TAB sales last 10 years) (Anonymous 2009b).
8
In 2004 the impact of the racing industry on the economy was assessed. The value-added
contribution of Thoroughbred racing and breeding to the gross domestic product (GDP) was around
NZ$ 1,100mn or 1% of GDP (Anonymous 2004b).
Over 40,600 people are involved in some capacity in the racing industry, which equates to 18,326 Full
Time Equivalent (FTE) jobs. Of this, the Thoroughbred industry is responsible for 13,567 FTE.
Participants are involved in the production of racing animals, the running of race clubs and New
Zealand Racing Board (NZRB) operations. Most of them (74%) are involved in breeding and training
operations.
Because of its popularity, Thoroughbred racing and breeding is the major equine-based industry in
New Zealand. During the 2008-2009 season 5826 horses, trained by 374 public trainers and 413
‘permit to train’ holders, ran in 3088 races (Anonymous 2009a). On an international scale, New
Zealand is ranked around eleventh in the numbers of horses starting in races each season (Fennessy
2010). Races were held on 51 different racetracks around the country. The total prize money was
$58,411,092, with an average of NZ$18,915 per race. The average earnings per runner per season
have risen by 50% from NZ$6,524 to NZ$10,026 in the last 4 seasons.
It is estimated that more than 1 million visitors attend race meetings in New Zealand each year.
These visitors generate a significant impact on the economy with their spending at the racecourse
and in the community (such as the transport and accommodation associated with attendance at the
races). A study commissioned by the NZRB in 2004 revealed that visitors spend in excess of NZ$54
million as a direct result of racing. For the 2008-2009 season total bets placed accounted for
NZ$465,924, 485 (Anonymous 2009a).
The on-going success of the Thoroughbred racing industry relies mainly on races for 2-year-old
horses. Therefore, the production of horses for these races is also very important. For the 2008-2009
season 8326 broodmares were registered, of which 6483 mares were served by 166 different
stallions. Approximately 4288 foals were born. This ranks New Zealand around eighth internationally
in the number of foals produced (Fennessy 2010).
The New Zealand Thoroughbred industry has a significant export focus of not only young stock
(yearlings) but also young promising racehorses. In the season of2008-2009 1354 Thoroughbreds
were exported, mainly to Australia (768), Singapore (220) and Hong Kong. (170) (Table
1)(Anonymous 2009b).
TABLE 1: Exports of Thoroughbreds in 2008-2009 season (taken from Anonymous 2009b)
9
2.4
Training of Thoroughbred horses
Beginning of training and training programme
Breaking to saddle and basic preparatory fitness work begins when the horses are aged 18-21
months and takes about 8 weeks. Usually the horses are spelled after breaking.
In New Zealand most trainers use a relatively standard training programme for all 2-year-old horses
up to trialling. In general, 2-year-old training consists of 6 days per week slow cantering (7-9 m/s) at
distances of approximately 2400-3200 m for about 4 weeks, fast cantering (9-11 m/s) at the same
distance for another 4 weeks, and then continuation of fast cantering with galloping (14-15 m/s) 3
times per week. Initially, horses start with others in jump-outs, which are prerace training sessions
that usually involve a 400-600 m sprint at near-racing speed, or time trials, which are unofficial races
managed by the New Zealand racing authorities, used to assess race potential and readiness. Trials
for 2-year olds are at 450, 600 and 800 m and 3-year old trials are at 800 and 1000 m. After suitable
performance, the horse is entered in an official race (Rogers et al. 2008; Rogers et al. 2010).
Fig 5: The average time from entering training stables after pre-training until the first competitive start (race trial or race
start) is approximately 10 weeks in New Zealand (Perkins et al. 2004a).
Older horses in full race training would typically exercise at canter 6 days per week and gallop 2-3
times per week, depending on their racing schedule (Verheyen et al. 2006). The average amount of
starts per horse per year is 5.9 (Anonymous 2009b).
This structure of training racehorses is similar within Australasia. In the UK many horses are trained
at gallops rather than on racecourses and so this influences the nature of the canter work the horses
are exposed to. However, the basic pattern of cantering 6 days a week still occurs depending on the
horses’ racing schedule (Verheyen et al. 2006). In the USA a classical training regimen refers to
galloping (which equals ¼-½ pace or 7.3-10 m/s in NZ, see next paragraph) horses 1 to 1.5 miles (1.62.4 km) a day and breezing (fast gallop or 15-16 m/s in NZ) every 7 to 10 days. The breeze distances
may be up to 0.5 miles (0.8 km) or even more (Boston and Nunamaker 2000).
Workload during training
Training of racehorses focuses on running horses at specific speeds. Trainers often use broad
definitions of gait and velocity, just defining whether the horse has to trot, canter or gallop is not
enough. Before every work out of every horse, the trainer tells the rider how he wants the horse to
be ridden by using a description of the gait, referred to as ‘subjective gait’. The different subjective
10
gaits are viz canter, ¼ pace, ½ pace and gallop (Rogers and Firth 2004).
Canter is a 3-beat gait that can be performed at a range of speeds up to 48 km/h (14 m/s). At around
this speed, the gait changes to gallop. This is a 4-beat gait whereby the stride lengthens and the
period of suspension increases. At gallop speeds of up to 60 km/h (17 m/s) can be reached.(
(Verheyen et al. 2006).
The trainer determines the daily workload of each horse and instructs the rider at what subjective
gait the horse should be worked, the track used and the distance and time taken for the work. The
rider then exercises the horse at this subjective gait and tries to meet the requested workload by
using a combination of a stopwatch and his ‘feel’ for a horse’s work intensity. Consequently, actual
work intensity for individual horses is not clearly defined, because it is based on the subjective
evaluation of the rider. Therefore, other variables are frequently used by trainers. At the
International Conference on Equine Exercise Physiology (ICEEP7), about 70 people attended a
workshop on workload and conditioning and the variables they used to measure workload were:





Heart rate was used by ± 50% of the participant
Run distance by ± 50%
Blood variables by ± 50%
Blood gas analysis by ± 10%
Muscle biopsies by ± 5% (Rogers et al. 2007)
In 2004 a study was performed on the subjective criteria of training workload and it identified that
the velocity of ½ pace decreased progressively over the weeks prior to the introduction of gallops
(weeks 6-8) (Fig 6). Once gallops had been introduced to the training programme (after week 8), ½
pace velocity also varied significantly between days of the week. On Mondays the horses worked
faster, while on Thursdays they had a significantly lower velocity when compared to the mean for the
whole week.
Half pace velocity was also slower on days that the horses were galloped than on the days they did
not gallop.
Fig 6: Mean (±SD) velocity (m/sec) for ½ pace by week, before (dark dot) and after (light dot) gallops were introduced to the
training programme of 2-year-old Thoroughbred racehorses (n=7) trained on turf, plough or sand tracks (Rogers and Firth
2004).
11
A further problem with the present descriptions of training and racing preparation of Thoroughbreds
is the conflicting language ascribed to different subjective gaits in different industries and countries.
This results in variable and confusing terms. The term ‘galloping’ for instance, is in the UK and New
Zealand reserved solely for the fastest of the exercise gaits (15-16 m/s). This, while in the USA
galloping is slower (7,3-10 m/s) and the fastest gait is called ‘breezing’(15-16 m/s) (Rogers and Firth
2004).
TABLE 2: Variability in terminology between countries (taken from Rogers and Firth 2004).
Training of young horses: exercise during development
Most of the training and conditioning of equine athletes occurs after skeletal maturity. The training
of racehorses is an exception to this. Here, the training population is dominated by 2-and 3-year-old
horses (Perkins et al. 2004b). This is caused by the fact that races for 2-year-old horses account for
much of the success of the Thoroughbred racing industry (Anonymous 2009). Besides that, there is
also an increasing demand from Asian countries for young (less than 5-years-old) New Zealand-bred
racehorses (Perkins et al. 2004a).
Epidemiological studies conducted in Australasia have identified that horses that started racing as 2year-olds had longer and more successful racing careers than horses that started racing at a later
age. Several prospective epidemiological studies of a large cohort of horses quantified the effect of
early exercise on the musculoskeletal system. The overriding theme of these studies was that early
exercise and training stimulate the development of the musculoskeletal system, without evidence of
any harm. These findings indicate that it may be of importance for the industry to place greater
emphasis on the early development of the athlete (Bolwell et al. 2010).
In young horses put into training, bone is exposed to new stresses. The high strains that are involved
with running at high speeds may predispose to micro damage when introduced in the adult. Since
bone is a dynamic tissue that responds to mechanical deformation, if these patterns of gait are
introduced gradually during the growth and modelling phase of skeletal development, appropriate
skeletal architecture can be attained with lower risk of damage. The bone will rapidly remodel to
enhance its ability to withstand stress, by decreasing bone porosity and increasing bone trabecular
width and mineralizing surface. This, combined with the incremental increases in training load during
development, will also increase skeletal mass and may reduce the strain magnitudes that would
12
occur if training were introduced after skeletal development, and thus minimize injuries.
Because the bone of younger hoses (2-year-olds) is less stiff, greater strains (bone movement) have
been measured during high speed exercise as compared to older horses. This may lead to high-strain,
low-cycle fatigue of the bone and subsequent bone pain (dorsal metacarpal cortex, sesamoid bones,
caudal metacarpal condyle) (Hinchcliff et al. 2007).
Nunamaker et al. (1990) tried to determine the mechanisms involved in fatigue failure of bone
leading to fracture, by measuring surface bone strains on the third metacarpal bone of young and old
North American Thoroughbred horses. The fatigue failure point of a bone depends on both the
magnitude of the deformation and the number of loading cycles to the failure point. It was identified
that young horses have greater levels of strains at the gallop, with almost a 40% reduction in strain in
the older horses. Thus if the strain magnitude is reduced, which is what happened in the older horses
that adapted at a young age, there can be a greater number of cycles of deformation prior to
reaching the failure point (Hinchcliff et al. 2007).
Hinchcliff et al. (2007) suggest that changes in shape of the third metacarpal bone during growth and
maturity may represent a mechanism to reduce strain and mitigate fatigue failure. The high
incidence of fatigue failure, as seen in ‘bucked shins’ in young horses subjected to exercise that
induces high strains over short periods of time, would support this hypothesis.
2.5
Bone and tendon reactions
Bone and cartilage adaptation to training and exercise
Increased exercise in the adult horse results in thickening of the trabecular part of the subchondral
bone, a thickening of the subchondral plate, and a thickening of both the calcified and hyaline layers
of the overlying articular cartilage. Experimental studies have also shown the sensitivity of cancellous
bone to changes in mechanical loading. These data can be indirectly related to training methods in
the horse.
Cartilage is well designed to tolerate exercise and able to handle larger biomechanical stress,
particularly in anatomical sites that receive a high load. Indentation studies on equine articular
cartilage from exercised and non-exercised horses demonstrated clear differences in biomaterial
properties of the cartilage. Sites of higher loading had greater changes, with the most dramatic
changes in cartilage permeability. Exercise promotes water to flow out of the cartilage on loading,
which is a known mechanism for cartilage lubrication. Besides that, chondrocytes increase their
production and quality of proteoglycan to increase the compressive stiffness of cartilage. It takes at
least 3 weeks of training before an increase in total proteoglycan content is measurable. When the
stress of exercise exceeds the capability of the cartilage to adapt, structural damage occurs. Almost
all equine athletes with an extended career will have some degree of osteoarthritis.
There is no consensus whether the influence of exercise on healing of injured articular cartilage is
beneficial or neutral, although assimilation of the studies would suggest a beneficial effect as long as
the impact trauma is below the level of repair tissue destruction (Hinchcliff et al. 2007).
Evidence majority injuries/wastage occur during training: microtrauma
Training of horses can have either a positive or negative effect on one or more connective tissues.
There is good evidence that the majority of injuries and wastage occur during training rather than at
the race track (Boston and Nunamaker 2000; Verheyen and Wood 2004; Verheyen et al. 2005).
13
In the process of preparing an athlete for competition the workload is intensified, which causes
tissues to attain greater resistance to the increasing forces that deform them. This force can be
excessive though, when the training is inappropriate in terms of the distance, velocity, change in
workload undertaken or rest periods. As a result, progressive damage can occur, such as micro-cracks
in bone or collagen fibril changes in tendon (Rogers and Firth 2004).
Studies performed in a variety of countries have indicated that broad patterns of injuries in racing
Thoroughbreds are similar. The most common type is musculoskeletal injury involving the lower
forelimb. There are also regional differences in risk for specific types of injury. In Britain, for example,
the most common musculoskeletal injury in racehorses involves tendons and ligaments of the lower
forelimb, while in the USA the most common injury involved the proximal sesamoid bones of the
lower forelimb. These differences may reflect regional differences in racetrack composition, race
characteristics, training methods, horse populations and variation in study design and case definition
(Perkins et al. 2004b).
Pathogenesis of tendon injury
Tendon and ligaments can be injured by either overstrain or percutaneous penetration/laceration.
The latter will not be considered further here. Overstrain injuries can occur by one of two
mechanisms. First, they can result from a sudden overloading, which overwhelms the resistive
strength of the tendon/ligament. This is probably the mechanism for most ligaments and some deep
digital flexor tendon (DDFT) injuries in the horse. However, most strain-induced injuries involving
palmar soft tissue structures of the metacarpal region are caused by the second mechanism,
whereby the clinical injury is believed to be preceded by a phase of degeneration. This preceding
degeneration is also referred to as ‘microtrauma’. The evidence for this microtrauma is based on 4
observations:




The identification of ‘asymptomatic’ lesions in post-mortem studies of normal horses. These
lesions could be identified both grossly and microscopically.
Almost all clinical strain-induced tendinopathies are bilateral, with one tendon more severely
affected than the other. In many cases, a careful ultrasonographic examination of the
contralateral limb will reveal that it has changes as well. Even in the seemingly unilateral
cases, blood-flow studies have demonstrated an increased blood flow to the ‘normal’
contralateral tendon, which suggests that it is not totally unaffected after all.
Epidemiologic studies have demonstrated close associations between age and exercise, and
tendon injury, which suggests that number of loading cycles is important.
Experimental investigations have demonstrated evidence of degeneration associated with a
synergistic action of both age and exercise.
The actual mechanism for the degeneration of the tendon is currently unknown, although there are
several possibilities for either physical or metabolic processes:


Direct damage can be caused by the physical energy imparted to the tendon under weightbearing load, resulting in the disruption of either cross-links or actual matrix proteins.
Energy lost through hysteresis is an indirect physical effect of weight bearing, which results in
a temperature rise within the centre of the tendon. Although tenocytes in the superficial
digital flexor tendon have shown to be resistant to these temperature rises, the matrix
proteins could still be damaged.
14


Loading cycles can induce cellular activity with potential release of proteolytic enzymes.
Cleaved matrix proteins can be generated from either direct physical forces or from
enzymatic cleavage. These can also provoke further matrix degradation.
The degeneration process can be likened to ‘molecular inflammation’, which does not provoke a
repair process, as after clinical injury, but rather progressively weakens the tendon. Since the tendon
is already operating close to its tolerance limit, any change in the structural properties does not have
to be great. When the highest stresses encountered by the tendon overwhelm its structural integrity,
irreversible damage is caused and clinical injury occurs. The physical disruption of the tendon matrix
varies in degree from fibrillar slippage, with breakage of cross-linking elements, to fibrillar rupture
and, in some cases, complete separation of tendon tissue. Initially there is intratendinous
haemorrhage, usually followed rapidly by a pronounced inflammation reaction. As with any
inflammation reaction, this is characterized by an increase in blood flow, the development of edema,
infiltration of neutrophils, macrophages and monocytes, and the release of proteolytic enzymes.
Although this is the earliest stage of repair and designed to remove damaged tendon tissue, the
response is usually excessive and causes further damage to the tendon.
Within a few days after the inflammatory phase, the reparative phase of repair begins. This consists
of a pronounced angiogenic response and the synthesis of scar tissue. Scar tissue has a different
composition to tendon, having a higher ratio of collagen types III/I (~50%, compared to 10% for
normal tendon), higher levels of glycosaminoglycans, and much lower levels of COMP.
The reparative phase merges with the remodelling phase, in which the gradual, but incomplete
transformation of collagen type III to type I takes place as the scar tissue matures. The new collagen
fibrils become thicker and cross-linked. The end result is the replacement of normal tendon tissue
with scar tissue. The healed tendon becomes strong, but is functionally of inferior quality compared
to normal tendon. Mature scar tissue is less stiff as a material than tendon tissue, but because large
amounts of scar tissue are formed, the scarred tendon often becomes stiffer as a structure than the
original tendon. The increase in stiffness compromises the tendon function and predisposes to
reinjury, which often occurs at sites adjacent to the original injury (Hinchcliff et al. 2007).
Pathogenesis of bone fractures
Since the Thoroughbred horse was selected for its speed, the requirements for the skeleton in these
horses are those of low mass and high strength. Bone matrix compromises approximately 65%
inorganic material, mostly hydroxyapatite, and 35% organic material and water. As with all
connective tissue, the matrix has a high proportion of water (approximately 25%, depending on the
type of bone). The remaining matrix is made up of predominantly type I collagen and a small
proportion of minor collagens and non-collagenous proteins, such as proteoglycans and
glycoproteins.
As stated earlier, bone can be adapted to loading by short periods of cyclical loading. By prolonging
this type of exercise or introducing long periods of such exercise too rapidly, particularly on a hard
surface, micro cracks may be induced which can ultimately lead to gross fracture of the bone.
The micro cracks are seen as damage within the bone matrix. They can alter the interaction between
cell and matrix and cell-to-cell communication, such as the generation of streaming potentials by
movement of ionic fluid through the macro-, micro-, and nanoporosities of bone. This may induce
processes such as osteocytic apoptosis and osteonal remodelling.
During osteonal remodelling, the formation of secondary osteons increases in order to limit crack
propagation and to replace damaged matrix. The disadvantage of this remodelling is that secondary
15
bone has been shown to be inferior in terms of material properties of the bone as a structure, which
can ultimately lead to fracture. This permanent change in bone type resembles those changes seen in
studies on the induction of bucked shins, using a high volume of high strain rate exercise.
Thus, to prevent the reduction of the mechanical properties of the overall bone structure, undue
secondary osteon formation must be avoided. A more controlled osteogenic exercise provokes a
more gradual adaptive response and increase in bone mass, with minimal damage of the matrix. This
does preserve the mechanical properties of the bone and reduces the risk of catastrophic failure.
Training regimens that appear to optimize bone adaptation without matrix damage comprise short
periods of high-intensity exercise (Hinchcliff et al. 2007). This is supported by the study of Parkin
(2008), who concluded that short bursts of high-speed training in the months before racing reduces
the fracture risk. This agrees with data showing increased bone formation with short periods of fast
work. Boston and Nunamaker (2000) also report the advantages of short-distance breezing rather
than long-distance gallops in reducing bucked shins.
The study of Verheyen et al. (2006) indicated a potentially protective effect of accumulating highspeed exercise with regard to skeletal injury. Higher amounts of cantering, especially within short
time periods, have been associated with an increased risk of injury and in particular with stress
fractures.
Safety factors
The skeleton can withstand occasional overloads, because there is a build-in safety factor to
accommodate unforeseen overload. This safety factor is a compromise between minimizing the risk
of structural failure and the high costs in terms of energy requirements for the excess of material.
These factors may explain the findings that bone mass is minimized at the distal extremities and
safety margins decrease toward the distal extremity of the limbs. This has been related to the
incidence of fractures in race horses being higher in the distal bones than those located proximally
(Hinchcliff et al. 2007).
Cumulative amount of work (work history)
Injury to the musculoskeletal system accounts for most (78-83%) injuries which occur in
Thoroughbred racehorses. These injuries have been associated with training and racing intensity, but
with apparently conflicting results. For example, German Thoroughbred racehorses suffered injuries
more frequently when training intensity increased in preparation for the racing season (Lindner and
Dingerkus, 1993), while Minnesota racehorses with relatively more rigorous training schedules
suffered injuries less frequently (Kobluk et al., 1990), and New York racehorses with lower lifetime
racing frequencies were at greater risk for severe racing injury (Mohammed et al., 1991). Estberg et
al. (1995) hypothesized that the different results from these studies might be related to the great
difficulty in accurately describing the exercise intensity over a season or entire racing career. They
found out that the relative risk for fatal musculoskeletal injury was significantly greater for horses
which ran higher cumulative racing and timed workout distances than their cut-off distances
estimated from the control horses over 2-month periods.
This could be interesting for Thoroughbred trainers, because right now it is common practice to just
record the distance and times of fast workouts. They use these data to get an idea of the race
preparedness of the horse. It would be better, however, to record the cumulative amount of work
(work history) performed by a horse over a period of training and racing (or a lifetime). This way, you
not only get an idea of the race preparedness of the horse, but also how intensively the horse has
worked in the previous period and thus how susceptible it could be to musculoskeletal injuries.
16
The cumulative workload index (CWI ) can be used to analyse the work history of a horse, because it
produces a cumulative exercise parameter for each horse. The CWI is the product of the average
velocity and distance at a certain gait or training session. The average velocity is calculated from the
distance and time of each horse’s daily workout data (Rogers and Firth 2004).
2.6
Measurement tools in training
Non-differential GPS
Speed is the rate of change of position; its determination requires measurements of distance and
time components. The most commonly used method for determining a horse’s speed is a simple
stopwatch. For this study however, accurate determination of a horse’s speed is fundamental.
An increasingly popular method of determining an individual’s position is the Global Positioning
System (GPS). Originally, GPS was only developed as a military tool, but now it is widely used.
Basically, GPS comprises a network of satellites that are controlled by the ground-station (the GPS
receiver). These satellites emit low power radio signals containing atomic clock time data (they all
have a clock set to exactly the same time). The ground-based GPS receiver uses the transit-time
delays in these time signals to triangulate position (Fig 7).
Fig 7: Principles of GPS (taken from http://www.aero.org/education/primers/gps/howgpsworks.html)
Since it was developed as a military tool, the US government limited the potential accuracy of the
system by introducing small random errors (termed selective availability, or SA) into the satellite
clock signals. This spurred the development of several approaches to enhance the accuracy of GPS.
The discontinuation of SA in 2000 meant that the accuracy of standard, non-differential GPS is
17
improved for position and possibly for speed determination. The positional accuracy of GPS since SA
removal has been determined, but validation of non-differential GPS for velocity determination has
not been undertaken. Manufacturers quote accuracies in the region of 0.1-0.2 m/s, with the specific
algorithm used being the variable which most influences accuracy between manufacturers. Further
information on how the system calculates speed and the limitations of the system are not published,
due to commercial confidentiality.
The accuracy of GPS is influenced by several variables. The number of satellites is clearly important,
to be able to get a 3D position fix a theoretical minimum of four satellites is required. Besides this,
the geometrical arrangement of the satellites relative to each other and the receiver also affects the
quality of the triangulation for position. This is quantified in a measurement known as dilution of
precision (DOP). The greatest predicted accuracy of triangulation (with a DOP of 1) will be seen when
one satellite is directly overhead and the remainder are equally spaced around the horizon. Higher
DOP values are seen if the satellites are tightly clustered overhead, with the maximum value of 50
meaning that the fix is unreliable.
The orientation of the satellites and the identity of the satellites used clearly changes over time, thus
experimental conditions cannot be completely standardised.
In 2004 a study was performed on the accuracy of non-differential GPS for the determination of
speed over ground, using a bike on a 400 m running track. The study showed that the speed
determined by the GPS receiver was within 0.2 m/s of the true speed measured for 45% of the
values. A further 19% of the values was within 0.4 m/s. A negative error (GPS underestimation of
speed) of greater than 1.0 m/s was seen in 12.6% of samples, while a positive error (GPS
overestimation of speed) of greater than 1.0 m/s was seen in 2.9% of samples. Thus, the speed error
was slightly skewed towards an underestimate of true speed (Fig 8.)
Fig 8: Distribution of speed error (difference between wheel and GPS speed in m/s, with a positive value meaning that GPS
speed is higher than the wheel speed)
There was a consistent inaccuracy in speed determination seen during bend cycling (Fig 9), with a
general trend toward underestimation of speed. Initially, the GPS is still able to accurately determine
the speed of the cyclist, but shortly after the onset of the bend the GPS tends to overestimate speed.
This is followed by a rapid fall in speed determined by the GPS, resulting in an underestimation for
the remainder of the bend. This underestimation of speed during bend cycling can also be seen by a
18
left skew in the data in Fig 8.
Fig 9: Inaccuracy in speed determination during bend cycling. Actual and GPS speed for two laps at the track at a speed of 15
km/h. The white circles represent actual bicycle speed (m/s) and the dark circles represent the GPS speed (m/s). The solid
horizontal bars represent the time spent on the bends (Witte and Wilson 2004).
They also tested the accuracy of GPS during a series of rapid speed changes (the maximum the cyclist
could achieve, so this might be less than a horse could achieve), comparing it to the golden standard
of the wheel speedometer. The GPS followed acceleration and deceleration reasonably well, but was
less accurate than the speedometer in following the transitions from acceleration to deceleration (Fig
10) (Witte and Wilson 2004).
Fig 10: Actual and GPS speed for a series of rapid stop-start events. The actual bicycle speed is represented by the white
circles and the GPS speed by dark circles(Witte and Wilson 2004).
Differential GPS (dGPS)
Differential GPS compares the known position of a fixed receiver with the position determined by
satellite triangulation and then uses this difference to correct the transit time of individual satellite
signals. It is currently not clear however, if these improved positional accuracies of dGPS also
enhance the accuracy of speed determination. This, because GPS speed determination does not rely
19
solely on differentiation of position data over time, but also depends on Doppler shift of the carrier
wave.
The use of two neighbouring receivers (carrier wave differentiation) could determine the phase
difference in the carrier wave signal from a satellite and thereby increase the positional accuracy
even further. For running with this system, accuracies with a standard deviation of 0.03 m/s have
been reported. Because the equipment is both costly and bulky (units weigh 2 kg or more), it is of
limited potential for many studies of field locomotion (Witte and Wilson 2004).
TurfTrax Racing Data System
Acquiring a reasonable sample of data on speed and distance profiles within races has been
extremely difficult in the competitive environment, since attachment of a GPS or inertial sensors to
the horse and/or jockey has been forbidden during Thoroughbred racing. The TurfTrax system is a
wireless radio tracking system, using a proprietary radio-transmitter system consisting of emitting
tags placed in the number cloth of each competitor and a set of fixed antennae around the
racecourse. The tag consists of a small black plastic housing (114 x 95 x 18 mm, containing the radiotransmitter electronics and battery power supply), attached via a 32 cm cable to an antenna,
(measuring 50 x 30 x 11 mm). The tag, antenna and cable are stitched to the inside of the
saddlecloth, with the tag on the flank of the horse and the antenna about 7.5 cm from the spine. This
arrangement is approved by the Jockey Club following veterinary and trainer consultation.
Signals emitted by the tags are picked up by base station antennae surrounding the course and are
relayed to a computer where a real-time processing system produces a live feed of position and
speed of each horse during the race, updated 4 times per second. In radio location systems there are
two major sources of error: multipath (erroneous signals reflected off objects, such as buildings) and
pack blocking effects. To provide resilience against these effects, the TurfTrax system uses software
algorithms that combine phase and Doppler velocity information. Typically, a tag is received by at
least 8 base stations, allowing for a safety margin in triangulation of position on the racecourse.
The accuracy of the TurfTrax system in measuring speed and position was determined in a study
using a quad-bike. Here, the TurfTrax system was compared with a golden standard carrier wave dual
frequency differential GPS system. The two systems provide very similar estimates of speed during
periods of low acceleration, but diverged during rapid speed changes. In the GPS data, spikes are
evident due to gear shift of the quad-bike, which occur at similar speeds during each acceleration.
These gear shifts are completely filtered out in the TurfTrax system. During periods of large positive
or negative acceleration (>0.5 m/s2), the TurfTrax data appear to give a smoothed account of
dynamics.
As can be seen from the figure below, the distribution of the difference in speed estimates shows a
large central peak and long tails. The long tails are representative of the regions of high acceleration.
The central peak is corresponds to the low acceleration range where the systems are in strong
agreement. The spread of the distribution around the peak is on the order of 0.15 m/s, meaning that
the TurfTrax estimate of speed is within 0.15 m/s of the dGPS systems during periods of low
acceleration (<0.5 m/s2) (Spence et al. 2008).
20
Fig 11: Comparison of the speed estimates of the TurfTrax and dGPS systems. Overlay of speed vs. time for both systems
during a run (A), and the distribution of the difference between the speed estimates (B). Both systems give very similar
estimates of speed, with the TurfTrax system yielding smoother data. For periods of low acceleration, where the data is
similar, the bulk of the data fall within 0.15 m/s of the mean value (Spence et al. 2008).
21
3. Reason for performing study
Training is an important variable for determining the success of a racehorse. Nonetheless, there has
been minimal scientific evaluation of racehorse training programmes. Training focuses on running
the horses at certain speeds in order to meet the daily workload the trainer determines for each
horse. The rider is instructed at what subjective gait the horse should be worked, the track used and
the distance and time taken for the work. The rider then exercises the horse at this subjective gait
and tries to meet the requested workload by using a combination of a stopwatch and his ‘feel’ for a
horse’s work intensity. Consequently, actual work intensity for individual horses is not clearly
defined, because it is depending on the subjective interpretation of the rider. The purpose of this
study was to determine the exact speed during training, using a global positioning system (GPS) and
see if there’s a significant difference between the average GPS speed and the average speed
recorded by the trainer.
By using the average speed over a certain distance, we assume that the horse is running at a
constant speed. It may well be, however, that the horse is not running at an equal speed for the
whole time, but instead is constantly accelerating and decelerating. It is possible that these constant
accelerations cost more energy and predispose horses to injuries.
Hypotheses:
In this project the following hypotheses were tested:
That there will be a significant difference between mean trainer’s velocity and mean GPS velocity.
That the mean acceleration during gallop will be faster than during canter
That the variation in velocity will decrease during the period of training.
That the variation in velocity will be smaller in good performing horses, than in poor performing
horses.
That the variation in velocity will be smaller during gallop than during canter.
22
4. Materials and methods
Horses
For this project nineteen healthy Thoroughbred racehorses age 3 years (15 fillies and 4 geldings)
from a single training stable were studied. The horses were in training as part of a larger project by
the Global Equine Research Alliance investigating the effect of preconditioning on musculoskeletal
health (Rogers et al. 2008).
Training protocol
The horses were trained 6 days a week on a training track located in the lower half of the North
Island of New Zealand (Foxton Racing Club, Inc.) consisting of flat grass and sand-based working
surfaces. The exercise direction was alternated daily. The diameter of the main grass track was 2000
m.
Fig 12: Training track at Foxton (taken from Google Earth)
Initially the workouts consisted of trotting and cantering. As the training progressed fast workouts
were introduced that consisted of a canter warm-up followed by a gallop. Fast and slow days were
alternated throughout the training period.
Typical week of August:
Day 1: 250 m trot and 2346 m canter on the 2 year old sand track.
Day 2: 250 m trot and 2796 m canter on the 2 year old sand track.
Day 3: 250 m trot and 2796 m canter on the 2 year old sand track.
Day 4: 200 m trot and 2846 m canter on the 2 year old sand track.
Day 5: 250 m trot and 2346 m canter on the 2 year old sand track.
Day 6: 200 m trot and 2846 m canter on the 2 year old sand track.
Day 7: Day off.
Typical week of December:
23
Day 1: 250 m trot and 2346 m canter on the 2 year old sand track.
Day 2: 200 m trot, 990 m canter and 800 m gallop on the outside sand track.
Day 3: 200 m trot and 2896 m canter on the 2 year old sand track.
Day 4: 100 m trot, 890 m canter and 1000 m gallop on the outside sand track.
Day 5: 250 m trot and 2346 m canter on the 2 year old sand track.
Day 6: 200 m trot and 2896 m canter on the 2 year old sand track.
Day 7: day off.
During the 5 month period, the horses also participated in ‘jump out’ sessions in starting gates, race
trials and race meetings.
GPS and HRM
Prior to each training session horses and riders were fitted with a combined GPS and heart rate
monitoring (HRM) system (Equitronic Technologies, Australia). The GPS data logging unit was held in
a pocket in the saddle blanket, with the antenna positioned in a racing cap placed over the rider’s
protective helmet, and the HRM electrodes were placed under the girth and saddle with the rider
wearing a watch that recorded and displayed the HRM signal (Kingston et al., 2006).
The GPS unit had a sample rate of 1Hz. Previous assessment of the accuracy of the GPS unit showed a
speed variation of up to 0.6% (Gramkow 2006). The unit specification indicated a precision of 1 km/h
(0.28 m/s). The trainer timed each training session and recorded each horse’s time and distance
covered each day. These data were recorded in a work diary and regularly logged onto a computer
spread sheet. The GPS and HRM data were merged and downloaded daily and converted into data
files that contained speed with corresponding heart rate for each 5 second interval.
Data were not recorded for every horse for every day (e.g. during trials and jump outs) and some
data were lost during heavy rainfall due to technical problems. Only those data that provided
complete daily work were used as potential data for analysis (approximately 88% of all workouts
during the 3-year-old training period).
All animal based procedures reported in this study were approved by the Massey University Animal
Ethics Committee.
Analysis of GPS data
Before statistics could be done, the GPS data had to be examined and cleaned. Descriptive statistics
and scatterplots were used to identify outliers and histograms were used to test the distribution of
the data. The statistical techniques used are described for each hypothesis.
24
Hypothesis 1 That there will be a significant difference between trainer’s velocity and GPS mean
velocity.
Canter
Fig 13: Typical plot of velocity of one horse during a canter training session.
To examine the variation between trainer and GPS canter velocity the workout was divided into
separate phases. The key features used for analysis were:
1: Minimum GPS velocity: lowest point and therefore the start of the acceleration phase.
2: Minimum GPS20 velocity: the endpoint of the acceleration phase and the beginning of the
constant phase. This point had to be above 20 km/h.
3: Maximum GPS velocity: the maximum speed reached during the constant phase.
4: End of constant phase: the horse start decelerating again, this is the last point where speed is
above 20 km/h.
5: Mean trainer velocity: average speed calculated by trainer, using the time timed by his stopwatch
and the distance covered on the track.
The ‘Mean trainer velocity’ assumes that the horse is running at constant speed. The GPS data
demonstrated that all workouts were in fact a constant acceleration.
To permit comparisons selected sections of the GPS data were selected for analysis. Within the GPS
data the training sessions were divided into two phases: the initial acceleration phase, when the
horse was still accelerating to reach the desired training speed (canter), and second the constant
phase, when the horse has reached this minimum desired speed. We assumed that any further
decelerations or accelerations after this point reflected the natural variation of the horse around the
mean.
The distinction between the acceleration- and the constant phase was made by the ‘Minimum GPS20
velocity’. This was defined as the start of canter velocity, which was set at 20 km/h.
25
After determining these points, two mean velocities were calculated:


The ‘Mean GPS velocity’, which refers to the mean velocity during both the acceleration- and
the constant phase. This will give us a general view of the mean velocity during the whole
work out. In the GPS graph, this is the mean velocity from point 1 to 4.
The ‘Mean GPS20 velocity’, because this would probably match the ‘Mean trainer velocity’
better: the trainer starts counting when the actual canter work has started, he doesn’t
include the acceleration phase in this. In the GPS graph, this is the mean velocity from point 2
to 4.
Gallop
Fig 14: Typical plot of velocity of one horse during a gallop training session.
The GPS graph of one of the horses during gallop work is shown above. In this figure the numbers
refer to certain moments of the training.
1: Minimum GPS velocity: start of the acceleration
2: Minimum GPS20 velocity: minimum value that has to be above 20 km/h
3: Minimum GPS40 velocity: minimum value that has to be above 40 km/h
4: Maximum GPS velocity: the maximum speed reached during the constant phase.
5: Cut-off point: assume that after this point the workout is finished and the horse is slowing down
for the cooling down.
Analysing the gallop data also provided us with a few problems. The graph shows that the gallop
workout doesn’t have anything like a constant phase at all, it is actually a constant acceleration.
26
Therefore, it was difficult to decide between which points we should calculate the mean velocity that
we could compare with the ‘Mean trainer velocity’.
Two mean velocities were calculated, with the cut-off point for both of them at 40 km/h. We chose
this point as the cut-off value, because we assumed that below 40 km/h the horse was slowing down
so quickly that the distance covered in those lower speeds couldn’t contribute to the workout
anymore. It was also good to have a single cut-off point for both of the mean velocities, and for the
gallop (defined as a velocity above 40 km/h) this needed to be at 40 km/h.


Mean GPS20 velocity: which refers to the mean velocity during the last part of the
acceleration phase (accelerating from 20 km/h to 40 km/h) and the ‘real’ gallop work
(velocity of above 40 km/h), see point 2-5.
Mean GPS40 velocity: which refers to the mean velocity during the ‘real’ gallop work.
Therefore, the velocity had to be above 40 km/h, see point 3-5.
We chose to determine these two mean velocities, because one of them would describe the mean
velocity during the whole workout (‘Mean GPS20 velocity’, thus including part of the acceleration
phase), since a huge part of the workout is not actual gallop work.
The other one would describe the mean velocity during the workout that the trainer timed with his
stopwatch (‘Mean GPS40 velocity’, which had to be above 40 km/h, thus only the actual gallop work).
This is the mean GPS velocity we could compare with the mean trainer’s velocity.
Hypothesis 2 That the mean acceleration during gallop will be faster than during canter
Canter
Fig 15: Typical plot of velocity of one horse during a canter training session
27
The GPS graph of one of the horses during canter work is shown above. In this figure the numbers
refer to certain moments of the training.
1: Start of the constant phase (t=0)
2: End of the constant phase
To test this hypothesis, the mean acceleration during the constant phase was calculated.
The start of the constant phase is defined as the first point when the horse did not accelerate
anymore.
The end of the constant phase was defined as the last point before the horse started to decelerate
again at the end of the work out.
The time of the constant phase was the amount of time (in seconds) between the start (point 1,
which is t=0) and the end (point 2) of the constant phase.
The mean acceleration was defined by calculating the acceleration between all following data points.
After this, the mean of the individual accelerations was calculated.
Gallop
Fig 16: Typical plot of velocity of one horse during a gallop training session
The GPS graph of one of the horses during gallop work is shown above. In this figure the numbers
refer to certain moments of the training.
1: Minimum GPS40 velocity: start of the true gallop phase (t=0)
2: Maximum GPS velocity: the maximum speed reached and end of the acceleration phase.
3: Time at end of the acceleration phase.
28
There was no constant phase in the gallop work; the whole workout was one prolonged acceleration.
To test this hypothesis, we wanted to determine the acceleration during the true gallop phase, with
the end of the workout being at Max GPS velocity.
The time at the end of the acceleration phase was time (in seconds) between the start (point of ‘Min
GPS velocity’, which is t=0) and the end of acceleration (point of ‘Max GPS velocity). The mean
acceleration was defined by calculating the acceleration between all following data points. After this,
the mean of the individual accelerations was calculated.
Hypothesis 3 That the variation in velocity will decrease during the period of training.
To describe the variation in velocity, the data of the constant phase of canter for the months August
and December were used. These data were trimmed and plotted. We removed the last part of the
acceleration and the beginning of deceleration (after last peak) so that we could look at the graph in
greater detail. An autoregressive moving average function with a two period lag was fitted to smooth
the data and the number of peaks was identified visually from the resultant output.
Hypothesis 4 That the variation in velocity will be smaller in good performing horses, than in poor
performing horses.
The same data and graphs were used as for hypothesis 3.
The distinction between good performing horses and poor performing horses was made based on
whether the horses had started in a race or not. Using these criteria, 8 ‘good performing horses’ and
11 ‘poor performing horses’ were identified. See table below.
TABLE 3: Racing history of horses used in GEXA study
Hypothesis 5 That the variation in velocity will be smaller during gallop than during canter.
The same data and graphs were used as for hypothesis 3 and 4, but now we also included the gallop
data of the month December. The data of gallop were also trimmed, using the same criteria as for
canter. An autoregressive moving average function with a 2 period lag was fitted and the number of
peaks was visually identified from the resultant output.
Statistics
All the workout data of all horses that were still in training were analysed for the months August and
December. In August, the horses only cantered.
To compare values between months a univariate GLM was fitted with month as a fixed factor and
29
horse ID as a random factor.
For comparisons between related variables (i.e. trainer recorded data and GPS data) a repeated
measures GLM was fitted with month as a fixed effect.
30
5. Results
Data were collected from 19 horses in August and 13 in December. The lower value in December was
due to removal of horses from training because of injury or lack of potential.
Data was not captured on every day; some days were lost due to heavy rain, satellite errors or public
holidays.
Hypothesis 1: That there will be a significant difference between trainer’s velocity and GPS mean
velocity.
Canter data of all horses were used of August and December.
There was a significant difference between Mean trainer velocity and Mean GPS velocity (mean ± std
error of 26.57 ± 0.15 km/h and 26.98 ± 0.15 km/h respectively, P=0.001), even though the
acceleration phase was still included in the data.
Removal of the acceleration phase and comparison of Mean trainer velocity to the Mean GPS20
velocity of both months made the difference even greater (26, 57 ± 0.15km/h and 29.36 ± 0.17 km/h
respectively, P=0.001).
Overall the general trend was an increase in velocity in December, irrespective of whether it was
trainer or GPS derived data (Table 4). The lack of horse effect across variables indicates that the
change in horse velocity was uniform. However, the magnitude of this change was not, which is
reflected by the significant Horse*Month interaction.
TABLE 4: Difference in canter velocity between August and December
N horses
Mean trainer
velocity
Mean GPS
velocity
Mean GPS20
velocity
Min GPS velocity
Min GPS20
velocity
Max GPS velocity
August
19
25.873 ± 0.174
December
13
27.268 ± 0.248
Month
Horse
Horse*month
P=0.026
P=0.604.
P=0.001
26.130 ± 0.136
27.979 ± 0.221
P=0.003
P=0.53
P=0.001
28.503 ± 0.188
30.350 ± 0.255
P=0.018
P=0.814
P=0.001
4.374 ± 0.091
21.173 ± 0.118
4.947 ± 0.123
21.214 ± 0.160
P=0.239
P=0.96
P=0.649
P=0.88
P-0.001
P=0.01
32.571 ± 0.274
34.473 ± 0.371
P=0.028
P=0.618
P=0.001
Gallop data were available for 13 horses in December, because there were no gallop trainings in
August.
The difference between Mean trainer velocity and Mean GPS velocity was significant (51.86 ± 0.37
and 35.13 ±0.33 km/h respectively, P=0.001), but this still included the acceleration phase.
Truncation of GPS velocity to those above 40km/h demonstrated that Mean GPS40 velocity was not
significantly greater than Mean trainer velocity (51.47 ± 0.32 and 51.86 ± 0.37 km/h respectively,
P=0.169).
31
TABLE 5: Gallop velocity in December
December
13
51.86 ± 0.37
35.13 ± 0.33
51.47 ± 0.32
4.56 ± 0.20
21.61 ± 0.23
41.01 ± 0.14
60.12 ± 0.55
N horses
Mean trainer velocity
Mean GPS velocity
Mean GPS40 velocity
Min GPS velocity
Min GPS20 velocity
Min GPS 40 velocity
Max GPS velocity
Hypothesis 2: Hypothesis 2 That the mean acceleration during gallop will be faster than during
canter
Canter data of all horses were of December.
The Mean canter acceleration was significantly lower than the Mean gallop acceleration (mean ± std
deviation of 0.024 ± 0.13 and 0.39 ± 0.14 respectively, P=0.001).
Hypothesis 3 : That the variation in velocity will decrease during the period of training.
Canter data of all animals for the months August and December were used. There was not a
significant difference in the total number of peaks between August and December (7.1 ± 2.6 and 6.9
± 2.7 respectively, P=0.23).
While comparing the data for the good performing horses and the poor performing horses, we found
that there was actually a month effect, but that we couldn’t identify it when looking at the
population as a whole. There was a significant month effect within the groups of good performing
and poor performing horses. See results of the next hypothesis.
Hypothesis 4: That the variation in velocity will be smaller in good performing horses than in poor
performing horses.
Canter data of all animals for the months August and December were used.
The difference in the number of peaks between the two groups was significant in August (P=0.003). It
was striking however, that for August the mean number of peaks was lower for the poor performing
horses than for the good performing horses.
The difference in the number of peaks between the two groups was also significant in
December(P=0.002), but now the good performing horses had a lower mean number of peaks than
the Poor performing horses.
TABLE 6: Mean number of peaks in good and poor performing horses
August
December
Good performing
7.9 ± 2.0
6.1 ± 2.6
Poor performing
6.8 ± 2.8
7.6 ± 2.6
P value
0.003
0.002
32
While comparing the number of peaks of the good and the poor performing horses, we saw that
there was not only a difference between these groups, but that there was actually a month effect,
but only within the groups.
As you can see from the figure below, the Poor Performing Horses (0) had a lower mean number of
peaks in August than the Good Performing Horses (1). The poor performing horses didn’t improve
during training however (they even deteriorated), while the good performing horses did.
This is why we couldn’t identify a month effect at first, because the improvement of the good
performing horses was cancelled out by the deterioration of the poor performing horses.
0
Fig 17: Mean number of peaks for good performing (1) and poor performing (0) horses in August and December.
When we split the groups, the month effect was significant for the Good Performing Horses
(P=0.001). For the Poor Performing Horses however, the month effect was still not significant
(P=0.059).
TABLE 7: Mean number of peaks for good and poor performing horses in the months August and
December
Good Performing
Poor Performing
August
7.9 ± 2.0
6.8 ± 2.8
December
6.1 ± 2.6
7.6 ± 2.6
P value
0.001
0.059
Hypothesis 5: That the variation in velocity will be smaller during gallop than during canter.
Canter data of all horses were used of August and December and gallop data of December.
We wanted to compare the mean number of peaks during canter and gallop, but it soon became
clear that the gallop training sessions were too different to compare to the canter training.
Therefore, this hypothesis was withdrawn from this project.
33
6. Discussion
Before analysing the GPS data, we expected that the horse would accelerate to a certain velocity and
that once that was reached, the velocity would fluctuate around the mean. The GPS graphs showed
however, that the horse keeps accelerating, even when the desired velocity is reached.
For canter, two phases could be identified in the graphs, with an initial phase of maximal acceleration
and a second phase of minimal acceleration where variation in acceleration did occur. Therefore, the
velocity did not fluctuate around the mean velocity as we expected, but instead the acceleration
fluctuates around the mean acceleration. This means that racehorses are trained in a different way
than what was intended. This different training pattern might also involve a different training load,
since the intensity of constant accelerations is probably higher than that of a constant speed.
A simple explanation for this could be that the jockey has trouble keeping the horse at ¼ or ½ pace,
because they are so excited to run, but it could also be a misperception of the speed by the jockey,
who gets used to the fast speed.
The gallop training showed a different pattern and consisted of just one long acceleration from start
to end, without these two phases that could be identified during canter. This is consistent with
Vermeulen and Evans (2006), who described a typical gallop training session that is used in the
training of Australasian Thoroughbreds:
1)
2)
3)
4)
Horses walked 50 m to the sand track.
Horses trotted 4 laps of the sand track (velocity of 4.2 m/sec for 6 min).
Horses walked 100 m to grass racetrack.
Horses completed one 2000 m lap of the grass racetrack.
a. Initially, horses trotted off at 4.2 m/sec for 100 m.
b. Horses then began cantering (8.3 m/sec) for 700 m.
c. Horses then galloped at a velocity of 14.2 m/sec for 800 m.
d. During the final 400 m of the lap, horses galloped with velocity increasing to
maximal. Generally, maximal velocity was between 16.7 and 19.4 m/sec.
5) Post 2000 m lap, horses velocity rapidly decelerated to a walk. Horses then trotted 350 m
until off the grass racetrack.
This seems to be the set up used in these gallop trainings as well, since the GPS graphs in both
studies look alike.
The training load might also be underestimated due to other events during training. For instance, if
the horse had consistently moved inside or outside of the ‘general path’ during the track work, this
could have resulted in an over- or underestimation of the running distance. We consider it unlikely
however, that this would have really influenced our data.
It also seems like the horses are actually galloping for a longer period of time than was intended by
the trainer. The jockey starts the workout at a specific point on the track, which is also the point
where he starts his stopwatch. On GPS graphs however, there were no specific starting points for
canter or gallop, because the trot is led into a canter and the canter into a gallop. We did not test this
statistically, but the distance galloped (i.e. velocity above 40 km/h) by the horses on the GPS graph
seemed to be longer than the distance put in the training schedule. This is probably because the
horses are already galloping before the starting point and keep galloping for another while after
reaching the finishing point.
34
Because the GPS graphs did not have a clear mean velocity, a standard way to analyse the graphs
was needed. Therefore, we divided the graphs in two phases, with an initial acceleration phase and a
constant phase. The distinction between these phases was hard however. It was not possible to see
when the horse had stopped accelerating as part of the initial acceleration phase and when it had
started to accelerate as part of the upwards fluctuation around the mean acceleration of the
constant phase. This point should not be chosen too low, because then a part of the acceleration
phase would be included in the constant phase. But this point should also not be chosen too high,
because then part of the variation around the mean acceleration would be missed.
That is why we decided to define the end of the acceleration phase as the first point that was either
20 km/h or higher, so that if we would under or overestimate the acceleration phase it would be with
the same amount of error for all horses
When looking at the GPS graphs, it was not clear between which points the trainer counted time with
his stopwatch.
For canter, initially we decided to take the mean velocity of the whole workout: from acceleration to
the point of deceleration. That is why the Mean GPS velocity is significantly lower than the Mean
trainer velocity, because this includes the part where the jockey is leading the trot into a canter. The
Mean GPS20 velocity did not include the acceleration phase and was not significantly different to the
Mean trainer velocity, because this was probably the part the trainer timed as well.
For gallop the same thing happened. The Mean GPS velocity is significantly lower than the Mean
trainer velocity, because this also includes the part where the jockey is leading the trot into a canter
and a canter into a gallop. The Mean GPS40 velocity is not significantly different to the Mean trainer
velocity, which indicates that the trainer started counting from a certain point when the horse was
already galloping.
For the canter work we analysed two months (August and December) and found that there was a
general trend of higher velocities in December, for both trainer and GPS derived data.
There was a significant month effect detectable, probably because the horses got fitter over the
period of training. A horse effect was not significant however, which could be explained by the fact
that all horses were ridden in couples and trained in the same way. Also, training schedules were
approximately the same for all horses and they were always ridden by the same jockey on the same
track. A horse by month effect was significant however, so some horses probably showed more
improvements than others over the months.
The mean acceleration during canter was significantly lower than the mean acceleration during
gallop (P=0.001). It could be questioned though, if this comparison could be made. The acceleration
during canter is not intended, while the acceleration during gallop is part of the training (Vermeulen
and Evans 2006).
We also compared the variation in velocity of good and poor performing horses and found a
difference between and a month effect within these groups. Although the good performing horses
improved over the months, while the poor performing horses deteriorated, the poor performing
horses started off with a lower variation in August than the good performing.
It is hard to explain this last finding, especially because the horses were already three years old at the
time of training. If the horses were two years old at the time of training, it could be imaginable that
some of them needed more time to grow and get used to training. But since the horses were already
older and back in training after a spell, they must have been almost fully grown and used to the
training methods so that makes this explanation unlikely.
35
The trend of increasing variation from August to December of the poor performing horses could be
explained by subclinical injuries that made their performance suboptimal. But this would not explain
why the poor performing horses started off with a lower variation than the good performing horses.
There might be other factors associated with training that made a horse a poor performer, although
it did have the ‘talent’ to be a good one. It could be possible that some horse who did have the right
genes, could not cope with the environment. Stress could be an important factor, because it is
known that the training of racehorses involves a lot of stress and horses react to this very differently.
Also gastric ulceration is a problem that frequently affects adult horses in active training for racing.
The syndrome has a complicated and multifactorial nature, but it is likely that it is related to stress.
Bell et al (2007) studied the prevalence of gastric ulceration in racehorses in New Zealand and found
evidence of gastric ulceration in 151 of the 171 horses they studied (prevalence of 88.3 %). Gastric
ulcers adversely affected physiologic indices of performance in horses in the study of Nieto et al
(2009).
Training is designed to prepare to ensure a horse is suitably fit for competition. Ideally, the training
intensity should be of a similar magnitude as that during a typical race.
The average racing distance for 3-year-old horses in New Zealand is 1200-1600 m. Horses racing
these distances can reach an average speed of over 60 km/h, depending on race class(Anonymous
2004a).
During the gallop training, this speed is probably reached only for a short period of time. Usually only
the Max GPS velocity was above 60 km/h, which was the point the horses started slowing down
again.
It is unknown if these horses achieve the maximum fitness they would be able to achieve during
training, because they rarely train at racing speed. It could be possible that they would have better
results when they had a longer exposure time to this threshold value.
On the other hand, the majority of injuries and wastage occur during training rather than at the race
track (Boston and Nunamaker 2000; Verheyen and Wood 2004; Verheyen et al. 2005). There is good
evidence that high rates of accumulation of distance performed at racing speeds may predispose
horses to fatal musculoskeletal injury (Estberg et al. 1995). So the training of racehorses might not
mainly focus on maximizing fitness, but rather on minimizing injury.
The comparison of variation in velocity during canter and gallop turned out to be harder than we
thought. At first, we thought this way of comparing was not accurate because we had to correct for
the difference in time of the different training sessions. A normal canter training takes more time
(thus more time for variation to occur), than a gallop training. But then we realised that these
trainings sessions have a major difference in set up and that they were too different to be able to
compare them.
During canter, the horse is first trotted and then led into canter which causes the acceleration phase.
Once the rider thinks that the correct subjective gait is reached (using his feel for the horse and his
stopwatch), he tries to keep the horse at this speed. Over this whole constant phase, the horse is still
slightly accelerating and variation occurs, but the intention of the training session is to train at a
constant speed.
Another set up is used during the gallop, or ‘fast’ training sessions, as showed above by the typical
gallop training session from Vermeulen and Evans (2006). During gallop, the horse is maximally
accelerating and a constant phase could not be identified. The whole workout consists of one long
acceleration, during which almost no variation occurs. The horse is constantly accelerating during
these trainings, probably up to its maximal speed.
Because of these major differences, we decided to withdraw this hypothesis from the project.
36
7. Conclusion
Horses were trained in a different way than we hypothesised during canter work outs. The intention
of the trainer was to run the horse at a constant velocity, while in fact the horse keeps accelerating
when the desired speed is reached. The mean velocity timed by the trainer with his stopwatch is
significantly different to the mean velocity measured by GPS.
A gallop work out consists of one long acceleration, but this is the intention of the training. There is
no significant difference between the mean velocity timed by the trainer and the mean GPS velocity
of the true gallop work. Race speeds are only reached for a couple of seconds during a gallop work
out.
The mean acceleration during canter was significantly lower than the mean acceleration during
gallop.
Good performing horses have less variation in velocity than poor performing horses and show a
decreasing amount of variation over the period of training.
37
8. References
Anonymous (2004a) New Zealand’s racing facts (2003-2004), New Zealand Thoroughbred Racing Inc,
Wellington.
Anonymous (2004b) Size and scope of the New Zealand racing industry: Economic impacts and
community social benefit. Wellington, N.Z.
Anonymous (2009a) New Zealand Thoroughbred Racing. Annual Report 2008-2009.
Anonymous (2009b) New Zealand Thoroughbred Racing Fact Book 2009. Wellington, N.Z.: N.Z.T.R.
Inc.
Bell, R. (2007). The prevalence of gastric ulceration in racehorses in New Zealand. New Zealand
Veterinary Journal, 55, 13-18.
Bolwell, C.F., Russel, L.J. and Rogers, C.W. (2010) A cross-sectional survey of training practices of 2year-old racehorses in the North Island of New Zealand. Comparative Exercise physiology 7, 37-42.
Boston, R.C. and Nunamaker, D.M. (2000). Gait and speed as exercise components of risk factors
associated with onset of fatigue injury of the third metacarpal bone in 2-year-old Thoroughbred
racehorses. American Journal Of Veterinary Research, 61, 602-608.
Estberg, L., Gardner, I.A., Stover, S.M., Johnson, B.J., Case, J.T. and Ardans, A. (1995) Cumulative
racing-speed exercise distance cluster as a risk factor for fatal musculoskeletal injury in
Thoroughbred racehorses in California. Prev. Vet. Med. 24, 253-263.
Fennesy, P.F.(2010) An overview of the New Zealand Thoroughbred Industry. Proceedings of the New
Zealand Society of Animal Production 2010, Vol 70: 137-139.
Gramkow, H.L.(2006) Field studies of heart rate and velocity in Thoroughbred racehorses. Masters
Thesis, University of Sydney, Sydney.
Hinchcliff, K.W., Geor, R.J. and Kaneps, A.J.(2007) Equine Exercise Physiology: The science of exercise
in the athletic horse. Blackwell Science Ltd, Oxford, UK.
Kingston, J.K., Soppet, G.M., Rogers, C.W. and Firth, E.C. (2006) Use of a global positioning and heart
rate monitoring system to asses training load in a group of Thoroughbred racehorses. Equine vet. J.
Suppl. 36, 106-109.
Kobluk, C.N., Robinson, R.A., Trent, A.M., Ames, T.R. and Gordon, B.J. (1990) Comparison of the
exercise level and problem rate of 95 Thoroughbred horses: a cohort study. Proc. 36th Annu. Conv.
Am. Assoc. Equine Pract., Lexington, Kentucky, USA, pp. 471-475.
Linder, A. and Dingerkus, A. (1993) Incidence of training failure among Thoroughbred horses at
Cologne, Germany. Prev. Vet. Med. 16, 85-94.
Marlin, D. and Nankervis, K. (2002) Equine Exercise Physiology, Iowa State Press, Ames, Iowa, USA.
38
Mohammed, H.O., Hill, T. and Lowe, J. (1991) Risk factors associated with injuries in Thoroughbred
horses Equine vet. J. 23, 445-448.
Nunamaker, D.M., Butterweck, D.M. and Provost M.T. (1990) Fatigue Fractures in Thoroughbred
Racehorses - Relationships With Age, Peak Bone Strain, and Training. Journal of Orthopaedic
Research 8, 604-11.
Nieto, J. E., Snyder, J.R., Vatistas, N.J. and Jones, J.H. (2009) Effect of gastric ulceration on physiologic
responses to exercise in horses. American J. of Vet. Research 70, 787-795.
Parkin, T. (2008). Epidemiology of racetrack injuries in racehorses. Veterinary Clinics Of North
America-Equine Practice, 24, 1-19.
Perkins, N.R., Reid, S.W.J. and Morris, R.S. (2004a) Profiling the New Zealand Thoroughbred racing
industry. 1. Training, racing and general health patterns. N.Z. vet. J. 53, 59-68.
Perkins, N.R., Reid, S.W.J. and Morris, R.S. (2004b) Profiling the New Zealand Thoroughbred racing
industry. 2. Conditions interfering with training and racing. N.Z. vet. J. 53, 69-76.
Pfau, T., Witte, T.H. and Wilson, A.M. (2006) Centre of mass movement and mechanical energy
fluctuation during gallop locomotion in the Thoroughbred racehorse. Journal of Experimental Biology
209, 3742-3757.
Rogers, C.W. and Firth, E.C. (2004) Musculoskeletal responses of 2-year-old Thoroughbred horses to
early training. 2. Measurement error and effect of training stage on the relationship between
objective and subjective criteria of training workload. N. Z. vet. J. 52, 272-279.
Rogers, C.W., Rivero, J.L.L., van Breda, E., Lindner, A. and Sloet van Oldruitenborgh-Oosterbaan,
M.M.(2007) Describing workload and scientific information on conditioning horses. Equine and
Comparative Exercise Physiology 4, 1-6.
Rogers, C.W., Firth, E.C., McIlwraith, C.W., Barneveld, A., Goodship, A.E., Kawcak, C.E., Smith, R.K.W.
and van Weeren, P.R. (2008) Evaluation of a new strategy to modulate skeletal development in
racehorses by imposing tack-based exercise during growth: The effects on 2- and 3-year-old racing
careers. Equine vet. J. 40, 119-127.
Rogers, C.W., Kidd, L. and Firth, E.C. (2010) Linear and temporal changes in the trot of 2-year-old
Thoroughbred racehorses in relation to early exercise and race training. Comparative Exercise
Physiology.
Spence, A.J., Tan, H. and Wilson, A. (2008) Accuracy of the TurfTrax Racing Data System for
determination of equine speed and position. Equine vet. J. 40, 680-683.
Verheyen, K.L.P. and Wood, J.L.N. (2004) Descriptive epidemiology of fractures occurring in British
Thoroughbred horses in training. Equine vet. J. 36, 167-173.
Verheyen, K.L.P., Henley, W.E., Price, J.S. and Wood, J.L.N. (2005) Training related factors associated
with dorsometacarpal disease in young Thoroughbred racehorses in the UK. Equine vet. J. 37, 442448.
39
Verheyen, K.L.P., Price, J.S., Lanyon, L. and Wood, J.L.N. (2006) Exercise distance and speed affect the
risk of fracture in racehorses. Bone 39, 1322-1330.
Vermeulen, A.D. and Evans, D.L. (2006) Measurements of fitness in Thoroughbred racehorses using
field studies of heart rate and velocity with a global positioning system. Equine vet. J. Suppl. 36, 113117.
Witte, T.H. and Wilson, A.M. (2004) Accuracy of non-differential GPS for the determination of speed
over ground. J. Biomech. 37, 1891-1898.
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