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Free-running has recently spread over the digital media and has since become an official sport. Despite this increase in popularity there is no peer-reviewed literature on the physiology and anthropometry of the athletes or the physical demands of the sport. This study aimed to define free-running and explore some of the physiological characteristics of the athletes and the sport, by undertaking the assessment of anthropometric data and vertical jump performance in 7 free-runners; in addition, GPS information was gathered from 3 training sessions for one free-runner. The free-runners in the present study displayed high power output as result of high vertical jump performance. They also displayed low levels of body fat and low body mass. The GPS data illustrated that free-running is an intermittent sport relying heavily on the phosphagenic energy system; therefore, free-runners should focus their training utilising this energy system. Another outcome from the research is the suggestion that future studies should trial different equipment to monitor the physiological demands and intensity of the sport due to particularly high-intensity short bursts of energy utilised.
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Warren et al.: A free-running case study Serb J Sports Sci 7(1): 25-30
Serbian Journal of Sports Sciences
ISSN 1820-6301
Original article
Original articleOriginal article
Original article
2013, 7(1): 25-30
ID 198165772
Received: 07 Jan 2013
UDC 796.4 Accepted: 16 Mar 2013
James Warren
, Jonathan Sinclair
& Lindsay Bottoms
School of Health, Sport and Bioscience, University of East London, UK.
Division of Sport Exercise and Nutritional Sciences, University of Central Lancashire, UK.
Abstract Abstract
Free-running has recently spread over the digital media and has since become an official sport.
Despite this increase in popularity there is no peer-reviewed literature on the physiology and anthropometry of
the athletes or the physical demands of the sport. This study aimed to define free-running and explore some of
the physiological characteristics of the athletes and the sport, by undertaking the assessment of anthropometric
data and vertical jump performance in 7 free-runners; in addition, GPS information was gathered from 3 training
sessions for one free-runner. The free-runners in the present study displayed high power output as result of
high vertical jump performance. They also displayed low levels of body fat and low body mass. The GPS data
illustrated that free-running is an intermittent sport relying heavily on the phosphagenic energy system;
therefore, free-runners should focus their training utilising this energy system. Another outcome from the
research is the suggestion that future studies should trial different equipment to monitor the physiological
demands and intensity of the sport due to particularly high-intensity short bursts of energy utilised.
Key words:
Key words:Key words:
Key words:
Free-running, profiling, training, case study
Free-running is a recently developed sport and a global phenomenon that began official competition in
2008, when the first annual games were held in London, England [1]. Prior to this, free-running was an
unknown discipline which originated in France as L’Art du Déplacement in the 1980’s. L’Art du Déplacement
derived from the French Military training Parcours du Combattant, a movement-based obstacle course
pioneered by Georges Herbert and inherited by Raymond Belle and his son David Belle. This discipline then
became Parkour [6, 7, 19]. Parkour was extended child’s play to David Belle, Sebastien Foucan and other
associates. They formed the Yamakasi training group in the Parisian suburbs drawing influences from Bruce
Lee and Jackie Chan, and developed parkour as a spiritual and physical mastery of one’s own movement
seeking to challenge one’s self [6, 7]. 2001 yielded the first media exposure of parkour in Yamakasi: Les
Samouraïs des Temps Modernes initiating a divide in the discipline. Further media exposure provoked the
appearance of the term Free-running among English speaking audiences, especially after the documentary
Jump London in 2003 [5, 6, 7].
Profiling the performance of athletes has been conducted in a variety of well established competitive
sports such as artistic gymnastics [13, 21, 25], rugby league [8, 9] and sport rock climbing [17, 23, 26].
However, free-running is a fledgling sport with no official definition or classification. Free-running has only
recently emerged from France through media exposure and has accelerated from a hobby based on military
training to an official competitive sport. The concept of free-running is fluid, as are the movements and
dynamics and therefore it is subject to much debate [6, 7]. Although as of 2008 competitions take place [1],
many athletes participate in free-running at a non-competitive, amateur level. As it is a recently developed
sport, there is limited peer-reviewed literature defining the biomechanical or physiological demands at either
a professional competitive or amateur level. Subsequently, there is also no peer-reviewed literature defining
physiological or anthropometric characteristics of free-running athletes.
While profiling sports and athletes is advantageous to those involved, the benefits have not been
reaped in all sports, especially in those less recognised. It is obvious that further investigation into newly
evolving sports is required; clearly outlining the demands of an emerging sport and comparing the performance of
the athletes to those of similar sports will help identify appropriate testing procedures. This will then enable
quantification of the athletes’ performance in standardised quantitative measurements such as body fat
Warren et al.: A free-running case study Serb J Sports Sci 7(1): 25-30
percentage, or standardise power output or power to weight ratio. Studies into free-running will identify the sport
within the research community, exposing it to further, more detailed research and advancing its practice.
Therefore, the aim of this case study was to a) clarify what free-running is and which characteristics
the sport requires of the athletes who engage in its practice, and b) explore the metabolic demands the
sport places on the athletes. This case study will be of huge value by bringing free-running and free-
runners into the scientific research community as a sport and a population. It will provide general
information on strength and power, anthropometry of the athletes, as well as intensity levels and energy
demands within the sport. It will also form a platform on which to base future research studies, further
profiling and cross-sectional studies of athletes and the sport, as well as encourage performance
enhancement and injury prevention by comparisons to the profiles of other sports.
Seven male amateur free-runners volunteered to take part in the study (Mean ± SD: age 21.70 ± 2.60 yrs,
body mass 67.80 ± 3.57 kg, height 174.4 ± 6.36 cm). All were free from musculoskeletal pathology at the
commencement of data collection and provided written informed consent in accordance with the declaration
of Helsinki. The procedures were approved by the University of East London, School of Health, Sport and
Bioscience ethics committee.
Six participants underwent testing on two occasions and one participant attended 6 sessions. All participants
undertook anthropometric measurements and vertical jump performance. However, only one participant attended
further strength testing and undertook training sessions with Geographical Positioning Software (GPS).
On the first visit, participants’ body mass was determined by electronic scales (Ironman BC-558,
Tanita, Tokyo, Japan) and height was measured using a stadiometer. Age and a brief training history were
recorded. Skinfolds were measured across seven sites (triceps, biceps, subscapular, supraspinale,
abdominal, thigh and calf) on the right side of the body using skinfold callipers (Harpenden, British
Indicators, Luton, UK) in accordance with the ISAK standard measurement procedures [18]. Measurements
were taken to the nearest 0.2 mm, four seconds after applying the callipers. The mean of three
measurements within 10 % of each other were taken by the same assessor to improve accuracy [14]. The
1978 Jackson and Pollock equation was used to establish body fat percentage of the participants [12].
After demographics and anthropometric measures were taken, participants were familiarised with the
jump mat and the protocol of how to jump, and how the vertical jump height was going to be measured. This
was to improve the effectiveness of later testing procedure and improve and negate learning effects [11, 20]
and was therefore a familiarisation session.
The second testing session was conducted at least 48 hours after the initial session to increase the
effectiveness of familiarisation. Peak vertical jump height was measured with an electronic cord
displacement jump mat (Takei Scientific Instruments Co. Ltd.), measuring jump height by displacement to
the nearest ± 0.5 cm. Participants were allowed to warm up in their own time as part of their standard
training session. Each participant was allowed five attempts to obtain maximal vertical jump height. Two
attempts were allowed as a warm up. Participants were instructed to take off with two feet, hip to shoulder
width apart, and land back onto the mat with two feet. Countermovement depth was unstandardised and
use of arms was allowed. Participants were asked to drop to a comfortable depth before explosively
accelerating vertically with the free use of arm movement. This was to mimic the plyometric actions of the
sport, and allow for differing limb length of the subjects. A passive rest of 30 seconds was given between
jump attempts. Peak power output was calculated using the Sayers et al [22] equation cited in Carlock et al
[2]. From this, power to weight ratio was calculated by dividing by body mass.
This was undertaken by one participant only. The first two of these sessions was protocol familiarisation,
and testing of one repetition maximum back squat (Technogym, Gymcompany Ltd, Somerset, UK) and
bench press (Technogym, Gymcompany Ltd, Somerset, UK). This was to measure maximal strength of the
participant in kilograms of weight lifted. The protocol from Harman and Garhammer [10] was used and all
attempts were performed to strict posture and technical failure to avoid injury.
The final three occasions measured heart rates, speeds, and distances achieved during the
participant’s training sessions. Data were averaged over three training sessions and measured via a Garmin
Forerunner 305 GPS and heart rate monitor (Garmin, UK). The participant was free to conduct any warm up
and training desired. GPS and heart rate data gave an estimate of the intensity of the training sessions. The
sessions lasted 26.21 minutes and 37.46 minutes and were conducted at just one training ‘spot’ in the
Warren et al.: A free-running case study Serb J Sports Sci 7(1): 25-30
centre of Norwich, Norfolk, UK; however, it was noted that training was not limited to that one location.
Training sessions involved jumps, vaults, and balances, in three dimensions. This included forward-
backward, lateral and vertical movement.
Data were analysed using SPSS statistics V 20.0. Means, standard deviations and coefficient of variations
were calculated for height, body mass, body fat percentage, vertical jump heights, vertical jump power
output, age and training age. Normal distribution of performance variables was tested using a Kolomogorov-
Smirnov test. Descriptive statistics were calculated for the training sessions. Reflections whether the
athletes’ physiological profiles met the observed demands of free-running were also made.
A total of seven participants completed this study. The free-runners’ vertical jump performances and
anthropometric tests are shown in Table 1.
Table 1. Participant demographics and performance
Variable Free-runners (n=7) [mean ± SD]
Age (years) 21.71 ± 2.60
Training age (years) 4.46 ± 1.66
Height (cm) 174.40 ± 6.36
Body mass (kg) 67.80 ± 3.57
Body fat (%) 7.26 ± 1.32
Power (W) 5,100.58 ± 141.29
Power per kg (W·kg
) 75.42 ± 2.59
The randomly selected participant (body mass 68.2 kg, height 168.2 cm, age 19 years) generated a
maximal one repetition back squat of 120 kg and a maximal one repetition bench press of 90 kg; the
kilograms lifted represented the maximal force that could be produced in that range of motion. No other
subject participated in any maximal lifting protocols.
Table 2. Mean GPS and heart rate data
Performance Variable Mean Value
Distance covered (km) 0.54 ± 0.10
Time (min) 33.05 ± 5.58.8
Movement time (min) 10.45 ± 1.43.51
Percentage time moving (%) 32.61 ± 1.94
Average speed (km·h
) 1.00 ± 0.25
Max speed (km·h
) 11.76 ± 2.66
Elevation change (m) 2.33 ± 1.53
Average heart rate (bpm) 144.33 ± 5.77
Max heart rate (bpm) 175.00 ± 5.00
Heart rate data were established over three individual training sessions (Table 2). The sessions lasted
between 26.21 minutes and 37.46 minutes, covering 0.44 km to 0.64 km. It was recorded that less than a
third of this time was spent moving while for the rest of the time minimal to no movement was detected.
Consequently, the average speed was only 1 km·h
. Maximum speed attained was measured at 9 km·h
14.3 km·h
. This converts to average and maximal speeds of 0.29 s
and 3.27 s
respectively. The
intensity values monitored by the heart rate monitor gave average heart rate values of 141 to 151 beats·min
and maximal values of 170 to 180 beats·min
Warren et al.: A free-running case study Serb J Sports Sci 7(1): 25-30
This study aimed to clarify what free-running is and what it demands as a sport, by quantitatively profiling
the athlete’s physiology and anthropometry. This investigation intended to evaluate and understand the
metabolic demands of the sport by using heart rate and GPS measurements.
The results of the study illustrated that the free-runner sample tested had high power output as the
result of high vertical jump performance. They also displayed low levels of body fat in comparison to other
sports (Table 1 and 3), and low body mass. When comparing the data to the study by Jemni et al [13] of
national and international level gymnasts, involved in a sport with similar movement patterns, free-runners
displayed a lower body fat percentage by a mean of -2.46%. Both the absolute and relative power output of
free-runners in this study were higher than the power outputs observed in the male gymnasts in Marina et al
[15] by a mean of 1525.58 W and 10.42 W·kg
respectively. However, Marina et al [15] used a ‘drop jump’
technique finding peak power output from a drop of 40 cm, as opposed to the countermovement jumps used
in this study.
Table 3. Comparison of Mean ± SD anthropometric and power output data of the present free-runners with
gymnasts and climbers
Variable Climbers (n=11)
MacLeod et al [16] Gymnasts (n=41)
Marina et al [15] Gymnasts
Jemni et al [13]
Age (years) 23.2 ±3.2 18 ±4.3 21.8 ±2.4
Training age (years) 5.3 ± 1.9
Height (cm) 175.5 ±6.7 161.0 ±11.9 168.2 ±6.0
Body mass (kg) 66.4 ± 6.8 55.0 ±12.5 67.5 ±8.0
Body fat (%) 11.3 ±3.6 10.3 ±1.5
Power (W) 3575*
Power per kg (W·kg
) 65.0*
*values are estimated from the figure in Marina et al
The ability to produce maximal force or ‘strength’ tests on an individual male free-runner elicited maximal
lower body strength of 120 kg during a back squat exercise and maximal upper body strength of 90 kg
during a bench press exercise when working to technical failure. Wilstøff et al [27] found maximal squat
significantly negatively correlated with 10 m sprint, 30 m sprint, and positively correlated to vertical jump in
elite soccer players. This highlights the importance of maximal level strength in the process of power
production over short distances in minimal amounts of time in movements very similar to that of free-
running. Despite this, Cronin and Hansen [4] found no significant relationship between three repetition
maximum squatting and sprint speeds or jumping power performance among part- and full-time professional
rugby league players. However, they highlighted that a large degree of variability in their sample could have
distorted the accuracy of their results. Three repetition maximum testing is also not a measure of true
maximal strength due to the repetition of the movement. Changing from a multiple effort test to a single
effort test may have elicited a relationship between ‘true’ maximal strength and sprinting or jump power
Although maximal strength is not the only performance determining factor, it could be
proposed that a an athlete with higher maximal strength could accelerate and decelerate a set mass,
for example body mass, with greater ease.
Despite large confidence intervals from a small sample, Stone
et al [24] confirmed this by demonstrating maximally stronger subjects had greater power outputs across a
range of loads. Thus, the stronger free-runners were, the greater was their ability to explosively jump or land
at a set distance, which increased the efficiency and safety of the athlete.
Despite the lack of available comparisons, GPS was trialled among free-runners in this study due to
the outdoor nature of the sport with the aim of establishing suitability for future testing. GPS has been used
successfully to measure intensities and workloads in sports like beach soccer [3]. Although an average free-
running session had reasonable length (33.05 ± 5.58.8 min), only 10.05 ± 5.58.8 min (less than a third) was
spent moving. This reflects the intermittent high intensity burst of the sport, where large portions of time are
spent resting between explosive bursts of activity thus having a causal affect, significantly lowering average
speeds to a slow walking pace [3]. While remaining intermittent, the accuracy upon workload may be very
poor due to the vertical component of movement where changes in latitude and longitude are negligible.
Thus a 1:2 work to rest ratio may be inaccurate. Metabolically demanding sequences of vertical activity may
be done with little to no horizontal momentum. Although this intensity may reflect in the heart rate, GPS
does not effectively account for vertical momentum. Average maximal speed was only 11.76 ± 2.66 km·h
Warren et al.: A free-running case study Serb J Sports Sci 7(1): 25-30
or 3.27 m·s
. This is not particularly fast and was classified as ‘quick running’ by Castellano and
Casamichana [3], falling between 7.0 km·h
and 12.9 km·h
Average heart rate (144.33 ± 5.77 beats·min
) and maximal heart rates (175 ± 5 beats·min
) were
obtained. Castellano and Casamichana [3] profiled beach soccer using heart rate intensities whereby nearly
60 % of match play was spent at greater than 90 % maximal heart rate. This is not surprising as more time
in beach soccer is spent moving and performance relies more on an aerobic component, although this could
be skewed by the short play time compared to the time the free-runner spent training. However, blood
lactate and heart rate have been used by authors to measure physiological capabilities of gymnasts [13].
Although this was during non-gymnastic lab tests, Jemni et al [13] found national and international gymnasts
to have blood lactate levels of 16.89 mmol·L
and 11.42 mmol·L
and heart rates of 169.33 and 169.77
at treadmill VO
exhaustion, although blood lactate was lower during the Wingate test. While
this gives an impression of physiological capabilities, more research is needed within the free-runner population
to verify both their capabilities and the physiological characteristics elicited by training demands. Together the
GPS and heart rate data reflect the intermittency of explosive bouts with long periods of rest within free-running. It
is apparent that free-running relies heavily on the phosphogen energy systems for energy production, with a
small demand of aerobic component while the athlete recovers for the next bout of activity.
The findings of this study are limited to a very small population of free-runners, or even a case study
of one free-runner’s abilities and characteristics of training. This gives a very low external validity and ability
to transfer the findings to the free-running population. The population also varied greatly in their physiology
and anthropometry. A more detailed study needs to be done on a much larger group of free-runners to gain
accurate knowledge of both the athlete characteristics and the demands of the sport. A refinement of the
testing procedures is needed and the knowledge of the test results obtained in this study and their
applicability to the biomechanics and physiology of the sport is essential for providing an accurate profile of
free-runners’ physiology most relevant to the sport.
In conclusion, free-runners in the present study displayed high power output as result of high vertical jump
performance. Free-runners also displayed low levels of body fat, and a low body mass. The GPS data
illustrated that free-running is an intermittent sport relying heavily on the phosphagenic energy system, and
therefore free-runners should focus their training on the utilisation of this energy system. The results also
suggest that, due to the physical nature of free-running, future studies should trial different equipment to
monitor the physiological demands and intensity of the sport.
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Address for correspondence:
Dr Lindsay Bottoms
School of Health, Sport and Bioscience,
University of East London,
Water Lane,
E15 4LZ, UK.
Tel: 020 8223 3371
... Our results were lower on sit and reach (10.58 ± 10.08 cm) than Leite et al., Subjects 14 (23.54 ± 8.32 cm). Warren et al., 27 described results for fat mass of 7.26 ± 1.32% which were very similar to the values of the athletes from this study (7.50 ± 0.52%). Otherwise, in contrast to their study, we got results of 5100.58 ± 141.29 W for vertical jump power while they ...
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Introduction: The aims of this study were to determinate the anthropometric profile and physical fitness of Parkour practitioners and to establish differences by performance level. Method: Thirteen Parkour practitioners participated on this study. Agility, hamstring extensibility, horizontal jump distance, vertical jump height, vertical jump power, estimation of maximal oxygen consumption, body composition and somatotype were assessed with a battery of six tests. Also, a specific test which simulated a competition situation was performed in order to establish two groups (A: high performance; B: low performance) by the obtained score. Results: Groups A and B obtained respectively 1.7-5.3-2.5 and 2.2-4.2-2.8 on somatotype; 7.50 ± 0.52 and 8.67 ± 2.1396 on fat mass; 47.44 ± 2.03 and 45.91 ±2.68% on skeletal muscle mass; 12.47 ± 0.70 and 12.53 ± 1.21% on bone mass; 72.80 ± 11.01 and 55.19 ± 6.06 ml-Kg'-min-' on estimated oxygen consumption; 14.36 ± 0.47 and 15.29 ± 0.44 s on Illinois test (agility); 13.77 ± 5.20 and 7.86 ± 12.70 cm on sit and reach test; 50.09 ± 3.47 and 37.19 ± 4.82 cm on vertical jump height; 2820.84 ± 453.72 and 2105.84 + 237.24 Won vertical jump power and 2.97 ± 0.71 and 2.60 ± 0.22 m on horizontal jump distance. Group A obtained significant lower values on ectomorphy and higher on mesomorphy, estimated oxygen consumption, agility, horizontal jump distance and vertical jump height and power. Conclusions: After determining anthropometrical profile and physical fitness, we observe that vertical jump seems to be the most important parameter on Parkour performance, also other variables like estimated maximal oxygen consumption, agility, vertical jump power, horizontal jump distance, mesomorphy and ectomorphy appear as possibly determinant factors on Parkour performance.
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Although beach soccer has become increasingly popular in recent years very little scientific research has been conducted into the sport. A pilot study was carried out with the aim of examining the physiological (heart rate) and physical (motion analysis) responses of beach soccer players during competitive matches. Ten players (age 25.5 ± 0.5 years; height 1.80 ± 0.08 m; weight 78.2 ± 5.6 kg.) were studied over five beach soccer matches. The physiological demands were analysed by measuring heart rate (HR) using telemetric devices, while the physical profile was evaluated by recording motion and speed by means of GPS devices. During competitive matches, players obtained a HRmean of 165.2 bpm (86.5% HRmax), with 59.3% of the time participating (TP) corresponding to values above 90% of the HRmax. The distance covered per minute of participation was 97.7 m, with 9.5% of this distance corresponding to high-intensity running and 2.5% to sprint; the work:rest ratio was 1.4:1 and the maximum speed 21.7 km·h(-1). These results showed that beach soccer is an intermittent physical activity of greater intensity than other team games. It requires a major contribution from the anaerobic system as emphasis is placed on players making quick bursts of high-intensity activity separated by brief rest periods. Key pointsThe distance covered per minute of play is around 100 m.Beach soccer is an intermittent sport with a work:rest ratio of 1.4:1.The playing surface in beach soccer is an important handicap to obtain maximum speeds.Beach soccer has a high physiological intensity, with more than half of the game is spent at intensities above 90 % of the HRmax.
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Background: Despite the increasing use of very low carbohydrate ketogenic diets (VLCKD) in weight control and management of the metabolic syndrome there is a paucity of research about effects of VLCKD on sport performance. Ketogenic diets may be useful in sports that include weight class divisions and the aim of our study was to investigate the influence of VLCKD on explosive strength performance. Methods: 8 athletes, elite artistic gymnasts (age 20.9 ± 5.5 yrs) were recruited. We analyzed body composition and various performance aspects (hanging straight leg raise, ground push up, parallel bar dips, pull up, squat jump, countermovement jump, 30 sec continuous jumps) before and after 30 days of a modified ketogenic diet. The diet was based on green vegetables, olive oil, fish and meat plus dishes composed of high quality protein and virtually zero carbohydrates, but which mimicked their taste, with the addition of some herbal extracts. During the VLCKD the athletes performed the normal training program. After three months the same protocol, tests were performed before and after 30 days of the athletes' usual diet (a typically western diet, WD). A one-way Anova for repeated measurements was used. Results: No significant differences were detected between VLCKD and WD in all strength tests. Significant differences were found in body weight and body composition: after VLCKD there was a decrease in body weight (from 69.6 ± 7.3 Kg to 68.0 ± 7.5 Kg) and fat mass (from 5.3 ± 1.3 Kg to 3.4 ± 0.8 Kg p < 0.001) with a non-significant increase in muscle mass. Conclusions: Despite concerns of coaches and doctors about the possible detrimental effects of low carbohydrate diets on athletic performance and the well known importance of carbohydrates there are no data about VLCKD and strength performance. The undeniable and sudden effect of VLCKD on fat loss may be useful for those athletes who compete in sports based on weight class. We have demonstrated that using VLCKD for a relatively short time period (i.e. 30 days) can decrease body weight and body fat without negative effects on strength performance in high level athletes.
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The aim of this study was to investigate the relation between anthropometric variables and total race time including split times in 184 recreational male Ironman triathletes. Body mass, body height, body mass index, lengths and circumferences of imbs, thicknesses of skin-folds, sum of skin-fold thicknesses, and percent body fat were related to total race time including split times using correlation analysis and effect size. A large effect size (r>0.37) was found for the association between body mass index and time in the run split and between both the sum of skin-folds and percent body fat with total race time. A medium effect size (r=0.24-0.36) was observed in the association between body mass and both the split time in running and total race time, between body mass index and total race time, between both the circumferences of upper arm and thigh with split time in the run and between both the sum of skin-folds and percent body fat with split times in swimming, cycling and running. The results of this study showed that lower body mass, lower body mass index and lower body fat were associated with both a faster Ironman race and a faster run split; lower circumferences of upper arm and thigh were also related with a faster run split.
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The objective of this study was to investigate and compare the factors influencing plyometric jumping performance between well-trained gymnasts and a control group. Seventy-six gymnasts and 91 moderately active subjects volunteered to participate in this study. Drop jumps (DJ) were performed from 20-, 40-, 60-, 80-, and 100-cm heights. Flight time (FT) and contact time (CT) were recorded using contact mat. Flight time to contact time (FC) ratio and Bosco expression (BE) were calculated. Male gymnasts scored similar FT to their controls, whereas female gymnasts had significantly longer FT compared with their peers. The gymnasts obtained significantly shorter CT than their control groups, whereas their FC ratios were significantly higher and increased when the height of the drops was close to 60 cm. Moreover, gymnasts' BE was greater in comparison to their respective control groups independent of the drop height. The height of the drop that produced the best FC ratio and BE varied between the groups. The best performances were obtained between 40- and 60-cm drop height for both groups. Female control group showed a trend toward a continuing decline with the increase of the drop height. The best gymnasts (finalists at World Championships) obtained their best performance at 80-cm drop. Flight time is the less discriminating factor distinguishing gymnasts' DJ performances. Considering CT, FC, and BE results all together could better profile the gymnasts rather than taken separately. Bosco expression was shown to be more sensitive to the increase in FT; we suggest BE as the best criteria to assess the appropriate drop height for plyometric training purposes in gymnasts as it has been significantly correlated to FT.
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1. Skinfold thickness, body circumferences and body density were measured in samples of 308 and ninety-five adult men ranging in age from 18 to 61 years. 2. Using the sample of 308 men, multiple regression equations were calculated to estimate body density using either the quadratic or log form of the sum of skinfolds, in combination with age, waist and forearm circumference. 3. The multiple correlations for the equations exceeded 0.90 with standard errors of approximately ±0.0073 g/ml. 4. The regression equations were cross validated on the second sample of ninety-five men. The correlations between predicted and laboratory-determined body density exceeded 0.90 with standard errors of approximately 0.0077 g/ml. 5. The regression equations were shown to be valid for adult men varying in age and fatness.
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To investigate the physiological and anthropometric characteristics of amateur rugby league players. Thirty five amateur rugby league players (19 forwards and 16 backs) were measured for height, body mass, percentage body fat (sum of four skinfolds), muscular power (vertical jump), speed (10 m and 40 m sprint), and maximal aerobic power (multistage fitness test). Data were also collected on match frequency, training status, playing experience, and employment related physical activity levels. The 10 m and 40 m sprint, vertical jump, percentage body fat, and multistage fitness test results were 20-42% poorer than previously reported for professional rugby league players. Compared with forwards, backs had significantly (p<0.01) lower body mass (79.7 (74.7-84.7) kg v 90.8 (86.2-95.4) kg) and significantly (p<0.01) greater speed during the 40 m sprint (6.45 (6.35-6.55) v 6.79 (6.69-6.89) seconds). Values for percentage body fat, vertical jump, 10 m sprint, and maximal aerobic power were not significantly different (p>0.05) between forwards and backs. When compared with professional rugby league players, the training status of amateur rugby league players was 30-53% lower, with players devoting less than three hours a week to team training sessions and about 30 minutes a week to individual training sessions. The training time devoted to the development of muscular power (about 13 minutes a week), speed (about eight minutes a week), and aerobic fitness (about 34 minutes a week) did not differ significantly (p>0.05) between forwards and backs. At the time of the field testing, players had participated, on average, in one 60 minute match every eight days. The physiological and anthropometric characteristics of amateur rugby league players are poorly developed. These findings suggest that position specific training does not occur in amateur rugby league. The poor fitness of non-elite players may be due to a low playing intensity, infrequent matches of short duration, and/or an inappropriate training stimulus.
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To identify the physiological and anthropometric determinants of sport climbing performance. Forty four climbers (24 men, 20 women) of various skill levels (self reported rating 5.6-5.13c on the Yosemite decimal scale) and years of experience (0.10-44 years) served as subjects. They climbed two routes on separate days to assess climbing performance. The routes (11 and 30 m in distance) were set on two artificial climbing walls and were designed to become progressively more difficult from start to finish. Performance was scored according to the system used in sport climbing competitions where each successive handhold increases by one in point value. Results from each route were combined for a total climbing performance score. Measured variables for each subject included anthropometric (height, weight, leg length, arm span, % body fat), demographic (self reported climbing rating, years of climbing experience, weekly hours of training), and physiological (knee and shoulder extension, knee flexion, grip, and finger pincer strength, bent arm hang, grip endurance, hip and shoulder flexibility, and upper and lower body anaerobic power). These variables were combined into components using a principal components analysis procedure. These components were then used in a simultaneous multiple regression procedure to determine which components best explain the variance in sport rock climbing performance. The principal components analysis procedure extracted three components. These were labelled training, anthropometric, and flexibility on the basis of the measured variables that were the most influential in forming each component. The results of the multiple regression procedure indicated that the training component uniquely explained 58.9% of the total variance in climbing performance. The anthropometric and flexibility components explained 0.3% and 1.8% of the total variance in climbing performance respectively. The variance in climbing performance can be explained by a component consisting of trainable variables. More importantly, the findings do not support the belief that a climber must necessarily possess specific anthropometric characteristics to excel in sport rock climbing.
The purpose of this investigation was to assess the intrasession and intersession reliability of the Vertec, Just Jump System, and Myotest for measuring countermovement vertical jump (CMJ) height. Forty male and 39 female university students completed 3 maximal-effort CMJs during 2 testing sessions, which were separated by 24-48 hours. The height of the CMJ was measured from all 3 devices simultaneously. Systematic error, relative reliability, absolute reliability, and heteroscedasticity were assessed for each device. Systematic error across the 3 CMJ trials was observed within both sessions for males and females, and this was most frequently observed when the CMJ height was measured by the Vertec. No systematic error was discovered across the 2 testing sessions when the maximum CMJ heights from the 2 sessions were compared. In males, the Myotest demonstrated the best intrasession reliability (intraclass correlation coefficient [ICC] = 0.95; SEM = 1.5 cm; coefficient of variation [CV] = 3.3%) and intersession reliability (ICC = 0.88; SEM = 2.4 cm; CV = 5.3%; limits of agreement = -0.08 ± 4.06 cm). Similarly, in females, the Myotest demonstrated the best intrasession reliability (ICC = 0.91; SEM = 1.4 cm; CV = 4.5%) and intersession reliability (ICC = 0.92; SEM = 1.3 cm; CV = 4.1%; limits of agreement = 0.33 ± 3.53 cm). Additional analysis revealed that heteroscedasticity was present in the CMJ when measured from all 3 devices, indicating that better jumpers demonstrate greater fluctuations in CMJ scores across testing sessions. To attain reliable CMJ height measurements, practitioners are encouraged to familiarize athletes with the CMJ technique and then allow the athletes to complete numerous repetitions until performance plateaus, particularly if the Vertec is being used.
The vertical jump-and-reach score is used as a component in the estimation of peak mechanical power in two equations put forth by Lewis and Harman et al. The purpose of the present study was to: 1) cross-validate the two equations using the vertical jump-and-reach test, 2) develop a more accurate equation from a large heterogeneous population, 3) analyze gender differences and jump protocols, and 4) assess Predicted Residual Sum of Squares (PRESS) as a cross-validation procedure. One hundred eight college-age male and female athletes and nonathletes were tested on a force platform. They performed three maximal effort vertical jumps each of the squat jump (SJ) and countermovement jump (CMJ) while simultaneously performing the vertical jump-and-reach test. Regression analysis was used to predict peak power from body mass and vertical jump height. SJ data yielded a better power prediction equation than did CMJ data because of the greater variability in CMJ technique. The following equation was derived from SJ data: Peak Power (W) = 60.7x (jump height cm]) +45.3x(body mass [kg])-2055. This equation revealed greater accuracy than either the Lewis or previous Harman et al. equations and underestimated peak power by less than 1%, with a SEE of 355.0 W using SJ protocol. The use of one equation for both males and females resulted in only a slight (5% of power output) difference between genders. Using CMJ data in the SJ-derived equation resulted in only a 2.7% overestimation of peak power. Cross-validation of regression equations using PRESS reveals accurate and reliable R2 and SEE values. The SJ equation is a slightly more accurate equation than that derived from CMJ data. This equation should be used in the determination of peak power in place of the formulas developed by both Harman et al. and Lewis. Separate equations for males and females are unnecessary.