Category 1: Quantifying Movement Demands
Studies within this category (n = 22) determined the basic patterns of movement to improve athlete conditioning and performances, examined error levels when using IT in different activity scenarios, and determined the validity and the reliability of using IT. Several studies (n = 13) used IT to examine the movement of players during competition, because coaches and sport physiologists often seek information about the basic patterns of movement to use tactical strategies or to improve fitness and conditioning during practice. For example, Wisbey et al. (82) reported that Australian Football League (AFL) athletes covered 3.4% more distance, and demonstrated a 23% increase in running and a 14% increase in intensity levels during a 5-year period. Aughey (4) compared regular season and finals matches of the AFL and noted an 11% increase in the total distance, a 9% increase in high-intensity running, and a 97% increase in the number of maximal accelerations during final matches. Such detailed results on the movement patterns of AFL athletes provide coaches with additional information about how to properly design conditioning, preparation, and recovery programs.
A common finding across the studies in this category (n = 15) was that IT errors resulted from increased velocity (n = 7), repetitive motion (e.g., sprinting, shuffling, and jogging) particularly within confined spaces (n = 4), short-interval exercise (n = 5), and high-intensity activities (n = 6). In AFL and cricket studies, the standard error of estimate (SEE) was between 10 and 40%, indicating that IT units require further validation when measuring sprints and change of direction activities, especially because results tend to be highly variable (4,66). This notion of the difficulty in measuring faster speeds with accuracy is evident when examining tennis movement patterns in confined spaces, where IT underestimated the peak and mean speed by 10–30% (27). Jennings et al. (42) reported a similar conclusion when assessing Australian Rules football player strides and movement patterns. The greatest percentage of difference was between IT monitoring during short-interval activities and changes in direction. These results suggest that the devices may fail to capture all relevant data during movements performed at high velocities and with frequent changes in the direction.
Johnston et al. (43) examined movement demands of team sport simulation circuits and cited the need to exercise caution when using IT to observe high-speed velocities, because IT error level increased as the velocity of exercise increased. In fact, the IT was found to be significantly unreliable at velocities >20 km·h−1, indicating that there may be a velocity threshold where the IT is less accurate for measuring the speed. However, when the duration of a task increased, the IT error decreased. Many studies in this category have indicated significant decreases in standard error with the increased duration of measurements, which held true for all current models of IT (18,27,51,68). When the velocity of a moving object is decreased or the movement patterns are more linear, the IT has more time to capture and process the data. Quick and sudden changes in the direction make it much more difficult for the IT to read changes in positional coordinates. By focusing on linear, endurance-based activities, researchers are able to collect more accurate long-term data.
Petersen et al. (66) reported an IT standard of error between 0.5 and 2.1% among MinimaxX, SPI-Pro, and SPI-10 units when measuring low-intensity endurance activities during cricket-specific training drills. These results validated the claims of previous studies that IT reported the lowest standard errors when measuring low-intensity endurance movements compared with an average SEE of 2%. Coutts and Duffield (18) reported a 5.3% SEE with SPI-10, SPI Elite, and WiSPI units when measuring high-intensity running and total distance, with increased errors occurring during very–high intensity running. The IT unit error detection, which varies from 1 to 10 Hz (79), is based on the hertz power, defined as the magnitude of frequency measured per second. The IT units will detect satellite signals at different rates and units with a higher frequency are statistically more reliable (3,79). Studies have indicated that despite variability among IT Hz power, error can be reduced by two-thirds if the task duration is increased (3,37,42). Integrated technology units have acceptable levels of accuracy and reliability for lower intensity, long duration movements but may not be reliable for measuring higher intensity activities. Further improvements are required to validate IT for accurately measuring high-intensity and variable movement tasks, and practitioners and researchers will have to account for the variability and error of such tasks to confidently quantify the tasks in question.
Category 2: Assessing Differences Between Training and Competition Demands
Studies within this category (n = 12) compared training and competition demands by assessing the physiological outputs of athletes (n = 10), measuring real-time quantitative measurement of player movement patterns (n = 8), and using fitness testing to predict athletic performances (n = 3). Comparing training and competition demands helps determine if modifications in training are needed so that training could adequately match competition demands. Gabbett et al. (33) studied women's field hockey training and competition and concluded that athletes spent more time in low-intensity activity during training sessions compared with the time spent in moderate and high-intensity activities during competition. The training sessions did not reflect the physiological demands of competition, indicating that modifications may be needed in training group size and drill complexity to simulate the competition demands better. These results are comparable with those of a study conducted on adolescent rugby players, where it was concluded that they covered greater distances and spent more time jogging, striding, and sprinting in match play compared with that while training (39). Integrated technology can then quantify the above velocity ranges to provide physiological information to compare training and competition demands. The studies above have significant implications on the field, for having specific quantitative information on training, and competition differences can aid in developing game-specific training sessions and drills that simulate the physical demands the athletes experience in games. A word of caution is advisable in regard to always using practice to simulate game play—this is best done in conjunction with a full periodization plan that incorporates rest and recovery periods with intense training and competition.
Another aspect often analyzed was the comparison of player position relative to training and competition. There are significantly different physiological demands imposed on athletes based on their respective position, and this applies to multiple sports (2,18,52,82). For example, Petersen et al. (65) quantified and compared cricket movement patterns during training sessions and competition. Fast bowlers sprinted twice as often, covered 3 times the distance, and had a small work-recovery ratio compared with that of other positions. In comparison, the wicketkeeper rarely sprinted during the match, and covered on average 7 less kilometers compared to sprinting of fast bowlers. These results suggest that positional differences may require specific conditioning programs to be fully prepared for the physical demands of the respective sport. Wisbey et al. (82) also noted differences between the player positions in Australian Rules football. Nomadic players covered 3.5% more distance, had a 17% higher exertion index, and spent 23% more time running compared with that of forwards and defenders. This information is vital for coaches, because understanding the differences between positions can determine the relationship between movement demands and key game indicators, such as decision making, skill execution, possessions, tackling, and scoring.
A third trend found in the literature was the relationship between fitness testing and athletic performance. Mooney et al. (62) determined that there was a strong relationship between the Yo-Yo IR2 fitness test and the match performance of AFL players, where the playing experience and position affected fitness test scores and ball disposals during matches. Differences were also noted in a study that examined male soccer players (22). Players who performed small-side game training compared with general running training spent 29.4 seconds more in aerobic training and had higher V[Combining Dot Above]O2max levels during training. These results indicated that soccer-specific training was a more effective method of aerobic training compared with general running and provides the soccer athletes an advantage heading into a match. Such results can reveal if player experience and position can affect fitness and match performance, and may reveal whether preseason and inseason conditioning programs need to be altered to provide proper preparation and recovery practices to benefit individual and team performance.
Understanding the differences between positional requirements and training protocols can further refine the training and recovery process, because modifying the design and complexity of training programs will better simulate the demands of competition. Then, in turn, there is a significantly greater transfer of skills from training to competition. Future studies should aim at assessing individual player differences at various positions, because basing training protocols on the skill sets of individual players can enable coaches and trainers to develop athletes not only by their positional requirements but also by their unique skill sets; this in turn can bring out the full potential of the athletes.
Category 3: Measuring Physiological and Metabolic Demands
Studies within this category (n = 16) measured physiological and metabolic demands to assess muscle damage and hormonal responses (n = 4), determine effective fitness and recovery protocols (n = 3), and determine fatigue levels among athletes (n = 10). Analysis of the literature revealed that the IT is used to examine muscle damage and hormonal response during activity, to formulate proper recovery protocols and to determine mechanism of fatigue during match play (2,20,26,60,63). For example, McLellan et al. (57) measured creatine kinase (CK), a biomarker for muscle damage, and hormone levels of testosterone and cortisol combined with IT movement data to assess muscle damage and determine the necessary time period for full muscle recovery. They concluded that CK and cortisol levels peaked at 24 hours postmatch and that a period of at least 5 days was required to achieve full recovery from muscle damage. Another study investigated the effects of Re-Activate:01, a multinutrient supplement that contains ingredients aimed to increase muscular strength (e.g., adenosine triphosphate), energy level (e.g., folic acid), metabolism function (e.g., black pepper extract), muscle recovery (e.g., L-histidine and citruline malate), alertness (e.g., lysine HCl), and electrolyte levels (e.g., sodium bicarbonate), on performance based on the IT movement data (60). The authors observed the same peak in CK and hormone levels postmatch as noted in the previous study. Supplementation increased very–high intensity mean speed during the first half and moderate-intensity mean speed during the second half, indicating that supplementation may aid in the recovery process. These studies can be highly beneficial to athletes and coaches, for quantitative information on the time frame needed for optimal muscle recovery can ensure that athletes are fully rested and prepared for future match play.
Analysis of physiological and metabolic demands also revealed information about formulating proper fitness and recovery methods, because failure to do so may cause athletes to have potential performance decrements and injuries. Buchheit et al. (9) observed the effects of spa treatment (sauna, cold water immersion, and jacuzzi) on match play performance. They determined that using the IT to measure high-intensity running was an effective way to link performance with the effects of various spa treatments on male soccer players. They concluded that spa treatments provided effective recovery for muscle damage caused by high-intensity running. Gabbett et al. (34) investigated the physical demands of professional rugby matches compared with training activities used in preparation for competition. Match play had more moderate and heavy collisions, skill activities, and high-intensity efforts compared with training sessions. These results suggest that the physical demands of training sessions did not fully prepare athletes for the demands of match play, and may increase the recovery time needed for muscle damage and fatigue. Such studies have significant implications for preparation and recovery programs, because determining the most effective prematch and postmatch protocols can decrease the time needed for athletes to recover from muscle damage and ensure that fatigue does not arise in future performances because of a lack of recovery time.
Using the IT to measure fatigue was found to be inconclusive, because studies noted mixed results as to whether the IT can properly quantify fatigue. It is unclear whether such results are generalizable to multiple sports because most studies examined AFL players (2,4,19). Currently, there are no fatigue studies on cricket, field hockey, and soccer, sports often used as the subject population for IT-based studies. For example, Aughey (2) examined male soccer player work rates with IT units and found indications of fatigue during high-intensity running and maximal accelerations. However, the use of distance (meters) per minute of match time failed to determine evidence of fatigue when the average distance covered by the athletes was on the lower side. This discrepancy exists because when the total distance covered is on the lower side, averages may not easily demonstrate significant changes in the distance per minute to indicate fatigue. This discrepancy also demonstrates the importance of avoiding sport comparisons of fatigue, for using total distance measures may lead to false assumptions. Examination of Petersen et al.'s (65) data on male cricket athletes would reveal greater distance measures from the IT compared with that in a similar study conducted on Australian Rules football players (19). Without a full examination of the average per minute data, researchers would fail to recognize that fatigue is more evident in the Australian Rules football players because of a higher average distance per minute covered. If such errors in data analysis are to be avoided, a universal protocol and definition of fatigue through IT must be determined and quantified.
Category 4: Determining a Valid Definition for Velocity and Sprint Efforts
The fourth and final category of studies (n = 8) examined the justification for a valid definition of velocity (n = 3), determined the need to categorize sprinting efforts (n = 4), and assessed the difficulty of analyzing repeated-sprint efforts (n = 4). Previous literature examining velocity ranges has reported variable median velocity ranges for walking, jogging, and sprinting, creating confusion as to where to set values (10,22,25,30). Recently, Duffield et al. (26) classified mean velocity values as low-intensity (<7.0 km·h−1), moderate-intensity (7.0–14.4 km·h−1), high-intensity (>14.5 km·h−1), and very–high intensity (>20 km·h−1) running when examining the core temperature and match performance of AFL players. However, Impellizeri et al. (40) classified mean velocity into different thresholds, using jogging (7.6 km·h−1), low-speed running (10.2 km·h−1), moderate-speed running (13.9 km·h−1), high-speed running (17.1 km·h−1), and running sprint (26.7 km·h−1). These studies demonstrate that the lack of a universal definition leads to confusion as to where to set velocity threshold levels, for these discrepancies can lead to false conclusions being made based on where the velocity threshold is set. Using averages also cannot account for data points that may lie between the means, negatively affecting the validity and reliability of IT data.
The literature on defining sprint efforts revealed that the IT usually characterizes sprint efforts as movements >6–7 m·s−1, with Krustrup et al. (44) first defining sprinting as a velocity of >6.7 m·s−1. Other sprint definitions have been proposed, such as reaching 6.7 m·s−1 and lasting >2 seconds, or using 3 different threshold velocities to characterize sprinting (10,40). However, these definitions are problematic because the majority of field-based sports may begin short-duration, maximal efforts at lower velocities (1–2 seconds), failing to reach the defined criteria of a sprint effort (30,35,77). Inconsistencies in speeds that meet the criteria of a sprint demonstrate the need for a more concrete definition of sprinting. For example, Dwyer and Gabbett (30) collected data from male and female soccer and field hockey athletes, and male AFL players to propose standard velocity ranges and a definition of sprinting. The researchers concluded that 40% of their sprinting data would not fit the definition of traditional sprint because of its short-duration, low-velocity nature. Creation of a standardized definition and a broader range of velocity measures would account for data similar to those identified by Dwyer and Gabbett (30), and could provide athletes and coaches with vital information on how to train for high-acceleration movements and provide additional information on field-based sport physiology.
A third trend found in the literature was that discrepancies in quantifying velocity ranges and sprinting increased the difficulty of assessing repeated-sprint ability (RSA). The RSA, defined as short-duration sprints (1–2 seconds) interspersed with brief recovery periods, are difficult to quantify because these sprints do not meet the traditional criteria for sprinting (36). Earlier studies failed to establish the validity and the reliability of using the IT to assess the RSA (29,75,76). However, Barbero-Alvarez et al. (6) validated the use of the IT to assess speed and RSA during small-sided soccer training matches. The IT was able to assess the RSA over 30 m but not for 15 m, suggesting that the IT may not be suitable for assessing the RSA over shorter distances. Gabbett and Mulvey (35) reached a similar conclusion, because the majority of women soccer players performed primarily short-duration (1–2 seconds), maximal sprinting efforts that failed to reach the RSA criterion of a minimum of 3 sprints with a recovery period <21 seconds between the sprints. Because of such inconsistencies, there is currently limited research on assessing the RSA using the IT; however, IT was recently piloted against a laser, and its errors of measuring instantaneous velocity were comparable with the criterion of the laser (3). With the large number of short-duration, high-acceleration movements that occur in field sports, it is vital that researchers continue to explore the RSA to provide athletes with additional information to enhance their performances and insight into advancing to higher levels of competition.
Although IT has significantly advanced over the past decade, there are several limitations derived from the literature that pose difficulties for collecting reliable and valid data (3,54). Limitations include scalability issues using ≥2 time-motion analysis devices, inconvenience for athletes to wear, and the effect of environmental conditions. Scalability issues often arise when using ≥2 time-motion analysis devices (e.g., the GPS network and an accelerometer) because of cost and participant inconvenience. The IT is primarily used for studying velocity and distance measurements, and to assess muscle damage, energy cost, and other physiological and biophysical adaptations that occur during match play, other devices are required. The IT alone is incapable of assessing physiological and biophysical measures. GPS is also incapable of measuring vertical jumps, because movement can only be monitored horizontally without satellite degradation; although accelerometers measure movements in several planes, this technology does not assess specific movements like the GPS does. The average cost of IT alone varies between $500 and 2,000 USDs, and adding additional technology can be very costly for researchers (15).
From the perspective of the subject, wearing multiple devices can be bothersome or uncomfortable, and can become distracting to athletes trying to focus on a game or practice while having to remember to comply with all directions on using the devices (54). Although research has noted that GPSports and Catapult Innovations have created IT devices that are compact and durable to withstand athletic competitions (3,10,19) the feasibility of using them during training and important matches is still questionable as many organizations (e.g., Guiness Premiership Rugby League, FIFA (Federation Internationale de Football Association), and US Tennis Association) refuse to allow their usage (8,20,31,63,71). Oftentimes, the device must be attached with special clothing such as a vest, which adds to the discomfort of the athlete and requires proper fitting to prevent errors in device readings (14). Also, the use of multiple devices may decrease the validity and reliability of the resulting data, because all devices must be calibrated and timed precisely in tangent, otherwise large discrepancies in data can occur (3,39,42).
Another limitation of IT is the potential effect of the environment on the desired measurement capacities. Environmental conditions that can affect the signal accuracy of the IT include the following: (a) being indoors, (b) atmospheric conditions such as clouds, (c) local obstructions such as tall buildings, or (d) being in a highly populated area (3,16,45,58). Before reaching the IT, the satellite signals can become influenced by such conditions, leading to an error in the calculated distance from the satellite and consequently produces errors and missing data in speed and position measurements (54).
The main issue plaguing the use of sport-based IT during match play is the inability to use IT indoors. No study to date has analyzed indoor-based team sports using the technology. Integrated technology requires towers and satellites for signal transmission, and having the barrier of a closed building prevents the IT from reading accurate data (49). Indoor settings cause degradations in signals with obstacles such as furniture, moving people, roofs, floors, and walls (73). These degradations lead to decreased recording of signals, inability to detect multipath locomotion, and increased near-far effects where reception of a stronger signal makes it impossible to detect weaker signals.
Analyzing physical fitness testing, assessing biophysical measures during training, and developing further usability of the IT are the focal points of future research. The correlations between physical fitness testing and match performance have been highlighted in many recent studies (9,60,62,63). Examining physical fitness testing enables the use of field tests to predict performances, provides strength and conditioning coaches with information on how to train their respective athletes, and provides comparisons for in-match performances. For example, Castagna et al. (12) examined the effects of intermittent endurance fitness on match performance in young male soccer players using IT to assess the effectiveness of plyometrics, sprinting training drills, and short or long interval running training. The endurance fitness measures used in the study positively affected match play performance in young male soccer players. Such studies can allow strength and conditioning coaches to hone in on the technical skills and fitness demands addressed during fitness testing and training to facilitate improved performance.
Another future direction of the field may involve biophysical measurements, such as the effects of supplements on performance, core temperature responses, indirect calorimetry, and hormone responses. These measures are vital when assessing changes in prematch and postmatch play, as preparation and recovery are 2 phases of training that can make or break the success of athletes. One area that is often an issue in high-intensity sports is blunt force trauma, which can cause microtears in skeletal muscle and postgame muscle soreness. McClellan et al. (57) addressed this issue by examining CK levels in the skeletal muscles and hormone responses of rugby players prematch, during, and postmatch play. The results suggested at least 5 days of modified activity were required to achieve optimal recovery from postcompetition muscle damage, providing insights into the physical demands of rugby. Examination of blunt force trauma and postmatch muscle damage through hormone responses in conjunction with IT movement data can enable sport scientists and coaches to further establish recovery programs to ensure that athletes are fully rested before future match play.
A solution to developing further usability of IT may reside in developing wearable clothing with built-in technology. In 2004, Japanese scientists developed the jogging support system, which combines GPS, heart rate monitoring, and accelerometry directly into clothing items worn (55). Elgethun et al. (32) also incorporated GPS technology into children's clothing to provide tracking and assessment of various physical activities, and concluded that GPS technology integrated into clothing provides the direct quantification of time-location activity patterns. Future development of wearable technology could enhance time, activity, and location of team sport studies, and provide valuable insights into the nature of physical activity among children and other populations.
An additional future direction involves enhancing the usability of IT. As mentioned above, IT devices are currently only usable outdoors, and development of advanced technology such as indoor cell phone GPS product SiRiFstar V can allow for necessary improvements in data collection to enable studies to be conducted within indoor settings. Another possible development for indoor IT use resides in creating indoor “satellites” or GPS towers inside stadiums or arenas that function in the same manner as those in outdoor settings (3). Because of the severe signal degradation that occurs while indoors, the development of advanced IT capabilities is an area in need of further examination. Data collection can also be enhanced through advanced mathematical equations, such as Player Load. Player Load, a modified vector magnitude calculated from accelerometer data, describes the differences in the relationship between distance and load, and can provide researchers with evidence to compare training drills (7,8,61). Player Load is highly correlated with the total distance measured by GPS networks, and creation of a standardized regression equation can provide increased accuracy on measuring data on total distance. This equation can be further applied to measure player load during match play analysis, and practitioners can use this information if a standardized reporting method is developed to enable them to give coaches and athletes feedback on individual and team performances. Increased accuracy in data may also lead to a better quantification of changes in the direction of movement, fatigue, and gait, which was difficult during early research (45,78).
Functionality of IT can be enhanced through increased battery power, minimization of the product size, and improved memory storage. Because some sporting events can exceed the average sport-based IT battery life of 4 hours, an increase in the battery life is needed (3). Decreasing IT device size can also be beneficial, because athletes wearing bulky technology during training and/or competition can limit their performance or endanger others. With a decrease in device size it is likely governing bodies will allow athletes to wear IT during elite competition (3). Increased memory storage within IT can enable researchers to perform extended studies determining the long-term effects of the variables in question, and it enables the study of trends and patterns in the physiological changes of the athletes. Memory storage can also improve upon limiting the amount of incomplete or lost data during match play analysis, which is a current problem in field-based sports (2,24,79). Field-based competition studies cannot be repeated unlike in laboratory settings; therefore, any discrepancies in data can prove to be catastrophic.
In conclusion, the development of IT has made a significant impact on the sporting realm. Integration technology provides coaches, physiologists, and athletes with real-time information on individual and team performances; in turn, this has led to significant improvements in the preparation, training, and recovery programs for field-based sport teams. Coaches can use IT to identify basic movement patterns to formulate tactical strategies based on player position, develop sport-specific training programs, and detect differences over extended periods of time. With an average reported reliability variation of 5–10% and low interunit variability across multiple studies, IT demonstrates significant accuracy in measuring field-based team sport exercise tasks. The ability of IT to provide real-time, noninvasive, and rapid data collection on measurements, such as impact load, directional movement, and activity identification offers an advantage over other time-motion analysis systems. Being small and lightweight in design, having memory storage of >4 hours of data, and measuring multiple athletes at once, IT serves as a cost-effective analysis method for training, match play, and injury prevention. Although IT has proven to accurately identify low-intensity, long duration, and linear movements, coaches should exercise caution when using IT to measure high-intensity and speed, frequent change of direction, and confined space movements because of its decreased validity and reliability. Coaches can also use IT data to compare training and competition demands to determine if modifications are needed in training and conditioning. The IT can assess if current training methodology adequately matches the demands placed on athletes during competition and determine whether player position and fitness levels impact training and match play demands.
A third use of IT involves physiological and metabolic demands, because IT data combined with biophysical markers (e.g., CK and testosterone) allow coaches to assess muscle damage and determine the necessary time period for full muscle recovery from match play. From this information, coaches have the ability to formulate improved recovery methods and determine which methods may be the most effective for the athletes. However, coaches should understand that the lack of a universal definition on velocity and repeated-sprint efforts can lead to a confusion on data analysis and where the velocity threshold should be set. Although measuring RSA can provide coaches information on high-intensity activities during competition, coaches must keep in mind using previous velocity threshold recommendations to assess RSA may lead to false conclusions based on where the threshold is set. Current IT research is aimed at investigating 4 main areas of study, where the strengths and weaknesses of IT are revealed through the validity and reliability of using IT to examine the 4 study areas. Fully understanding the current limitations and future directions of IT can enable coaches to understand the issues and topics of concern that need to be addressed to facilitate a lasting, significant influence of IT on team-based sport performance.
The authors would like to thank the reviewers and journal for meticulously critiquing this article. The authors disclose no conflicts of interest. Further, the results of the present review do not constitute endorsement of any product by the authors of the National Strength and Conditioning Association.
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Keywords:Copyright © 2014 by the National Strength & Conditioning Association.
accelerometers; GPS; training; field-based sports