Commercially available microtechnology devices containing global positioning systems (GPSs) and microsensors (accelerometers, gyroscopes, and magnetometers) are commonly used to quantify the physical demands of rugby union. 1 During match play and training, players are divided into subgroups of
Ryan M. Chambers, Tim J. Gabbett, and Michael H. Cole
Simon J. MacLeod, Chris Hagan, Mikel Egaña, Jonny Davis, and David Drake
load monitoring models need to be prescriptive in nature by linking the training dose with the athlete’s response and performance. Microtechnology has allowed the monitoring and evaluation of external work performed by athletes. However, previous work 4 , 5 has shown variability in the accuracy of
Dean J. McNamara, Tim J. Gabbett, Peter Blanch, and Luke Kelly
including Australian football and rugby league use microtechnology and global positioning system (GPS) devices to monitor external workload. 7 – 9 In addition to GPS data, a combination of accelerometers (electromechanical devices that measure acceleration forces), gyroscopes (electronic devices that
Jamie Highton, Thomas Mullen, Jonathan Norris, Chelsea Oxendale, and Craig Twist
This aim of this study was to examine the validity of energy expenditure derived from microtechnology when measured during a repeated-effort rugby protocol. Sixteen male rugby players completed a repeated-effort protocol comprising 3 sets of 6 collisions during which movement activity and energy expenditure (EEGPS) were measured using microtechnology. In addition, energy expenditure was estimated from open-circuit spirometry (EEVO2). While related (r = .63, 90%CI .08–.89), there was a systematic underestimation of energy expenditure during the protocol (–5.94 ± 0.67 kcal/min) for EEGPS (7.2 ± 1.0 kcal/min) compared with EEVO2 (13.2 ± 2.3 kcal/min). High-speed-running distance (r = .50, 95%CI –.66 to .84) was related to EEVO2, while PlayerLoad was not (r = .37, 95%CI –.81 to .68). While metabolic power might provide a different measure of external load than other typically used microtechnology metrics (eg, high-speed running, PlayerLoad), it underestimates energy expenditure during intermittent team sports that involve collisions.
Gordon G. Sleivert
Wireless microtechnologies are rapidly emerging as useful tools for sport scientists to move their work out of the laboratory and into the field. The purpose of this report is to describe some of the practical aspects of using ingestible radiotelemetric temperature sensors in sport physiology. Information is also presented to demonstrate the utility of this technology in understanding individual differences in coping with environmental stress, optimizing heat adaptation, and fine-tuning competition strategy (pacing). Wireless core-temperature technology has already revolutionized field monitoring of elite athletes training and competing in extreme environments. These technologies are valuable tools for sport scientists to better understand the interaction between the physiology of exercise and the environment.
Heidi R. Thornton, André R. Nelson, Jace A. Delaney, Fabio R. Serpiello, and Grant M. Duthie
interest to declare. References 1. Cummins C , Orr R , O’Connor H , West C . Global positioning systems (GPS) and microtechnology sensors in team sports: a systematic review . Sports Med . 2013 ; 43 ( 10 ): 1025 – 1042 . PubMed ID: 23812857 doi:10.1007/s40279-013-0069-2 23812857 10.1007/s
Thomas W.J. Lovell, Anita C. Sirotic, Franco M. Impellizzeri, and Aaron J. Coutts
The purpose of this study was to examine the validity of session rating of perceived exertion (sRPE) for monitoring training intensity in rugby league.
Thirty-two professional rugby league players participated in this study. Training-load (TL) data were collected during an entire season and assessed via microtechnology (heart-rate [HR] monitors, global positioning systems [GPS], and accelerometers) and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and various other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during rugby league training.
There were significant within-individual correlations between sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 62.4% of the adjusted variance in sRPE-TL could be explained by TL measures of distance, impacts, body load, and training impulse (y = 37.21 + 0.93 distance − 0.39 impacts + 0.18 body load + 0.03 training impulse). Furthermore, 35.2% of the adjusted variance in sRPE could be explained by exercise-intensity measures of percentage of peak HR (%HRpeak), impacts/min, m/min, and body load/min (y = −0.01 + 0.37%HRpeak + 0.10 impacts/min + 0.17 m/min + 0.09 body load/min).
A combination of internal and external TL factors predicts sRPE in rugby league training better than any individual measures alone. These findings provide new evidence to support the use of sRPE as a global measure of exercise intensity in rugby league training.
Stuart J. Cormack, Renee L. Smith, Mitchell M. Mooney, Warren B. Young, and Brendan J. O’Brien
To determine differences in load/min (AU) between standards of netball match play.
Load/min (AU) representing accumulated accelerations measured by triaxial accelerometers was recorded during matches of 2 higher- and 2 lower-standard teams (N = 32 players). Differences in load/min (AU) were compared within and between standards for playing position and periods of play. Differences were considered meaningful if there was >75% likelihood of exceeding a small (0.2) effect size.
Mean (± SD) full-match load/min (AU) for the higher and lower standards were 9.96 ± 2.50 and 6.88 ± 1.88, respectively (100% likely lower). The higher standard had greater (mean 97% likely) load/min (AU) values in each position. The difference between 1st and 2nd halves’ load/min (AU) was unclear at the higher standard, while lower-grade centers had a lower (−7.7% ± 10.8%, 81% likely) load/min (AU) in the 2nd half and in all quarters compared with the 1st. There was little intrastandard variation in individual vector contributions to load/min (AU); however, higher-standard players accumulated a greater proportion of the total in the vertical plane (mean 93% likely).
Higher-standard players produced greater load/min (AU) than their lower-standard counterparts in all positions. Playing standard influenced the pattern of load/min (AU) accumulation across a match, and individual vector analysis suggests that different-standard players have dissimilar movement characteristics. Load/min (AU) appears to be a useful method for assessing activity profile in netball.
Annie C. Jeffries, Lee Wallace, and Aaron J. Coutts
To describe the training demands of contemporary dance and determine the validity of using the session rating of perceived exertion (sRPE) to monitor exercise intensity and training load in this activity. In addition, the authors examined the contribution of training (ie, accelerometry and heart rate) and non-training-related factors (ie, sleep and wellness) to perceived exertion during dance training.
Training load and ActiGraphy for 16 elite amateur contemporary dancers were collected during a 49-d period, using heart-rate monitors, accelerometry, and sRPE. Within-individual correlation analysis was used to determine relationships between sRPE and several other measures of training intensity and load. Stepwise multiple regressions were used to determine a predictive equation to estimate sRPE during dance training.
Average weekly training load was 4283 ± 2442 arbitrary units (AU), monotony 2.13 ± 0.92 AU, strain 10677 ± 9438 AU, and average weekly vector magnitude load 1809,707 ± 1015,402 AU. There were large to very large within-individual correlations between training-load sRPE and various other internal and external measures of intensity and load. The stepwise multiple-regression analysis also revealed that 49.7% of the adjusted variance in training-load sRPE was explained by peak heart rate, metabolic equivalents, soreness, motivation, and sleep quality (y = –4.637 + 13.817%HRpeak + 0.316 METS + 0.100 soreness + 0.116 motivation – 0.204 sleep quality).
The current findings demonstrate the validity of the sRPE method for quantifying training load in dance, that dancers undertake very high training loads, and a combination of training and nontraining factors contribute to perceived exertion in dance training.
James J. Malone, Ric Lovell, Matthew C. Varley, and Aaron J. Coutts
Athlete-tracking devices that include global positioning system (GPS) and microelectrical mechanical system (MEMS) components are now commonplace in sport research and practice. These devices provide large amounts of data that are used to inform decision making on athlete training and performance. However, the data obtained from these devices are often provided without clear explanation of how these metrics are obtained. At present, there is no clear consensus regarding how these data should be handled and reported in a sport context. Therefore, the aim of this review was to examine the factors that affect the data produced by these athlete-tracking devices and to provide guidelines for collecting, processing, and reporting of data. Many factors including device sampling rate, positioning and fitting of devices, satellite signal, and data-filtering methods can affect the measures obtained from GPS and MEMS devices. Therefore researchers are encouraged to report device brand/model, sampling frequency, number of satellites, horizontal dilution of precision, and software/firmware versions in any published research. In addition, details of inclusion/exclusion criteria for data obtained from these devices are also recommended. Considerations for the application of speed zones to evaluate the magnitude and distribution of different locomotor activities recorded by GPS are also presented, alongside recommendations for both industry practice and future research directions. Through a standard approach to data collection and procedure reporting, researchers and practitioners will be able to make more confident comparisons from their data, which will improve the understanding and impact these devices can have on athlete performance.