encouraging PA. The use of global positioning system (GPS) data in addition to accelerometer data has been advocated in health behavior studies in order to enrich data with location-specific information, 31 allowing researchers to more accurately specify the use of physical environments in relation to PA and
Dave H.H. Van Kann, Sanne I. de Vries, Jasper Schipperijn, Nanne K. de Vries, Maria W.J. Jansen and Stef P.J. Kremers
Bruno Marrier, Yann Le Meur, Cédric Leduc, Julien Piscione, Mathieu Lacome, Germain Igarza, Christophe Hausswirth, Jean-Benoît Morin and Julien Robineau
system (GPS), except during 2 tournaments (C2 at In-2 and In-3), where the stadium structure prevented capture of the satellite signals. For these 2 tournaments, the GPS data were extrapolated on the basis of the average variation in each GPS variable observed from the first to the second tournament in
James J. Malone, Arne Jaspers, Werner Helsen, Brenda Merks, Wouter G.P. Frencken and Michel S. Brink
) and the in-season period (39 wk). The GK trained on average of 5 times per week during preseason and 4.2 times per week during in-season, respectively. The GK wore a global positioning system (GPS) device (firmware version 717, OptimEye G5; Catapult Sports, Melbourne, Australia), which has shown
Javier Yanci, Daniel Castillo, Aitor Iturricastillo, Tomás Urbán and Raúl Reina
capacity. 4 – 7 However, only 2 scientific studies have been focused on analyzing official matches. 8 , 9 Boyd et al, 9 using global positioning system (GPS) monitors, analyzed the total distance (TD) covered, as well as the distance covered at high and very high intensity, in 40 high-level footballers
Dale B. Read, Ben Jones, Sean Williams, Padraic J. Phibbs, Josh D. Darrall-Jones, Greg A.B. Roe, Jonathon J.S. Weakley, Andrew Rock and Kevin Till
The physical characteristics of match play (ie, running and collisions) in age-grade (eg, U18 [under-18]) rugby union players is a growing area of research. 1 – 3 Studies using global positioning systems (GPS) have published data from county representative, 4 school, 5 academy, 2 and
Paolo Gaudino, F. Marcello Iaia, Anthony J. Strudwick, Richard D. Hawkins, Giampietro Alberti, Greg Atkinson and Warren Gregson
The aim of the current study was to identify the external-training-load markers that are most influential on session rating of perceived exertion (RPE) of training load (RPE-TL) during elite soccer training.
Twenty-two elite players competing in the English Premier League were monitored. Training-load data (RPE and 10-Hz GPS integrated with a 100-Hz accelerometer) were collected during 1892 individual training sessions over an entire in-season competitive period. Expert knowledge and a collinearity r < .5 were used initially to select the external training variables for the final analysis. A multivariateadjusted within-subjects model was employed to quantify the correlations of RPE and RPE-TL (RPE × duration) with various measures of external training intensity and training load.
Total high-speed-running (HSR; >14.4 km/h) distance and number of impacts and accelerations >3 m/s2 remained in the final multivariate model (P < .001). The adjusted correlations with RPE were r = .14, r = .09, and r = .25 for HSR, impacts, and accelerations, respectively. For RPE-TL, the correlations were r = .11, r = .45, and r = .37, respectively.
The external-load measures that were found to be moderately predictive of RPE-TL in soccer training were HSR distance and the number of impacts and accelerations. These findings provide new evidence to support the use of RPE-TL as a global measure of training load in elite soccer. Furthermore, understanding the influence of characteristics affecting RPE-TL may help coaches and practitioners enhance training prescription and athlete monitoring.
Rich D. Johnston, Tim J. Gabbett and David G. Jenkins
To assess the influence of playing standard and physical fitness on pacing strategies during a junior team-sport tournament.
A between-groups, repeated-measures design was used. Twenty-eight junior team-sport players (age 16.6 ± 0.5 y, body mass 79.9 ± 12.0 kg) from a high-standard and low-standard team participated in a junior rugby league tournament, competing in 5 games over 4 d (4 × 40-min and 1 × 50-min game). Players wore global positioning system (GPS) microtechnology during each game to provide information on match activity profiles. The Yo-Yo Intermittent Recovery Test (level 1) was used to assess physical fitness before the competition.
High-standard players had an initially higher pacing strategy than the low-standard players, covering greater distances at high (ES = 1.32) and moderate speed (ES = 1.41) in game 1 and moderate speed (ES = 1.55) in game 2. However, low-standard players increased their playing intensity across the competition (ES = 0.57–2.04). High-standard/high-fitness players maintained a similar playing intensity, whereas high-standard/low-fitness players reduced their playing intensities across the competition.
Well-developed physical fitness allows for a higher-intensity pacing strategy that can be maintained throughout a tournament. High-standard/low-fitness players reduce playing intensity, most likely due to increased levels of fatigue as the competition progresses. Low-standard players adopt a pacing strategy that allows them to conserve energy to produce an “end spurt” in the latter games. Maximizing endurance fitness across an entire playing group will maximize playing intensity and minimize performance reductions during the latter stages of a tournament.
Tim J. Gabbett and Caleb W. Gahan
To examine the nature and frequency of rugby league repeated high-intensity-effort (RHIE) activity in relation to tries scored and conceded in successful and unsuccessful teams.
185 semiprofessional rugby league players (mean ± SD age 23.7 ± 3.2 y) from 11 teams.
Global positioning system (GPS) data were collected during 21 matches and analyzed for the total number of RHIE bouts, efforts per bout, duration of efforts, and recovery between efforts. Using notational analysis, a RHIE-bout frequency distribution, representing 0–60 s, 61–120 s, 121–180 s, 181–240 s, and 241–300 s before scoring and conceding a try, was established.
Over 50% of RHIE bouts occurred within 5 min of a try. Bottom-4 teams performed a greater proportion of bouts within 5 min of a try than top-4 teams (61.5% vs 48.2%, effect size, ES = 0.69 ± 0.28, P = .0001). Top-4 teams performed a greater number of RHIE bouts per conceded try (3.0 ± 2.1 vs 1.6 ± 0.7, ES = 0.74 ± 0.51, P < .05), while bottom-4 teams performed a greater number of RHIE bouts per try scored (3.6 ± 2.5 vs 2.1 ± 1.7, ES = 0.70 ± 0.71, P = .10).
The majority of rugby league RHIE bouts occur at critical periods during match play. Successful rugby league teams perform more RHIE bouts before conceding tries, while unsuccessful teams perform more bouts before scoring tries. These findings demonstrate that unsuccessful teams are required to work harder to score tries while successful teams work harder to prevent tries.
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.
Sergio Jiménez-Rubio, Archit Navandar, Jesús Rivilla-García, Víctor Paredes-Hernández and Miguel-Ángel Gómez-Ruano
. These parameters can be easily obtained from global positioning system (GPS) units attached to players. In particular, the recent and widely use of GPS devices monitors the player’s performance during training sessions and competitions, 17 recording various parameters. This information could help to