) 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
James J. Malone, Arne Jaspers, Werner Helsen, Brenda Merks, Wouter G.P. Frencken and Michel S. Brink
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
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.
Helen J. Moore, Catherine A. Nixon, Amelia A. Lake, Wayne Douthwaite, Claire L. O’Malley, Claire L. Pedley, Carolyn D. Summerbell and Ashley C. Routen
Evidence suggests that many contemporary urban environments do not support healthy lifestyle choices and are implicated in the obesity pandemic. Middlesbrough, in the northeast of England is one such environment and a prime target for investigation.
To measure physical activity (PA) levels in a sample of 28 adolescents (aged 11 to 14 years) and describe the environmental context of their activity and explore where they are most and least active over a 7-day period, accelerometry and Global Positioning System (GPS) technology were used. Twenty-five of these participants also took part in focus groups about their experiences and perceptions of PA engagement.
Findings indicated that all participants were relatively inactive throughout the observed period although bouts of moderate-vigorous physical activity (MVPA) were identified in 4 contexts: school, home, street, and rural/urban green spaces, with MVPA levels highest in the school setting. Providing access to local facilities and services (such as leisure centers) is not in itself sufficient to engage adolescents in MVPA.
Factors influencing engagement in MVPA were identified within and across contexts, including ‘time’ as both a facilitator and barrier, perceptions of ‘gendered’ PA, and the social influences of peer groups and family members.
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
Matthew Pearce, David H. Saunders, Peter Allison and Anthony P. Turner
. 17 By dividing adolescent leisure-time physical activity into context-based dimensions and combining data from global positioning system (GPS) receivers, diaries, and accelerometers, it may be possible to more accurately characterize the specific contexts where MVPA occurs. Consistent with an
Dan Weaving, Nicholas E. Dalton, Christopher Black, Joshua Darrall-Jones, Padraic J. Phibbs, Michael Gray, Ben Jones and Gregory A.B. Roe
embedment in decision making for coaches during their planning of the training process. 5 In the age of technology, 5 numerous TL methods and variables are now available to practitioners working in team-sports including microtechnology (eg, global positioning systems [GPS]) and the session rating of