Search Results

You are looking at 41 - 50 of 298 items for :

Clear All
Restricted access

John F. Fitzpatrick, Kirsty M. Hicks and Philip R. Hayes

during the 6-week period, using a number of different methods: global positioning system (GPS), heart rate (HR) telemetry, and session rating of perceived exertion (sRPE). External load was measured using GPS units (MinimaxX S4; Catapult Sports, Melbourne, Australia) sampling at a frequency of 10 Hz. GPS

Restricted access

Matthew C. Varley, Arne Jaspers, Werner F. Helsen and James J. Malone

Purpose:

Sprints and accelerations are popular performance indicators in applied sport. The methods used to define these efforts using athlete-tracking technology could affect the number of efforts reported. This study aimed to determine the influence of different techniques and settings for detecting high-intensity efforts using global positioning system (GPS) data.

Methods:

Velocity and acceleration data from a professional soccer match were recorded via 10-Hz GPS. Velocity data were filtered using either a median or an exponential filter. Acceleration data were derived from velocity data over a 0.2-s time interval (with and without an exponential filter applied) and a 0.3-second time interval. High-speed-running (≥4.17 m/s2), sprint (≥7.00 m/s2), and acceleration (≥2.78 m/s2) efforts were then identified using minimum-effort durations (0.1–0.9 s) to assess differences in the total number of efforts reported.

Results:

Different velocity-filtering methods resulted in small to moderate differences (effect size [ES] 0.28–1.09) in the number of high-speed-running and sprint efforts detected when minimum duration was <0.5 s and small to very large differences (ES –5.69 to 0.26) in the number of accelerations when minimum duration was <0.7 s. There was an exponential decline in the number of all efforts as minimum duration increased, regardless of filtering method, with the largest declines in acceleration efforts.

Conclusions:

Filtering techniques and minimum durations substantially affect the number of high-speed-running, sprint, and acceleration efforts detected with GPS. Changes to how high-intensity efforts are defined affect reported data. Therefore, consistency in data processing is advised.

Restricted access

Richard J. Taylor, Dajo Sanders, Tony Myers, Grant Abt, Celia A. Taylor and Ibrahim Akubat

training dose; however, if these TL measures fail to inform a strong enough dose-response relationship the manipulation of training using such measures may not result in expected training outcomes. 7 The availability of microelectromechanical systems/global positioning systems (MEMS/GPS) and heart

Restricted access

Jonathan D. Bartlett, Fergus O’Connor, Nathan Pitchford, Lorena Torres-Ronda and Samuel J. Robertson

Purpose:

The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches.

Methods:

TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39–89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis.

Results:

Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players.

Conclusions:

This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.

Restricted access

Simon J. MacLeod, Chris Hagan, Mikel Egaña, Jonny Davis and David Drake

. PubMed ID: 23090320 doi:10.1519/JSC.0b013e318277fd21 23090320 10.1519/JSC.0b013e318277fd21 5. Johnston R , Watsford M , Kelly S , Pine M , Spurrs R . Validity and interunit reliability of 10 Hz and 15 Hz GPS units for assessing athlete movement demands . J Strength Cond Res . 2014 ; 28

Restricted access

Peter Collins, Yahya Al-Nakeeb and Mark Lyons

Background:

Active school commuting is widely regarded as a key opportunity for youth to participate in physical activity (PA). However, the accurate measurement of the commute home from school and its contribution to total free-living moderateto- vigorous PA (MVPA) is relatively unexplored.

Methods:

Seventy-five adolescents (38 males, 37 females) wore an integrated GPS and heart rate device during after-school hours for 4 consecutive weekdays.

Results:

Active commuters were significantly more active (11.72 minutes MVPA) than passive commuters (3.5 minutes MVPA) during their commute home from school (P = .001). The commute home of walkers and cyclists on average contributed 35% of their total free-living PA. However, there was no significant difference in the overall free-living PA levels of passive and active commuters (P > .05). A total 92.7% of the youth living within 1.5 miles of the school actively commuted, compared with 16.7% of the youth who lived further away. Socioeconomic differences in commuting patterns were also evident.

Conclusions:

The findings highlighted the significant proportion of total free-living PA that was attributed to active commuting home from school. The study demonstrates the usefulness of utilizing GPS and heart rate data to accurately track young people’s after-school PA. Demographic influences and implications for future research are discussed.

Restricted access

Tom Kempton, Anita Claire Sirotic, Ermanno Rampinini and Aaron James Coutts

Purpose:

To describe the metabolic demands of rugby league match play for positional groups and compare match distances obtained from high-speed-running classifications with those derived from high metabolic power.

Methods:

Global positioning system (GPS) data were collected from 25 players from a team competing in the National Rugby League competition over 39 matches. Players were classified into positional groups (adjustables, outside backs, hit-up forwards, and wide-running forwards). The GPS devices provided instantaneous raw velocity data at 5 Hz, which were exported to a customized spreadsheet. The spreadsheet provided calculations for speed-based distances (eg, total distance; high-speed running, >14.4 km/h; and very-highspeed running, >18.1 km/h) and metabolic-power variables (eg, energy expenditure; average metabolic power; and high-power distance, >20 W/kg).

Results:

The data show that speed-based distances and metabolic power varied between positional groups, although this was largely related to differences in time spent on field. The distance covered at high running speed was lower than that obtained from high-power thresholds for all positional groups; however, the difference between the 2 methods was greatest for hit-up forwards and adjustables.

Conclusions:

Positional differences existed for all metabolic parameters, although these are at least partially related to time spent on the field. Higher-speed running may underestimate the demands of match play when compared with high-power distance—although the degree of difference between the measures varied by position. The analysis of metabolic power may complement traditional speed-based classifications and improve our understanding of the demands of rugby league match play.

Restricted access

Niels J. Nedergaard, Mark A. Robinson, Elena Eusterwiemann, Barry Drust, Paulo J. Lisboa and Jos Vanrenterghem

Purpose:

To investigate the relationship between whole-body accelerations and body-worn accelerometry during team-sport movements.

Methods:

Twenty male team-sport players performed forward running and anticipated 45° and 90° side-cuts at approach speeds of 2, 3, 4, and 5 m/s. Whole-body center-of-mass (CoM) accelerations were determined from ground-reaction forces collected from 1 foot–ground contact, and segmental accelerations were measured from a commercial GPS accelerometer unit on the upper trunk. Three higher-specification accelerometers were also positioned on the GPS unit, the dorsal aspect of the pelvis, and the shaft of the tibia. Associations between mechanical load variables (peak acceleration, loading rate, and impulse) calculated from both CoM accelerations and segmental accelerations were explored using regression analysis. In addition, 1-dimensional statistical parametric mapping (SPM) was used to explore the relationships between peak segmental accelerations and CoM-acceleration profiles during the whole foot–ground contact.

Results:

A weak relationship was observed for the investigated mechanical load variables regardless of accelerometer location and task (R 2 values across accelerometer locations and tasks: peak acceleration .08–.55, loading rate .27–.59, and impulse .02–.59). Segmental accelerations generally overestimated whole-body mechanical load. SPM analysis showed that peak segmental accelerations were mostly related to CoM accelerations during the first 40–50% of contact phase.

Conclusions:

While body-worn accelerometry correlates to whole-body loading in team-sport movements and can reveal useful estimates concerning loading, these correlations are not strong. Body-worn accelerometry should therefore be used with caution to monitor whole-body mechanical loading in the field.

Restricted access

Thomas Kempton, Anita C. Sirotic and Aaron J. Coutts

Purpose:

To examine differences in physical and technical performance profiles using a large sample of match observations drawn from successful and less-successful professional rugby league teams.

Methods:

Match activity profiles were collected using global positioning satellite (GPS) technology from 29 players from a successful rugby league team during 24 games and 25 players from a less-successful team during 18 games throughout 2 separate competition seasons. Technical performance data were obtained from a commercial statistics provider. A progressive magnitude-based statistical approach was used to compare differences in physical and technical performance variables between the reference teams.

Results:

There were no clear differences in playing time, absolute and relative total distances, or low-speed running distances between successful and less-successful teams. The successful team possibly to very likely had lower higher-speed running demands and likely had fewer physical collisions than the less-successful team, although they likely to most likely demonstrated more accelerations and decelerations and likely had higher average metabolic power. The successful team very likely gained more territory in attack, very likely had more possessions, and likely committed fewer errors. In contrast, the less-successful team was likely required to attempt more tackles, most likely missed more tackles, and very likely had a lower effective tackle percentage.

Conclusions:

In the current study, successful match performance was not contingent on higher match running outputs or more physical collisions; rather, proficiency in technical performance components better differentiated successful and less-successful teams.

Restricted access

Jason C. Tee, Mike I. Lambert and Yoga Coopoo

Purpose:

In team sports, fatigue is manifested by a self-regulated decrease in movement distance and intensity. There is currently limited information on the effect of fatigue on movement patterns in rugby union match play, particularly for players in different position groups (backs vs forwards). This study investigated the effect of different match periods on movement patterns of professional rugby union players.

Methods:

Global positioning system (GPS) data were collected from 46 professional match participations to determine temporal effects on movement patterns.

Results:

Total relative distance (m/min) was decreased in the 2nd half for both forwards (–13%, ±8%, ES = very likely large) and backs (–9%, ±7%, ES = very likely large). A larger reduction in high-intensity-running distance in the 2nd half was observed for forwards (–27%, ±16%, ES = very likely medium) than for backs (–10%, ±15%; ES = unclear). Similar patterns were observed for sprint (>6 m/s) frequency (forwards –29%, ±29%, ES = likely small vs backs –13% ±18%, ES = possibly small) and acceleration (>2.75 m/s2) frequency (forwards –27%, ±24%, ES = likely medium vs backs –5%, ±46%, ES = unclear). Analysis of 1st- and 2nd-half quartiles revealed differing pacing strategies for forwards and backs. Forwards display a “slow-positive” pacing strategy, while the pacing strategy of backs is “flat.”

Conclusions:

Forwards suffered progressively greater performance decrements over the course of the match, while backs were able to maintain performance intensity. These findings reflect differing physical demands, notably contact and running loads, of players in different positions.