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Steve Barrett

3 , 5 and Australian rules football (AF 6 ) players using vector magnitude equations, with the most common referred to as PlayerLoad. 7 The reliability and validity of these devices have shown acceptable levels during running activities in team sport players for PlayerLoad 7 and locomotor

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Charlie Bowen, Kristian Weaver, Nicola Relph and Matt Greig

measures of PlayerLoad based on the rate of change of acceleration. 15 , 22 Given the aims of the current study, the uniaxial measures of PlayerLoad (mediolateral, anteroposterior, and vertical) were also subdivided into directional indices, so as to consider medial and lateral for example. The medial

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Billy T. Hulin, Tim J. Gabbett, Rich D. Johnston and David G. Jenkins

), mediolateral ( x -axis), and vertical ( z -axis). 1 – 3 Triaxial vector-magnitude PlayerLoad (PL VM ) is calculated as the sum of the squared instantaneous rate of change in acceleration in each of the 3 vectors ( x -, y - and z -axes), which is then squared and divided by 100. 3 PlayerLoad in each

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Eirik H. Wik, Live S. Luteberget and Matt Spencer

Team handball matches place diverse physical demands on players, which may result in fatigue and decreased activity levels. However, previous speed-based methods of quantifying player activity may not be sensitive for capturing short-lasting team-handball-specific movements.

Purpose:

To examine activity profiles of a women’s team handball team and individual player profiles, using inertial measurement units.

Methods:

Match data were obtained from 1 women’s national team in 9 international matches (N = 85 individual player samples), using the Catapult OptimEye S5. PlayerLoad/min was used as a measure of intensity in 5- and 10-min periods. Team profiles were presented as relative to the player’s match means, and individual profiles were presented as relative to the mean of the 5-min periods with >60% field time.

Results:

A high initial intensity was observed for team profiles and for players with ≥2 consecutive periods of play. Substantial declines in PlayerLoad/min were observed throughout matches for the team and for players with several consecutive periods of field time. These trends were found for all positional categories. Intensity increased substantially in the final 5 min of the first half for team profiles. Activity levels were substantially lower in the 5 min after a player’s most intense period and were partly restored in the subsequent 5-min period.

Discussion:

Possible explanations for the observed declines in activity profiles for the team and individual players include fatigue, situational factors, and pacing. However, underlying mechanisms were not accounted for, and these assumptions are therefore based on previous team-sport studies.

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Dean J. McNamara, Tim J. Gabbett, Paul Chapman, Geraldine Naughton and Patrick Farhart

Purpose:

The use of wearable microtechnology to monitor the external load of fast bowling is challenged by the inherent variability of bowling techniques between bowlers. This study assessed the between-bowlers variability in PlayerLoad, bowling velocity, and performance execution across repeated bowling spells.

Methods:

Seven national-level fast bowlers completed two 6-over bowling spells at a batter during a competitive training session. Key dependent variables were PlayerLoad calculated with a MinimaxX microtechnology unit, ball velocity, and bowling execution based on a predetermined bowling strategy for each ball bowled. The between-bowlers coefficient of variation (CV), repeated-measures ANOVA, and smallest worthwhile change were calculated over the 2 repeated 6-over bowling spells and explored across 12-over, 6-over, and 3-over bowling segments.

Results:

From the sum of 6 consecutive balls, the between-bowlers CV for relative peak PlayerLoad was 1.2% over the 12-over bowling spell (P = .15). During this 12-over period, bowling-execution (P = .43) scores and ball-velocity (P = .31) CVs were calculated as 46.0% and 0.4%, respectively.

Conclusions:

PlayerLoad was found to be stable across the repeated bowling spells in the fast-bowling cohort. Measures of variability and change across the repeated bowling spells were consistent with the performance measure of ball velocity. The stability of PlayerLoad improved when assessed relative to the individual’s peak PlayerLoad. Only bowling-execution measures were found to have high variability across the repeated bowling spells. PlayerLoad provides a stable measure of external workload between fast bowlers.

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Steve Barrett, Adrian Midgley and Ric Lovell

Purpose:

The study aimed to establish the test–retest reliability and convergent validity of PlayerLoad™ (triaxial-accelerometer data) during a standardized bout of treadmill running.

Methods:

Forty-four team-sport players performed 2 standardized incremental treadmill running tests (7–16 km/h) 7 d apart. Players’ oxygen uptake (VO2; n = 20), heart rate (n = 44), and triaxialaccelerometer data (PlayerLoad; n = 44) measured at both the scapulae and at the center of mass (COM), were recorded. Accelerometer data from the individual component planes of PlayerLoad (anteroposterior [PLAP], mediolateral [PLML], and vertical [PLV]) were also examined.

Results:

Moderate to high test–retest reliability was observed for PlayerLoad and its individual planes (ICC .80–.97, CV 4.2–14.8%) at both unit locations. PlayerLoad was significantly higher at COM vs scapulae (223.4 ± 42.6 vs 185.5 ± 26.3 arbitrary units; P = .001). The percentage contributions of individual planes to PlayerLoad were higher for PLML at the COM (scapulae 20.4% ± 3.8%, COM 26.5% ± 4.9%; P = .001) but lower for PLV (scapulae 55.7% ± 5.3%, COM 49.5% ± 6.9%; P = .001). Between-subjects correlations between PlayerLoad and VO2, and between PlayerLoad and heart rate were trivial to moderate (r = –.43 to .33), whereas within-subject correlations were nearly perfect (r = .92–.98).

Conclusions:

PlayerLoad had a moderate to high degree of test–retest reliability and demonstrated convergent validity with measures of exercise intensity on an individual basis. However, caution should be applied in making between-athletes contrasts in loading and when using recordings from the scapulae to identify lower-limb movement patterns.

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Steve Barrett, Adrian W. Midgley, Christopher Towlson, Andrew Garrett, Matt Portas and Ric Lovell

Purpose:

To assess the acute alterations in triaxial accelerometry (PlayerLoad [PLVM]) and its individual axial planes (anteroposterior PlayerLoad [PLAP], mediolateral PlayerLoad [PLML], and vertical PlayerLoad [PLV]) during a standardized 90-min soccer match-play simulation (SAFT90). Secondary aims of the study were to assess the test–retest reliability and anatomical location of the devices.

Methods:

Semiprofessional (n = 5) and university (n = 15) soccer players completed 3 trials (1 familiarization, 2 experimental) of SAFT90. PlayerLoad and its individual planes were measured continuously using micromechanical-electrical systems (MEMS) positioned at the scapulae (SCAP) and near the center of mass (COM).

Results:

There were no between-halves differences in PLVM; however, within-half increases were recorded at the COM, but only during the 1st half at the SCAP. Greater contributions to PLVM were provided by PLV and PLML when derived from the SCAP and COM, respectively. PLVM (COM 1451 ± 168, SCAP 1029 ± 113), PLAP (COM 503 ± 99, SCAP 345 ± 61), PLML (COM 712 ± 124, SCAP 348 ± 61), and PLV (COM 797 ± 184, SCAP 688 ± 124) were significantly greater at the COM than at the SCAP. Moderate and high test–retest reliability was observed for PlayerLoad and its individual planes at both locations (ICC .80–.99).

Conclusions:

PlayerLoad and its individual planes are reliable measures during SAFT90 and detected within-match changes in movement strategy when the unit was placed at the COM, which may have implications for fatigue management. Inferring alterations in lower-limb movement strategies from MEMS units positioned at the SCAP should be undertaken with caution.

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Billy T. Hulin, Tim J. Gabbett, Nathan J. Pickworth, Rich D. Johnston and David G. Jenkins

et al. 6 Workload Quantification Microtechnology (100 Hz, Optimeye S5; Catapult Innovations, Melbourne, Australia) was used to quantify training and match workloads. This technology has acceptable reliability for measuring PlayerLoad. 10 External workload associated with field-based demands of rugby

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Adam Jones, Chris Brogden, Richard Page, Ben Langley and Matt Greig

measure of external loading in soccer. 17 , 18 Typically integrated within the GPS unit is a triaxial accelerometer, a microinertial sensor that provides a higher sampling frequency than the GPS unit. This microtechnology is used to establish a mechanical loading metric termed PlayerLoad, based on the

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Adam Douglas, Michael A. Rotondi, Joseph Baker, Veronica K. Jamnik and Alison K. Macpherson

recordings of the gyroscope and magnetometer, can successfully quantify sport-specific movements. 7 One such method to quantify the workload performed by an athlete is PlayerLoad, which sums the individual triaxial accelerometer vectors to produce an instantaneous measure of work rate, expressed in