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Marco Cardinale and Matthew C. Varley

The need to quantify aspects of training to improve training prescription has been the holy grail of sport scientists and coaches for many years. Recently, there has been an increase in scientific interest, possibly due to technological advancements and better equipment to quantify training activities. Over the last few years there has been an increase in the number of studies assessing training load in various athletic cohorts with a bias toward subjective reports and/or quantifications of external load. There is an evident lack of extensive longitudinal studies employing objective internal-load measurements, possibly due to the cost-effectiveness and invasiveness of measures necessary to quantify objective internal loads. Advances in technology might help in developing better wearable tools able to ease the difficulties and costs associated with conducting longitudinal observational studies in athletic cohorts and possibly provide better information on the biological implications of specific external-load patterns. Considering the recent technological developments for monitoring training load and the extensive use of various tools for research and applied work, the aim of this work was to review applications, challenges, and opportunities of various wearable technologies.

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Andrew M. Murray and Matthew C. Varley

Purpose:

To investigate the influence of score line, level of opposition, and timing of substitutes on the activity profile of rugby sevens players and describe peak periods of activity.

Methods:

Velocity and distance data were measured via 10-Hz GPS from 17 international-level male rugby sevens players on 2–20 occasions over 4 tournaments (24 matches). Movement data were reported as total distance (TD), high-speed-running distance (HSR, 4.17−10.0 m/s), and the occurrence of maximal accelerations (Accel, ≥2.78 m/s2). A rolling 1-min sample period was used.

Results:

Regardless of score line or opponent ranking there was a moderate to large reduction in average and peak TD and HSR between match halves. A close halftime score line was associated with a greater HSR distance in the 1st minute of the 1st and 2nd halves compared with when winning. When playing against higher-compared with lower-ranked opposition, players covered moderately greater TD in the 1st minute of the 1st half (difference = 26%; 90% confidence limits = 6, 49). Compared with players who played a full match, substitutes who came on late in the 2nd half had a higher average HSR and Accel by a small magnitude (31%; 5, 65 vs 34%; 6, 69) and a higher average TD by a moderate magnitude (16%; 5, 28).

Conclusions:

Match score line, opposition, and substitute timing can influence the activity profile of rugby sevens players. Players are likely to perform more running against higher opponents and when the score line is close. This information may influence team selection.

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Matthew C. Varley, George P. Elias and Robert J. Aughey

Purpose:

To compare the peak 5-min period of high-velocity running (HiVR) during a soccer match using a predefined vs a rolling time interval.

Methods:

Player movement data were collected from 19 elite Australian soccer players over 11 competitive matches (77 individual match files) using a 5-Hz global-positioning system. Raw velocity data were analyzed to determine the period containing the greatest HiVR distance per match half and the distance covered in the subsequent epoch. Intervals were identified using either a predefined (distance covered in 5 min at every 5-min time point) or rolling (distance covered in 5 min from every time point) method. The percentage difference ± 90% confidence limits were used to determine differences between methods.

Results:

Predefined periods underestimated peak distance covered by up to 25% and overestimated the subsequent epoch by up to 31% compared with rolling periods. When the distance decrement between the peak and following period was determined, there was up to a 52% greater reduction in running performance using rolling periods than predefined ones.

Conclusions:

It is recommended that researchers use rolling as opposed to predefined periods when determining specific match intervals because they provide a more accurate representation of the HiVR distance covered. This will avoid underestimation of both match running distance and the decrement in running performance after an intense period of play. This may have practical implications for not only researchers but also staff involved in a club setting who use this reduction as evidence of transient fatigue during a match.

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Laura A. Garvican, Kristal Hammond, Matthew C. Varley, Christopher J. Gore, Francois Billaut and Robert J. Aughey

Purpose:

This study investigated the decrement in running performance of elite soccer players competing at low altitude and time course for abatement of these decrements.

Methods:

Twenty elite youth soccer players had their activity profile, in a sea-level (SL) and 2 altitude (Alt, 1600 m, d 4, and d 6) matches, measured with a global positioning system. Measures expressed in meters per minute of match time were total distance, low- and high-velocity running (LoVR, 0.01–4.16 m/s; HiVR, 4.17–10.0 m/s), and frequency of maximal accelerations (>2.78 m/s2). The peak and subsequent stanza for each measure were identified and a transient fatigue index calculated. Mean heart rate (HR) during the final minute of a submaximal running task (5 min, 11 km/h) was recorded at SL and for 10 d at Alt. Differences were determined between SL and Alt using percentage change and effect-size (ES) statistic with 90% confidence intervals.

Results:

Mean HR almost certainly increased on d 1 (5.4%, ES 1.01 ± 0.35) and remained probably elevated on both d 2 (ES 0.42 ± 0.31) and d3 (ES 0.30 ± 0.25), returning to baseline at d 5. Total distance was almost certainly lower than SL (ES –0.76 ± 0.37) at d 4 and remained probably reduced on d 6 (ES –0.42 ± 0.36). HiVR probably decreased at d 4 vs SL (–0.47 ± 0.59), with no clear effect of altitude at d 6 (–0.08 ± 0.41). Transient fatigue in matches was evident at SL and Alt, with a possibly greater decrement at Alt.

Conclusion:

Despite some physiological adaptation, match running performance of youth soccer players is compromised for at least 6 d at low altitude.

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George P. Elias, Victoria L. Wyckelsma, Matthew C. Varley, Michael J. McKenna and Robert J. Aughey

Purpose:

The efficacy of a single exposure to 14 min of contrast water therapy (CWT) or cold-water immersion (COLD) on recovery postmatch in elite professional footballers was investigated.

Method:

Twenty-four elite footballers participated in a match followed by 1 of 3 recovery interventions. Recovery was monitored for 48 h postmatch. Repeat-sprint ability (6 × 20-m), static and countermovement jump performance, perceived soreness, and fatigue were measured prematch and immediately, 24 h, and 48 h after the match. Soreness and fatigue were also measured 1 h postmatch. Postmatch, players were randomly assigned to complete passive recovery (PAS; n = 8), COLD (n = 8), or CWT (n = 8).

Results:

Immediately postmatch, all groups exhibited similar psychometric and performance decrements, which persisted for 48 h only in the PAS group. Repeatsprinting performance remained slower at 24 and 48 h for PAS (3.9% and 2.0%) and CWT (1.6% and 0.9%) but was restored by COLD (0.2% and 0.0%). Soreness after 48 h was most effectively attenuated by COLD (ES 0.59 ± 0.10) but remained elevated for CWT (ES 2.39 ± 0.29) and PAS (ES 4.01 ± 0.97). Similarly, COLD more successfully reduced fatigue after 48 h (ES 1.02 ± 0.72) than did CWT (ES 1.22 ± 0.38) and PAS (ES 1.91 ± 0.67). Declines in static and countermovement jump were ameliorated best by COLD.

Conclusions:

An elite professional football match results in prolonged physical and psychometric deficits for 48 h. COLD was more successful at restoring physical performance and psychometric measures than CWT, with PAS being the poorest.

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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.

Open access

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.

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Carlo Castagna, Matthew Varley, Susana C.A. Póvoas and Stefano D’Ottavio

Purpose:

To test the interchangeability of 2 match-analysis approaches for external-load detection considering arbitrary selected speeds and metabolic power (MP) thresholds in male top-level soccer.

Methods:

Data analyses were performed considering match physical performance of 60 matches (1200 player cases) of randomly selected Spanish, German, and English first-division championship matches (2013–14 season). Match analysis was performed with a validated semiautomated multicamera system operating at 25 Hz.

Results:

During a match, players covered 10,673 ± 348 m, of which 1778 ± 208 m and 2759 ± 241 m were performed at high intensity, as measured using speed (≥16 km/h, HI) and metabolic power (≥20 W/kg, MPHI) notations. High-intensity notations were nearly perfectly associated (r = .93, P < .0001). A huge method bias (980.63 ± 87.82 m, d = 11.67) was found when considering MPHI and HI. Very large correlations were found between match total distance covered and MPHI (r = .84, P < .0001) and HI (r = .74, P < .0001). Player high-intensity decelerations (≥–2 m/s2) were very largely associated with MPHI (r = .73, P < .0001).

Conclusions:

The speed and MP methods are highly interchangeable at relative level (magnitude rank) but not absolute level (measure magnitude). The 2 physical match-analysis methods can be independently used to track match external load in elite-level players. However, match-analyst decisions must be based on use of a single method to avoid bias in external-load determination.

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George P. Elias, Matthew C. Varley, Victoria L. Wyckelsma, Michael J. McKenna, Clare L. Minahan and Robert J. Aughey

Purpose:

The authors investigated the efficacy of a single exposure to 14 min of cold-water immersion (COLD) and contrast water therapy (CWT) on posttraining recovery in Australian football (AF).

Method:

Fourteen AF players participated in 3 wk of standardized training. After week 1 training, all players completed a passive recovery (PAS). During week 2, COLD or CWT was randomly assigned. Players undertook the opposing intervention in week 3. Repeat-sprint ability (6 × 20 m), countermovement and squat jumps, perceived muscle soreness, and fatigue were measured pretraining and over 48 h posttraining.

Results:

Immediately posttraining, groups exhibited similar performance and psychometric declines. At 24 h, repeat-sprint time had deteriorated by 4.1% for PAS and 1.0% for CWT but was fully restored by COLD (0.0%). At 24 and 48 h, both COLD and CWT attenuated changes in mean muscle soreness, with COLD (0.6 ± 0.6 and 0.0 ± 0.4) more effective than CWT (1.9 ± 0.7 and 1.0 ± 0.7) and PAS having minimal effect (5.5 ± 0.6 and 4.0 ± 0.5). Similarly, after 24 and 48 h, COLD and CWT both effectively reduced changes in perceived fatigue, with COLD (0.6 ± 0.6 and 0.0 ± 0.6) being more successful than CWT (0.8 ± 0.6 and 0.7 ± 0.6) and PAS having the smallest effect (2.2 ± 0.8 and 2.4 ± 0.6).

Conclusions:

AF training can result in prolonged physical and psychometric deficits persisting for up to 48 h. For restoring physical-performance and psychometric measures, COLD was more effective than CWT, with PAS being the least effective. Based on these results the authors recommend that 14 min of COLD be used after AF training.

Open access

Pitre C. Bourdon, Marco Cardinale, Andrew Murray, Paul Gastin, Michael Kellmann, Matthew C. Varley, Tim J. Gabbett, Aaron J. Coutts, Darren J. Burgess, Warren Gregson and N. Timothy Cable

Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on “Monitoring Athlete Training Loads—The Hows and the Whys” was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.