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Josh L. Secomb, Jeremy M. Sheppard and Ben J. Dascombe

Purpose:

To provide a descriptive and quantitative time–motion analysis of surfing training with the use of global positioning system (GPS) and heart-rate (HR) technology.

Methods:

Fifteen male surfing athletes (22.1 ± 3.9 y, 175.4 ± 6.4 cm, 72.5 ± 7.7 kg) performed a 2-h surfing training session, wearing both a GPS unit and an HR monitor. An individual digital video recording was taken of the entire surfing duration. Repeated-measures ANOVAs were used to determine any effects of time on the physical and physiological measures.

Results:

Participants covered 6293.2 ± 1826.1 m during the 2-h surfing training session and recorded measures of average speed, HRaverage, and HRpeak as 52.4 ± 15.2 m/min, 128 ± 13 beats/min, and 171 ± 12 beats/min, respectively. Furthermore, the relative mean times spent performing paddling, sprint paddling to catch waves, stationary, wave riding, and recovery of the surfboard were 42.6% ± 9.9%, 4.1% ± 1.2%, 52.8% ± 12.4%, 2.5% ± 1.9%, and 2.1% ± 1.7%, respectively.

Conclusion:

The results demonstrate that a 2-h surfing training session is performed at a lower intensity than competitive heats. This is likely due to the onset of fatigue and a pacing strategy used by participants. Furthermore, surfing training sessions do not appear to appropriately condition surfers for competitive events. As a result, coaches working with surfing athletes should consider altering training sessions to incorporate repeated-effort sprint paddling to more effectively physically prepare surfers for competitive events.

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Carl Petersen, David Pyne, Marc Portus and Brian Dawson

Purpose:

The validity and reliability of three commercial global positioning system (GPS) units (MinimaxX, Catapult, Australia; SPI-10, SPI-Pro, GPSports, Australia) were quantified.

Methods:

Twenty trials of cricket-specific locomotion patterns and distances (walking 8800 m, jogging 2400 m, running 1200 m, striding 600 m, sprinting 20- to 40-m intervals, and run-a-three) were compared against criterion measures (400-m athletic track, electronic timing). Validity was quantified with the standard error of the estimate (SEE) and reliability estimated using typical error expressed as a coefficient of variation.

Results:

The validity (mean ± 90% confidence limits) for locomotion patterns walking to striding ranged from 0.4 ± 0.1 to 3.8 ± 1.4%, whereas for sprinting distances over 20 to 40 m including run-a-three (approx. 50 m) the SEE ranged from 2.6 ± 1.0 to 23.8 ± 8.8%. The reliability (expressed as mean [90% confidence limits]) of estimating distance traveled by walking to striding ranged from 0.3 (0.2 to 0.4) to 2.9% (2.3 to 4.0). Similarly, mean reliability of estimating different sprinting distances over 20 to 40 m ranged from 2.0 (1.6 to 2.8) to 30.0% (23.2 to 43.3).

Conclusions:

The accuracy and bias was dependent on the GPS brand employed. Commercially available GPS units have acceptable validity and reliability for estimating longer distances (600–8800 m) in walking to striding, but require further development for shorter cricket-specifc sprinting distances.

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Andrew D. White and Niall MacFarlane

Purpose:

The current study assessed the impact of full-game (FG) and time-on-pitch (TOP) procedures for global-positioning-system (GPS) analysis on the commonly used markers of physical performance in elite field hockey.

Methods:

Sixteen international male field hockey players, age 19–30, were studied (yielding 73 player analyses over 8 games). Physical activity was recorded using a 5-Hz GPS.

Results:

Distance covered, player load, maximum velocity, high-acceleration efforts, and distance covered at specified speed zones were all agreeable for both analysis procedures (P > .05). However, percentage time spent in 0–6 km/h was higher for FG (ES: –21% to –16%; P < .001), whereas the percentage time in all other speed zones (1.67–3.06 m/s, 3.06–4.17 m/s, 4.17–5.28 m/s, and > 6.39 m/s) and relative distance (m/min) were higher for TOP (ES: 8–10%, 2–7%, 2–3%, 1–1%, 0–1%, respectively; P < .001).

Conclusions:

These data demonstrate that GPS analysis procedures should be appropriate for the nature of the sport being studied. In field hockey, TOP and FG analysis procedures are comparable for distance-related variables but significantly different for time-dependent factors. Using inappropriate analysis procedures can alter the perceived physiological demand of elite field hockey because of “rolling” substitutions. Inaccurate perception of physiological demand could negatively influence training prescription (for both intensity and volume).

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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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Dac Minh Tuan Nguyen, Virgile Lecoultre, Yoshiyuki Sunami and Yves Schutz

Background:

Physical activity (PA) and related energy expenditure (EE) is often assessed by means of a single technique. Because of inherent limitations, single techniques may not allow for an accurate assessment both PA and related EE. The aim of this study was to develop a model to accurately assess common PA types and durations and thus EE in free-living conditions, combining data from global positioning system (GPS) and 2 accelerometers.

Methods:

Forty-one volunteers participated in the study. First, a model was developed and adjusted to measured EE with a first group of subjects (Protocol I, n = 12) who performed 6 structured and supervised PA. Then, the model was validated over 2 experimental phases with 2 groups (n = 12 and n = 17) performing scheduled (Protocol I) and spontaneous common activities in real-life condition (Protocol II). Predicted EE was compared with actual EE as measured by portable indirect calorimetry.

Results:

In protocol I, performed PA types could be recognized with little error. The duration of each PA type could be predicted with an accuracy below 1 minute. Measured and predicted EE were strongly associated (r = .97, P < .001).

Conclusion:

Combining GPS and 2 accelerometers allows for an accurate assessment of PA and EE in free-living situations.

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Carl Petersen, David B. Pyne, Marc R. Portus, Stuart Karppinen and Brian Dawson

Purpose:

The time-motion characteristics and the within-athlete variability in movement patterns were quantified for the same male fast bowler playing One Day International (ODI) cricket matches (n = 12).

Methods:

A number of different time motion characteristics were monitored using a portable 5-Hz global positioning system (GPS) unit (Catapult, Melbourne, Australia).

Results:

The bowler’s mean workload per ODI was 8 ± 2 overs (mean ± SD). He covered a total distance of 15.9 ± 2.5 km per game; 12 ± 3% or 1.9 ± 0.2 km was striding (0.8 ± 0.2 km) or sprinting (1.1 ± 0.2 km), whereas 10.9 ± 2.1 km was spent walking. One high-intensity (running, striding, or sprinting) repetition (HIR) occurred every 68 ± 12 s, and the average duration of a HI effort was 2.7 ± 0.1 s. The player also completed 66 ± 11 sprints per game; mean sprint distance was 18 ± 3 m and maximum sprinting speed 8.3 ± 0.9 m·s−1.

Conclusions:

The movement patterns of this fast bowler were a combination of highly intermittent activities of variable intensity on the base of ~16 km per game. This information provides insight for conditioning coaches to determine the physical demands and to adapt the training and recovery processes of ODI fast bowlers.

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Will Vickery, Ben Dascombe and Rob Duffield

Purpose:

To examine the relationship between session rating of perceived exertion (sRPE) and measures of internal and external training load (TL) in cricket batsmen and medium-fast bowlers during net-based training sessions.

Methods:

The internal (heart rate), external (movement demands, PlayerLoad), and technical (cricket-specific skills) loads of 30 male cricket players (age 21.2 ± 3.8 y, height 1.82 ± 0.07 m, body mass 79.0 ± 8.7 kg) were determined from net-based cricket-training sessions (n = 118). The relationships between sRPE and measures of TL were quantified using Pearson product–moment correlations respective to playing position. Stepwise multiple-regression techniques provided key internal- and external-load determinants of sRPE in cricket players.

Results:

Significant correlations were evident (r = -.34 to .87, P < .05) between internal and external measures of TL and sRPE, with the strongest correlations (r ≥ .62) for GPS-derived measures for both playing positions. In batsmen, stepwise multiple-regression analysis revealed that 67.8% of the adjusted variance in sRPE could be explained by PlayerLoad and high-intensity distance (y = 27.43 + 0.81 PlayerLoad + 0.29 high-intensity distance). For medium-fast bowlers, 76.3% of the adjusted variance could be explained by total distance and mean heart rate (y = 101.82 + total distance 0.05 + HRmean – 0.48).

Conclusion:

These results suggest that sRPE is a valid method of reporting TL among cricket batsmen and medium-fast bowlers. Position-specific responses are evident and should be considered when monitoring the TL of cricket players.

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Weimo Zhu, Zorica Nedovic-Budic, Robert B. Olshansky, Jed Marti, Yong Gao, Youngsik Park, Edward McAuley and Wojciech Chodzko-Zajko

Purpose:

To introduce Agent-Based Model (ABM) to physical activity (PA) research and, using data from a study of neighborhood walkability and walking behavior, to illustrate parameters for an ABM of walking behavior.

Method:

The concept, brief history, mechanism, major components, key steps, advantages, and limitations of ABM were first introduced. For illustration, 10 participants (age in years: mean = 68, SD = 8) were recruited from a walkable and a nonwalkable neighborhood. They wore AMP 331 triaxial accelerometers and GeoLogger GPA tracking devices for 21 days. Data were analyzed using conventional statistics and highresolution geographic image analysis, which focused on a) path length, b) path duration, c) number of GPS reporting points, and d) interaction between distances and time.

Results:

Average steps by subjects ranged from 1810−10,453 steps per day (mean = 6899, SD = 3823). No statistical difference in walking behavior was found between neighborhoods (Walkable = 6710 ± 2781, Nonwalkable = 7096 ± 4674). Three environment parameters (ie, sidewalk, crosswalk, and path) were identified for future ABM simulation.

Conclusion:

ABM should provide a better understanding of PA behavior’s interaction with the environment, as illustrated using a real-life example. PA field should take advantage of ABM in future research.

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Luis Suarez-Arrones, Carlos Arenas, Guillermo López, Bernardo Requena, Oliver Terrill and Alberto Mendez-Villanueva

Purpose:

This study describes the physical match demands relative to positional group in male rugby sevens.

Methods:

Ten highly trained players were investigated during competitive matches (N = 23) using GPS technology, heart rate (HR), and video recording.

Results:

The relative distance covered by the players throughout the match was 102.3 ± 9.8 m/min. As a percentage of total distance, 35.8% (36.6 ± 5.9 m/min) was covered walking, 26.0% (26.6 ± 5.5 m/min) jogging, 10.0% (10.2 ± 2.4 m/min) running at low intensity, 14.2% (14.5 ± 4.0 m/min) at medium intensity, 4.6% (4.7 ± 1.6 m/min) at high intensity, and 9.5% (9.7 ± 3.7 m/min) sprinting. For the backs, a substantial decrease in total distance and distance covered at low, medium, and high intensity was observed in the second half. Forwards exhibited a substantial decrease in the distance covered at medium intensity, high intensity, and sprinting in the 2nd half. Backs covered substantially more total distance at medium and sprinting speeds than forwards. In addition, the maximum length of sprint runs was substantially greater for the backs than forwards. On the contrary, forwards performed more tackles. The mean HR during the match in backs and forwards was similar, with the exception of time spent at HR intensities >90%HRmax, which was substantially higher in forwards.

Conclusion:

These findings provide a description of the different physical demands placed on rugby sevens backs and forwards. This information may be helpful in the development of positional and/or individualized physical-fitness training programs.

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Nick B. Murray, Tim J. Gabbett and Andrew D. Townshend

Objectives:

To investigate the relationship between the proportion of preseason training sessions completed and load and injury during the ensuing Australian Football League season.

Design:

Single-cohort, observational study.

Methods:

Forty-six elite male Australian football players from 1 club participated. Players were divided into 3 equal groups based on the amount of preseason training completed (high [HTL], >85% sessions completed; medium [MTL], 50–85% sessions completed; and low [LTL], <50% sessions completed). Global positioning system (GPS) technology was used to record training and game loads, with all injuries recorded and classified by club medical staff. Differences between groups were analyzed using a 2-way (group × training/competition phase) repeated-measures ANOVA, along with magnitude-based inferences. Injury incidence was expressed as injuries per 1000 h.

Results:

The HTL and MTL groups completed a greater proportion of in-season training sessions (81.1% and 74.2%) and matches (76.7% and 76.1%) than the LTL (56.9% and 52.7%) group. Total distance and player load were significantly greater during the first half of the in-season period for the HTL (P = .03, ES = 0.88) and MTL (P = .02, ES = 0.93) groups than the LTL group. The relative risk of injury for the LTL group (26.8/1000 h) was 1.9 times greater than that for the HTL group (14.2/1000 h) (χ2 = 3.48, df = 2, P = .17).

Conclusions:

Completing a greater proportion of preseason training resulted in higher training loads and greater participation in training and competition during the competitive phase of the season.