There is currently no universally recommended and accepted method of data processing within the science of indirect calorimetry for either mixing chamber or breath-by-breath systems of expired gas analysis. Exercise physiologists were first surveyed to determine methods used to process oxygen consumption ((.)VO2) data, and current attitudes to data processing within the science of indirect calorimetry. Breath-by-breath datasets obtained from indirect calorimetry during incremental exercise were then used to demonstrate the consequences of commonly used time, breath and digital filter post-acquisition data processing strategies. Assessment of the variability in breath-by-breath data was determined using multiple regression based on the independent variables ventilation (VE), and the expired gas fractions for oxygen and carbon dioxide, FEO2 and FECO2, respectively. Based on the results of explanation of variance of the breath-by-breath (.)VO2 data, methods of processing to remove variability were proposed for time-averaged, breath-averaged and digital filter applications. Among exercise physiologists, the strategy used to remove the variability in (.)VO2 measurements varied widely, and consisted of time averages (30 sec [38%], 60 sec [18%], 20 sec [11%], 15 sec [8%]), a moving average of five to 11 breaths (10%), and the middle five of seven breaths (7%). Most respondents indicated that they used multiple criteria to establish maximum ((.)VO2 ((.)VO2max) including: the attainment of age-predicted maximum heart rate (HR(max)) [53%], respiratory exchange ratio (RER) >1.10 (49%) or RER >1.15 (27%) and a rating of perceived exertion (RPE) of >17, 18 or 19 (20%). The reasons stated for these strategies included their own beliefs (32%), what they were taught (26%), what they read in research articles (22%), tradition (13%) and the influence of their colleagues (7%). The combination of VE, FEO2 and FECO2 removed 96-98% of (.)VO2 breath-by-breath variability in incremental and steady-state exercise (.)VO2 data sets, respectively. Correction of residual error in (.)VO2 datasets to 10% of the raw variability results from application of a 30-second time average, 15-breath running average, or a 0.04 Hz low cut-off digital filter. Thus, we recommend that once these data processing strategies are used, the peak or maximal value becomes the highest processed datapoint. Exercise physiologists need to agree on, and continually refine through empirical research, a consistent process for analysing data from indirect calorimetry.

Global positioning system (GPS) technology has improved the speed, accuracy, and ease of time-motion analyses of field sport athletes. The large volume of numerical data generated by GPS technology is usually summarized by reporting the distance traveled and time spent in various locomotor categories (e.g., walking, jogging, and running). There are a variety of definitions used in the literature to represent these categories, which makes it nearly impossible to compare findings among studies. The purpose of this work was to propose standard definitions (velocity ranges) that were determined by an objective analysis of time-motion data. In addition, we discuss the limitations of the existing definition of a sprint and present a new definition of sprinting for field sport athletes. Twenty-five GPS data files collected from 5 different sports (men's and women's field hockey, men's and women's soccer, and Australian Rules Football) were analyzed to identify the average velocity distribution. A curve fitting process was then used to determine the optimal placement of 4 Gaussian curves representing the typical locomotor categories. Based on the findings of these analyses, we make recommendations about sport-specific velocity ranges to be used in future time-motion studies of field sport athletes. We also suggest that a sprint be defined as any movement that reaches or exceeds the sprint threshold velocity for at least 1 second and any movement with an acceleration that occurs within the highest 5% of accelerations found in the corresponding velocity range. From a practical perspective, these analyses provide conditioning coaches with information on the high-intensity sprinting demands of field sport athletes, while also providing a novel method of capturing maximal effort, short-duration sprints.

Accelerometry is a valuable method of measuring player load in netball, and the present results provide new information about the activity profile of different playing positions.

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4The purpose of this study was to assess the validity of a GPS tracking system to estimate 5 energy expenditure (EE) during exercise and field sport locomotor movements. Twenty-6 seven participants each completed one 90 minute exercise session on an outdoor synthetic 7 futsal pitch. During the exercise session participants wore a 5 Hz GPS unit interpolated to 15 8 Hz (SPI HPU, GPSports Pty Ltd, Australia) and a portable gas analyser (Metamax® 3B, 9Cortex Pty Ltd, Germany) which acted as the criterion measure of EE. The exercise session 10 was comprised of alternating five minute exercise bouts of randomised walking, jogging, 11 running or a field sport circuit (x3) followed by 10 minutes of recovery. One-way ANOVA 12 showed significant (p<0.01) and very large underestimations between GPS metabolic power 13 derived EE and VO2 derived EE for all field sport circuits (% difference ≈ -44%). No 14 differences in EE were observed for the jog (7.8%) and run (4.8%) while very large 15 overestimations were found for the walk (43.0%). The GPS metabolic power EE over the 16 entire 90 minute session was significantly lower (p<0.01) than the VO2 EE, resulting in a 17 moderate underestimation overall (-19%). The results of this study suggest that a GPS 18 tracking system using the metabolic power model of EE does not accurately estimate EE in 19 field sport movements or over an exercise session consisting of mixed locomotor activities 20 interspersed with recovery periods; however is able to provide a reasonably accurate 21 estimation of EE during continuous jogging and running. 22 23