## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Participant**} & \multicolumn{8}{c}{**Large Circle**} \\ \cline{2-9}  & **Number of Laps** & **Known Distance/m** & **GNSSD/m** & **Impulse Load** & **MAG** & **SUM** & **Player Load** \\ \hline
17 & 8.5 & 213.63 & 205.1 & 1154.62 & 420.24 & 19,031.75 & 11.63 \\
18 & 9 & 226.19 & 222.1 & 1006.09 & 488.78 & 24,166.76 & 10.03 \\
19 & 9.5 & 238.76 & 233.9 & 819.64 & 514.5 & 27,886.33 & 11.93 \\
20 & 10 & 251.33 & 249.4 & 1238.42 & 669.27 & 28,285.3 & 15.27 \\
21 & 10.5 & 263.89 & 259.9 & 1215.66 & 560.68 & 26,430.16 & 16.72 \\
22 & 11 & 276.46 & 273.9 & 981.49 & 648.91 & 32,951.12 & 13.32 \\
23 & 11.5 & 289.03 & 283.3 & 1073.9 & 641.12 & 37,486.39 & 14.05 \\
24 & 12 & 301.59 & 300.9 & 1081.76 & 646.01 & 31,967.92 & 15.82 \\
25 & 12.5 & 314.16 & 288.1 & 1460.79 & 631.2 & 27,005.63 & 19.97 \\
26 & 13 & 326.73 & 312.3 & 1364.23 & 680.51 & 32,644.37 & 17.12 \\
27 & 13.5 & 339.29 & 338.1 & 1223.83 & 709.73 & 35,287.43 & 14.34 \\
28 & 14 & 351.86 & 347.1 & 1833.86 & 767.71 & 35,025.29 & 18.75 \\
29 & 14.5 & 364.42 & 344.6 & 1631.2 & 796.79 & 35,797.69 & 20.84 \\
30 & 15 & 376.99 & 381.3 & 2339.7 & 881.78 & 45,740.11 & 20.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._of running demands compared to PL [12]. Future studies should investigate what, if any, advantages MAG or other accelerometry based measures may provide compared to PL. Previous research has indicated that, independent of movement velocity (i.e., walk, jog, run, sprint), rapid directional change degrades GNSS accuracy. For instance, GNSS may underestimate distance during shuttle trials (\(-2.16\pm 3.84\%\)) and overestimate distance during exercise completed on curvilinear tracks (2.99 \(\pm\) 2.96%) [31]. However, the development of multiple GNSS technology may explain the high level of accuracy found in this study, which included two different curvilinear conditions (small circle, R = 0.999, R\({}^{2}\) = 0.997, RMSE = 0.047, \(p<0.001\); large circle, R = 0.999, R\({}^{2}\) = 0.999, RMSE = 0.027, \(p<0.001\)). Despite these promising results, questions remain about multiple GNSS system accuracy when measuring the distance completed in sport-related movements, where many changes of direction are required, and movement velocity is often higher than that used in this study. This investigation demonstrates that multiple GNSS systems and several accelerometry derived metrics can indicate total distance completed while walking. However, a host of questions remain regarding the potential advantages associated with these technologies to quantify training loads and detect events (e.g., contact, jumps, sprinting) in sport. In this study, total distance was the only load accounted for, and only locomotor movements  
Figure 7: Demonstration and summary of the study results. were included. Of course, in sport, different factors can influence training loads (e.g., acceleration, deceleration, jumping), and locomotor and non-locomotor movements will be performed. More broadly, further investigation is needed to assess the ability of GNSS and accelerometry derived metrics to measure training loads that include different movements and events such as running, sprinting, change of direction, jumping, collision, and kicking. Although this study demonstrated that GNSS and accelerometry derived measures are valid indicators of total distance, three important limitations to this study should be considered. First, while walking was performed around circles to limit the influence of initiating movement and braking associated with changing direction and total distance was the only variable assessed, caution should be used when applying the current results to other activities. Second, participants were asked to walk at their natural pace, and no method has been used to standardize the walking speed, in which differences in the velocity rate between participants might induce some variation.