## 4 Discussion  
The objective of this study was to quantify STP on a trail course by using low-cost sensors in ecological conditions. The main results suggest that the use of inexpensive sensors (Stryd sensors, GPS watch and heart belt) allow the measurement of STP. These parameters are related to the running technique and the speed of a runner on the whole course or in different sections of the course (UH, DH, R). Following an analysis, these measurements allow the identification of different running strategies for the same type of terrain within a group of runners. However, they also allow a more detailed analysis to identify the different Spt in relation to the performance according to the variation of the terrain. Firstly, analysis of the physical characteristics of the runners showed that mass of the subjects was positively correlated with their height (r = 0.80; \(p<0.001\)) and to the length of \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **Time** & **NP** & **SF** & **GCT** & **FT** & **SL** & **VO** & **Kleg** \\ \hline  & _Min_ & _W/kg_ & _Spm_ & _Ms_ & _Ms_ & _Cm_ & _Cm_ & _kh/m_ \\ \hline
**Global** & \(54^{\prime}42\pm 8^{\prime}42\) & 3.3 \(\pm\) 0.5 & \(154\pm 11\) & \(484\pm 74\) & \(-93\pm 49\) & \(102\pm 13\) & \(5.5\pm 0.7\) & \(8.2\pm 1.4\) \\
**UH1** & \(12^{\prime}18\pm 2^{\prime}29\) & 4.1 \(\pm\) 0.7 & \(164\pm 15\) & \(334\pm 125\) & \(33\pm 91\) & \(86\pm 12\) & \(5.7\pm 1.0\) & \(9.4\pm 2.2\) \\
**UH2** & \(10^{\prime}57\pm 2^{\prime}09\) & 1.9 \(\pm\) 0.6 & \(88\pm 14\) & \(1327\pm 184\) & \(-631\pm 115\) & \(41\pm 5\) & \(0.3\pm 0.7\) & \(0.6\pm 1.4\) \\
**R** & \(5^{\prime}30\pm 54^{\prime\prime}\) & 3.9 \(\pm\) 0.6 & \(172\pm 10\) & \(252\pm 32\) & \(96\pm 26\) & \(112\pm 15\) & \(7.1\pm 1.0\) & \(9.7\pm 1.2\) \\
**DH1** & \(2^{\prime}09\pm 36^{\prime\prime}\) & 2.8 \(\pm\) 0.8 & \(193\pm 14\) & \(221\pm 32\) & \(90\pm 23\) & \(100\pm 20\) & \(6.1\pm 1.0\) & \(13.9\pm 2.7\) \\
**DH2** & \(6^{\prime}29\pm 57^{\prime\prime}\) & 3.6 \(\pm\) 0.7 & \(174\pm 12\) & \(208\pm 26\) & \(135\pm 25\) & \(145\pm 19\) & \(8.1\pm 1.3\) & \(10.7\pm 1.4\) \\ \hline \end{tabular} (NP) Normalized Power, (SF) Stride frequency, (GCT) Ground contact time, (FT) Fly time, (SL) Stride length, (VO) Vertical oscillation, (Kleg)  
Leg stiffness, (UH1) first uphill, (UH2) second uphill, (R) road section, (DH1) first downhill, (DH2) second downhill.  
\end{table} 
Table 2: Avg \(\pm\) SD values of time, normalized power, stride frequency, ground contact time, flight time, step length, vertical oscillation, and leg stiffness over the entire course and over each defined area of the course. their lower limb (r = 0.80; \(p\) < 0.001). Moreover, larger subjects were shown to have a lower SF than smaller subjects. Secondly, analysis of the data while looking at the performance of the riders over the entire course displayed that consistency of effort was one of the criteria for obtaining a better time on the loop determined in the test. In fact, the subjects who made a fast UH1 and a DH2 at high speed were those who achieved the best performance. In a study of pace strategies at the World Cross Country Championships, it was shown that the top 10 athletes had a steady race pace, while those who finished lower in the rankings had a fast pace tactic at the beginning of the race which decreased steadily throughout the course [30]. It is possible to suggest that when riders start in a bunch, their effort management changes. This study was a form of time trial which the subjects were alone, therefore it would be interesting to see in a future study if the race management would be different in a group. The \(p\) analysis showed that it is in the UH1 and R part that the most important values are developed by the subjects. This corresponds to sections of the course where the runners had to keep a high speed despite the slope or flatness. In the DH sections the \(p\) developed by the subjects is also important even though the runners are on a descending profile.