## 4 Discussion
This flexibility allows overloads to be customised to an athlete's specific needs. By customising the F\({}_{\text{r}}\), coaches can tailor the overloads to different sprint acceleration phases or parts of the force-velocity curve to ensure that the training stimulus matches the goals [5]. One of the aims of this study was to determine how HRD overloads alter the force or power output and the FvP profile. To our knowledge, only two studies have investigated the FvP profiles of sled sprinting (with overload) under different conditions [13; 17]. In both studies, \(\
u_{0}\) decreased and F\({}_{0}\) increased with greater overloads, while P\({}_{\text{max}}\) remained unchanged or changed only minimally. This is probably because P\({}_{\text{max}}\) is the product of F\({}_{0}\) and \(\
u_{0}\), which change in an inversely proportional manner with increasing overloads. The FvP profiles calculated from the HRD and FP in our study yielded identical results. F\({}_{0}\) increased and \(\
u_{0}\) decreased significantly from MiL to HiL, while P\({}_{\text{max}}\) remained unchanged. These observations confirm that different HRD overload conditions can create biomechanical conditions specific to the sprint acceleration phase over an extended period of time [5]. As the FvP profiles of overload sprints can be directly determined using a non-motorised hydraulic device, studies focusing on the assessment of neuromuscular capacity during resisted sprinting in athletes could benefit from the use of the HRD. In addition, the HRD is user-friendly, affordable compared to motorised devices [22], and able to monitor training loads and adaptations using integrated hardware and external software. The future development of a fully integrated system could improve its practical application. It has already been suggested that the FvP profile variables, in addition to neuromuscular capacity information, have limited value for training prescription per se [35]. The use of theoretical and instantaneous F\({}_{0}\) and P\({}_{\text{max}}\) may not represent the force and power output over the entire sprint. The description of this ability is particularly important as overground sprint running is a cyclical task where overall performance is influenced by the ability to generate a high horizontal force [36; 37] and maintain a high power output [38] throughout the sprint. Therefore, F\({}_{\text{avg}}\) and P\({}_{\text{avg}}\) may be more appropriate performance metrics for optimising overload and resisted sprint training as they reflect the force and power output over the entire 20 m of sprinting. An illustrative example comparing the FvP profile and average variables for optimisation from the results of our study is the following. If the goal is to train with the highest power output, the MiL, MoL, and HiL could all be optimal because a similar P\({}_{\text{max}}\) was obtained in the 20 m sprint. However, the P\({}_{\text{avg}}\) suggests that HiL may be more effective because an average power output during the 20 m sprint was the highest. It remains questionable whether the overloads that lead to a higher power output are the optimal training stimuli for sprint acceleration performance. Interestingly, the results of the sled sprint intervention study conducted by [PERSON] et al. [39] indicated that the greatest improvements in acceleration performance were achieved when overloads that decreased v\({}_{\text{max}}\) by 24% and 32% were used. For comparison, a similar decrease in v\({}_{\text{max}}\) was observed during HiL in our study (15-40% v\({}_{\text{max}}\) decrease). The F\({}_{\text{avg}}\) and P\({}_{\text{avg}}\) could also be used within sessions to optimise training volume. Just as strength training performed with a lower power decay between repetitions has been associated with an improved performance [40], tracking the P\({}_{\text{avg}}\) decay could be a useful metric for improving the economy of resisted sprint training.