INTRODUCTION: To enable fair competitions, para nordic sit-skiing (PNSS) athletes are grouped based on their impairments effect on performance ability, i.e. classification. Each class is assigned a time factor which is multiplied with race times to get comparable corrected times. Time factors do not consider system mass (body and equipment) although it can differ significantly. Force generation within PNSS classes have been quantified [1] and course sections crucial to PNSS race times have been identified [2], but there is limited research on system mass in PNSS. Therefore, the aim was to quantify the effect of system mass on race times in PNSS classes. METHODS: System masses and GPS-data were collected from 11 PNSS athletes (4 classes) during the 10-km world championship race in Östersund, 2023. More than 50 mathematical models adhering to Newton`s 2nd law of motion were formulated, which included propulsive power (PP) and forces from gravity, friction and air drag. Model parameters were individually optimized to minimize the error between measured and modeled time along the entire race. In the final model, 2 PP parameters were used to differentiate between the classes. The average corrected race time from the top half of the official results list was multiplied with 22/23 season time factors to obtain typical race times for each class. A model for a typical skier (TS) of each class was then created from the typical race times and linear regressions between race times and the 2 PP parameters, respectively. All other parameter values were identical for all classes. The model for each TS was used to obtain PP profiles for the entire race which was then used to obtain race times for every TS with 3 different system masses (67, 69, and 71 kg). External force parameters were sampled from a normal distribution to reduce model sensitivity, totaling 4×3×500 simulations. RESULTS/DISCUSSION: The table shows the mean race times for the TSs with a system mass of 69 kg and the difference in mean race time with 2 kg system mass increase and reduction, respectively. On average, the race times differ 0.48% per kg from the 69 kg race times, but more data in each class is needed to further investigate possible differences between classes. The figure shows larger differences in speed between classes with the same system mass than within a class with different system masses, but similar overall distributions (for a course section). CONCLUSION: The differences in race time due to altered system mass could be used as guidance if equipment restrictions are considered to minimize the influence from equipment on competition results. It can also be used by athletes and teams in prioritizing their competition preparations.
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