Statistical parametric mapping regression allow to identify the area of force-velocity-endurance profile related to the sport performance in elite athletes - Morel, B.; Bowen, M.; Vonderscher, M.; Delhaye, C.; Busseneau, P. F.; Di domenico, H.; Blervaque, L.; Messonnier, L.; Samozino, P. (29th Annual Congress of the European College of Sport Science, 2-5 July 2024, Book of Abstracts, 2024)
INTRODUCTION: A model has recently been proposed to describe the individuals force capacities as a function of movement velocity and exercise duration simultaneously [1]. This force-velocity-endurance (FoVE) profile of an athlete can be assessed during a 3-min all-out test modified to include regular assessments of force-velocity capacities all over the test. Statistical parametric mapping (SPM) can be used to analyse data with multiple dimensions (>1) [2]. It is, for instance, possible to run SPM regression analysis. The aim of this study was therefore to identify the area of the FoVE profile that most correlated with sports performance in elite athletes for 2 disciplines being rowing and ski mountaineering. METHODS: The same approach was used in two groups of elite athletes from the rowing (n=17, 4 women) and ski mountaineering (n=18, 7 women) national teams. Firstly, the FoVE profiles were assessed using the IFLET test [1] performed on a rowing ergometer for rowers and a cycling ergometer for skiers. To compare the profiles with sport performance, the best performance of the year (i.e. time) over 2000m was considered for rowers and the time at the national vertical race championships was used for skiers. SPM regression was performed to determine the correlation between performance being a scalar and the 3D FoVE profiles. The 5 percentiles most correlated with performance were retained for further analysis. RESULTS: The goodness of fit of the FoVE profiles were excellent (median r² = 0.95). The mean performance over 2000 m was 378 ± 23 s for the rowing group. The mean vertical run time was 22 min 4 s ± 2 min 4 s for the skiers group. SPM regression revealed that the top 5 percentiles of the FoVE profiles were highly correlated and had a low root-mean-square error (RMSE) with sport performance for both sports (r² = 0.88 and RMSE = 2.2%; r² = 0.83 and RMSE = 5.4% for rowers and skiers, respectively). In addition, the FoVE area most correlated with performance differed between the disciplines. The centroid of the area was for a velocity of 31% of maximum initial velocity and 1.3 Tau in rowing and 50% of maximum initial velocity and 6.3 Tau for ski mountaineering. CONCLUSION: For the two disciplines tested, and despite a particularly homogeneous population (all were elite athletes in the national team of their sport, including world champions and Olympic champions), a specific area of the FoVE profile correlated very strongly with sport performance. This opens up particularly interesting perspectives for individualised training. Indeed, based on the proposed approach, it is possible to simulate the performance benefits of training that specifically targets force or velocity capacity in the initial or fatigued state, in order to tailor training to optimise performance.