Comparing bioenergetic models for the optimisation of pacing strategy in road cycling

Abstract

PostprintThis is the accepted version of a paper published in Sports Engineering. This paper has been peerreviewed but does not include the final publisher proof-corrections or journal pagination.Citation for the original published paper (version of record):Sundström, D., Carlsson, P., Tinnsten, M. (2014) Comparing bioenergetic models for the optimisation of pacing strategy in road cycling. Sports Engineering AbstractRoad cycling performance is dependent on race tactics and pacing strategy. To optimise the pacing strategy for any race performed with no drafting, a numerical model was introduced, one that solves equations of motion while minimising the finishing time by varying the power output along the course. The power output was constrained by two different hydraulic models: the simpler critical power model for intermittent exercise (CPIE) and the more sophisticated Margaria-Morton model (M-M). These were compared with a constant power strategy (CPS). The simulation of the three different models was carried out on a fictional 75 kg cyclist, riding a 2,000 m course. This resulted in finishing times of 162.4 s, 155.8 s and s and speed variances of 0.58 %, 0.26 % and 0.29 % for the CPS, CPIE and M-M simulations respectively. Furthermore, the average power output was 469.7 W, 469.7 W and 469.1 W for the CPS, CPIE and M-M simulations respectively. The M-M model takes more physiological phenomena into consideration compared to the CPIE model and therefore contributes to an optimised pacing strategy that is more realistic. Therefore, the M-M model might be more suitable for future studies on optimal pacing strategy, despite the relatively slower finishing time.

Sport science for salmon and other species: ecological consequences of metabolic power constraints

“…; Sundström et al . ). Although the function to be optimised in ecology and sports typically differs (e.g.…”

Prediction of pacing and cornering strategies during cycling individual time trials with optimal control

“…Therefore, it was impossible to evaluate the goodness of the predictions of the roll angles, lateral accelerations, and steering angles. Similarly, the choice of the bioenergetic model (i.e., the critical power model) might not have been the best possible choice [22] and this assumption cannot be tested against any experimental data. (e) In the adopted version of the critical power model, the time characteristics ( ) for the discharge and the recharge of the anaerobic sources were considered equal.…”

“…For example, an optimal control problem can be formulated [15][16][17][18][19][20], where the objective is to minimise the arrival time by appropriate selection of the power output distribution. In these optimal control problems, the physiological constraints such as the anaerobic sources depletion are typically included by means of the critical power model [21,22].…”

Adaptive feedback system for optimal pacing strategies in road cycling

“…Most of the recent studies investigating optimal pacing strategies have focused on the underlying physiological model [8][9][10][11][12]. Unfortunately, they provide not much information with regard to practical applications, since they are mainly based on theoretical arguments and none of them provide experimental evidence for the validity of the resulting strategies.…”