Modification of the spontaneous seat-to-stand transition in cycling with bodyweight and cadence variations

Abstract

When a high power output is required in cycling, a spontaneous transition by the cyclist from a seated to a standing position generally occurs. In this study, by varying the cadence and cyclist bodyweight, we tested whether the transition is better explained by the greater power economy of a standing position or by the emergence of mechanical constraints that force cyclists to stand. Ten males participated in five experimental sessions corresponding to different bodyweights (80%, 100%, or 120%) and cadences (50RPM, 70RPM, or 90RPM). In each session, we first determined the seat-to-stand transition power (SSTP) in an incremental test. The participants then cycled at 20%, 40%, 60%, 80%, 100%, or 120% of the SSTP in the seated and standing positions, for which we recorded the saddle forces and electromyogram (EMG) signals of eight lower limb muscles. We estimated the cycling cost using an EMG cost function (ECF) and the minimal saddle forces in the seated position as an indicator of the mechanical constraints. Our results show the SSTP to vary with respect to both cadence and bodyweight. The ECF was lower in the standing position above the SSTP value (i.e., at 120%) in all experimental sessions. The minimal saddle forces varied significantly with respect to both cadence and bodyweight. These results suggest that optimization of the muscular cost function, rather than mechanical constraints, explain the seat-to-stand transition in cycling.

Cycling Biomechanics and Its Relationship to Performance

“…√ M 2 represents the Euclidian norm of the vector. Using this cost-function it has been possible to define the power-output at which the standing position should be chosen over the seated position [44,45] (i.e., when comparing the cost of the two techniques over a wide range of powers there is a clear point or a clear power-output, at which the standing position becomes the optimal position). Cost functions have been suggested to be useful to define individualized bike settings for cyclists or to monitor the effects of training, fatigue and rehabilitation, for example [46], but further investigations are needed.…”