How to Assess Performance in Cycling: the Multivariate Nature of Influencing Factors and Related Indicators.

• Main Authors: Anna Margherita eCastronovo, Silvia eConforto, Maurizio eSchmid, Daniele eBibbo, Tommaso eD'Alessio
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2013-05-01
• Series: Frontiers in Physiology
• Subjects: Efficiency; Fatigue; Cycling; effectiveness; ergogenic aids; Performance monitoring
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fphys.2013.00116/full

Finding an optimum for the cycling performance is not a trivial matter, since the literature shows the presence of many controversial aspects. In order to quantify different levels of performance, several indexes have been defined and used in many studies, reflecting variations in physiological and biomechanical factors. In particular, indexes such as Gross Efficiency (GE), Net Efficiency (NE) and Delta Efficiency (DE) have been referred to changes in metabolic efficiency (EffMet), while the Indexes of Effectiveness (IE), defined over the complete crank revolution or over part of it, have been referred to variations in mechanical effectiveness (EffMech). All these indicators quantify the variations of different factors (i.e. muscle fibers type distribution, pedaling cadence, setup of the bicycle frame, muscular fatigue, environmental variables, ergogenic aids, psychological traits), which, moreover, show high mutual correlation. In the attempt of assessing cycling performance, most studies in the literature keep all these factors separated. This may bring to misleading results, leaving unanswered the question of how to improve cycling performance. This work provides an overview on the studies involving indexes and factors usually related to performance monitoring and assessment in cycling. In particular, in order to clarify all those aspects, the mutual interactions among these factors are highlighted, in view of a global performance assessment. Moreover, a proposal is presented advocating for a model-based approach that considers all factors mentioned in the survey, including the mutual interaction effects, for the definition of an objective function E representing the overall effectiveness of a training program in terms of both metabolic efficiency and mechanical effectiveness.