The effective execution of physical tasks requires adequate NMF. Tasks can range from those performed as part of daily life to those required in the sporting arena. Stationary cycle ergometry is an ecologically valid, safe, multi-joint movement that can be performed by a wide range of populations, making it a useful tool for the assessment of the lower limb muscles. Force-velocity (F-V) tests performed on a stationary cycle ergometer are commonly used to measure the maximal levels of torque and power that an individual can produce at a given cadence (i.e. torque vs cadence (T-C) and power vs cadence (P-C) relationships), enabling assessment of their functional capacity (Arsac et al., 1996; Dorel et al., 2005; Samozino et al., 2007; Yeo et al., 2015). Variables commonly estimated from the F-V test including maximal power (Pmax), optimal cadence (Copt), maximal torque (T0) and maximal cadence (C0) allow the limits of NMF to be estimated (Dorel et al., 2005; Driss et al., 2002; Gardner et al., 2007; Martin et al., 1997; McCartney et al., 1985; Samozino et al., 2012). However, the type of ergometer used (i.e. isoinertial vs isokinetic), F-V test protocol employed (i.e. single vs multiple sprints) and approach for modelling the experimental values of torque and power varies in the literature, with no previous studies investigating the best method for the assessment of the limits NMF (Arsac et al., 1996; Dorel et al., 2005; Driss et al., 2002; Gardner et al., 2007; Hautier et al., 1996; Martin et al., 1997; McCartney et al., 1985; Samozino et al., 2007; Sargeant et al., 1981; Yeo et al., 2015).
Unlike the assessment of force and velocity in single muscle fibers, the level of force and power produced on a stationary cycle ergometer can be affected by many factors other than cadence. The pedalling movement is complex, requiring the coordination of a large number of muscles for the effective transmission of force/power to the crank (Raasch et al., 1997; Zajac, 2002). However, as shown previously, regardless of an individual’s experience with cycling, they may be unable to produce maximal levels of force/power during every pedal cycle within a test protocol (Arsac et al., 1996; Samozino et al., 2007). Indeed, humans may not maximally and optimally activate and coordinate their lower limb muscles every time a movement is executed (i.e. every revolution of a pedal cycle) (Allen et al., 1995). Based upon previous findings, if the main the muscles used in the pedaling movement are not maximally and optimally activated, power production may suffer (van Ingen Schenau, 1989; Zajac et al., 2002). Further, due to the complexity of the pedaling movement, there is an abundance of solutions offered by the human body (Bernstein, 1967; Latash, 2012) which may cause variability in the recruitment and coordination of the lower limb muscles. If these patterns deviate too far from optimal power
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production could be negatively impacted. These deviations may be even greater for those who are novice cyclists, with those unskilled in the pedalling movement exhibiting muscle recruitment strategies that are less refined (Chapman et al., 2006; Hug et al., 2008; Muller & Sternad, 2009). In the current literature, it is unclear if researchers take the points listed above into consideration when designing their methodologies, whereby a method chosen allows the true maximal ability of an individual to produce torque and power over a range of cadences is exhibited. Also, if some form of data filtering or selection takes place it does not get stated. Test protocols that include multiple sprints may allow for the collection of several data points for a given cadence (Arsac et al., 1996; Dorel et al., 2010; Dorel et al., 2005), increasing an individual’s chance of producing maximal levels of torque and power, unlike the use of a single maximal effort (Martin et al., 1997).
A review of literature reveals that there is a lack of consensus regarding the shapes of T- C and P-C relationships. As two methods have been used previously to predict the shapes of these relationships, it is unclear which method provides the best fit for experimental data points collected. In the majority of studies, the T-C relationship is fit with a linear regression, relying on the assumption that the relationship is linear, while the P-C relationship appears as a symmetrical parabola and as such is typically fit with a second order polynomial (i.e. a quadratic) (Dorel et al., 2010; Dorel et al., 2005; Gardner et al., 2007; Hintzy et al., 1999; Martin et al., 1997; McCartney et al., 1985; Samozino et al., 2007). Although, in a few studies (Arsac et al., 1996; Hautier et al., 1996; Yeo et al., 2015), the methods used to fit the experimental data points (i.e. second order polynomials for T-C and third order polynomials for P-C) relied on the notion that the T-C relationship might not be perfectly linear while the P-C relationship might be an asymmetrical parabola. Bobbert (2012) reported that the shape of the force vs velocity relationships obtained from a F-V test during maximal leg press exercise was not perfectly linear. The author observed slight curvatures of the external force vs velocity relationship that could have been attributed to the reduced ability of individuals to control the external force at high speeds. Considering that maximal leg cycling exercise is a multi-joint movement requiring a higher level of external force control (compared to maximal leg press exercise), it seemed necessary to consider the possibility that the shapes of T-C and P-C relationships obtained using F-V tests performed on a stationary cycle ergometer might be more complex than previously assumed in a large number of studies (Dorel et al., 2010; Dorel et al., 2005; Gardner et al., 2007; Hintzy et al., 1999; Martin et al., 1997; McCartney et al., 1985; Samozino et al., 2007). A variety of physiological mechanisms identified during other forms of maximal intensity exercise could potentially limit the production of torque and power when cycling at low (Babault et al., 2002; Perrine & Edgerton, 1978; Robbins, 2005; Westing et al., 1991; Yamauchi et al., 2007) and high cadences (McDaniel et al., 2014; van Soest & Casius, 2000). Upon consideration of the potential roles played by these various factors on the
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ability to produce torque and power, it is of importance to investigate and define the best way to describe T-C and P-C relationships obtained from F-V tests performed on a stationary cycle ergometer.
The first aim of this study was to measure variations in torque and EMG profiles between maximal and non-maximal pedal cycles during a F-V test performed on a stationary cycle ergometer. It was assumed that higher levels of torque and power would be calculated from maximal pedal cycles associated with higher levels of peak EMG and co-activation and less variable EMG profiles of the lower limb muscles. The second aim of this study was to compare the ability of two modelling procedures previously used in the literature to describe the shapes of T-C (i.e. linear regressions vs second order polynomial) and P-C (i.e. second order polynomials vs third order polynomials) relationships and quantify the limits of NMF. It was assumed that higher order polynomials would provide a better fit for the experimental data points, leading to more accurate description of T-C and P-C relationships as well as key variables (i.e. Pmax, Copt, C0, T0) calculated to define the limits of NMF.
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