Statistical outcome of 2x2 RM ANOVA: Bayesian estimated mean (95% CI) of the differences (8%—5% and 12–10 k h^-1), BF_inclusion, classical F-values (η²) and p-values.

Statistical outcome of 2x2 RM ANOVA: Bayesian estimated mean (95% CI) of the differences (8%—5% and 12–10 k h-1), BFinclusion, classical F-values (η2) and p-values.</p

Mean, SD and individual values of overlap of power distribution for the three techniques.

Differences between maximal and minimal power for ‘adjacent’ techniques are shown (irrespective of which session these were obtained).</p

S1 Data -

In cross-country skiing, athletes use different techniques akin to locomotor gaits such as walking and running. Transitions between these techniques generally depend on speed and incline, in a similar way as walk-run transitions. Previous studies have examined the roles of incline, speed, and mechanical power demand in triggering transitions. However, it is still not known if mechanical power demand, as an isolated factor, has any role on the choice of technique. The aim of this study was to examine the isolated role of mechanical power on the choice of technique during classic cross-country roller skiing by changing mechanical power demand at fixed speeds and inclines. Six male and eight female athletes performed classical roller skiing on a treadmill at the four combinations of two speeds (10 and 12 km h-1) and two inclines (5 and 8%) while additional resistive forces were applied via a weight-pulley system. Athletes were free to choose between three techniques: double poling, double poling with kick, and diagonal stride. Power and resistive forces at transition were compared using repeated measure (2x2) ANOVA. At a given incline, technique transitions occurred at similar additional resistive force magnitudes at the two speeds. On the steeper incline, the transitions occurred at smaller additional resistive forces. Importantly, transitions were not triggered at similar mechanical power demands across the different incline/speed/resistive force conditions. This suggests that mechanical power itself is not a key technique transition trigger. Both total and additional resistive force (i.e., the manipulated mechanism to regulate power) may be transition triggers when incline is fixed and speed is changed. In combination with previous findings, the current results suggest that no single factor triggers technique transitions in classic cross-country skiing.</div

Equipment setup, study design, and original time traces (cycle rate, power and additional resistive force) during one session.

A. Athlete on treadmill with hanging resistance weight. B. Timeline of protocol showing resistance weights. C. Cycle rate and power time trace (at v = 10 k h-1, 8% incline). The techniques are indicated by colour. Bottom diagram shows the raw resistive force data (also in zoomed range) and smoothed over a 2 s window.</p

Fig 4 -

Mean, SD and individual values of (A) mechanical power, (B) additional resistive force and (C) total resistive force at technique transitions. Grey connecting lines indicate individual data from the same athlete. For reasons of clarity, these are only shown between different inclines at the same speed.</p

Mean, SD and individual values of relative time that the different techiques were applied during the four protocols (2 speeds x 2 inclines).

Color signature for techniques is the same as in Fig 1. The individual values often range from 0 and 1.</p

Mean, SD and individual values of mechanical power difference for technique transitions when increasing and decreasing resistive force.

The data are the means over the four protocol sessions per athlete.</p

Curve fittings for the mean data for each step over all athletes.

<p>Only steps with a complete data set for the variable at hand are shown and were used for the fitting procedures. Dotted line: exponential; Solid lines: piecewise linear. Horizontal (top diagram only) and vertical bars are standard deviation (n = 24). Note that the seemingly very low standard deviation, especially for horizontal velocity, is only partly due to the homogeneous group and mainly due to scaling of the diagram, which covers low velocity at the first steps to almost maximal sprinting velocity. Time = 0 is the time of the first movement of CoM during the sprint start.</p

Example of CoM’s vertical position and curve fitting from step to step in time.

<p>Open markers are data, solid marker indicates the breakpoint according to Nagahara et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159701#pone.0159701.ref010" target="_blank">10</a>] and piecewise twice linear fit (solid lines, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159701#pone.0159701.e005" target="_blank">Eq 5a</a>). Vertical arrow indicates the breakpoint according to linear-exponential piecewise fit (grey curves, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159701#pone.0159701.e006" target="_blank">Eq 5b</a>). Dotted line is exponential fit (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159701#pone.0159701.e004" target="_blank">Eq 4</a>).</p

Schematic drawing of a position during accelerated running.

<p>The variables described in eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159701#pone.0159701.e001" target="_blank">1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159701#pone.0159701.e003" target="_blank">3</a> and their interrelationships are graphically presented. Accelerations <i>g</i> and <i>a</i>_<i>hCoM</i> are defined as by di Prampero et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159701#pone.0159701.ref006" target="_blank">6</a>]. Solid circle is CoM.</p