The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique

Abstract

Anaerobic capacity is an important performance-determining variable of sprint cross-country skiing. Nevertheless, to date, no study has directly compared the anaerobic capacity, determined using the maximal accumulated oxygen deficit (MAOD) method and gross efficiency (GE) method, while using different skiing sub-techniques. Purpose: To compare the anaerobic capacity assessed using two different MAOD approaches (including and excluding a measured y-intercept) and the GE method during double poling (DP) and diagonal stride (DS) cross-country skiing. Methods: After an initial familiarization trial, 16 well-trained male cross-country skiers performed, in each sub-technique on separate occasions, a submaximal protocol consisting of eight 4-min bouts at intensities between ~47-78% of $\stackrel{.}{\text{V}}$ O_2peak followed by a 4-min roller-skiing time trial, with the order of sub-technique being randomized. Linear and polynomial speed-metabolic rate relationships were constructed for both sub-techniques, while using a measured y-intercept (8+Y _LIN and 8+Y_POL) or not (8-Y_LIN and 8-Y_POL), to determine the anaerobic capacity using the MAOD method. The average GE (GE_AVG) of all eight submaximal exercise bouts or the GE of the last submaximal exercise bout (GE_LAST) were used to calculate the anaerobic capacity using the GE method. Repeated measures ANOVA were used to test differences in anaerobic capacity between methods/approaches. Results: A significant interaction was found between computational method and skiing sub-technique (P < 0.001, η² = 0.51) for the anaerobic capacity estimates. The different methodologies resulted in significantly different anaerobic capacity values in DP (P < 0.001, η² = 0.74) and in DS (P = 0.016, η² = 0.27). The 8-Y_POL model resulted in the smallest standard error of the estimate (SEE, 0.24 W·kg^-1) of the MAOD methods in DP, while the 8-Y_LIN resulted in a smaller SEE value than the 8+Y _LIN model (0.17 vs. 0.33 W·kg^-1) in DS. The 8-Y_LIN and GE_LAST resulted in the closest agreement in anaerobic capacity values in DS (typical error 2.1 mL O₂eq·kg^-1). Conclusions: It is discouraged to use the same method to estimate the anaerobic capacity in DP and DS sub-techniques. In DP, a polynomial MAOD method (8-Y_POL) seems to be the preferred method, whereas the 8-Y_LIN, GE_AVG, and GE_LAST can all be used for DS, but not interchangeable, with GE_LAST being the least time-consuming method.

Keywords: MAOD; cross-country skiing; diagonal stride; double poling; gross efficiency; maximal accumulated oxygen deficit method; metabolic demand; time trial.

Figure 1

A schematic overview of the protocol used for both double poling (at a 1.5° treadmill incline) and diagonal stride (at a 6.5° treadmill incline). After a short rest, the subjects were fitted with the equipment for cardiopulmonary measurements at rest (baseline oxygen uptake [$\stackrel{.}{\text{V}}{\text{O}}_2$]). Capillary blood samples for the determination of blood lactate concentration were collected four times. Abbreviations: @, at; W-up, warm-up; Sub, submaximal; TT, time trial; $\stackrel{.}{\text{V}}$O_2peak, peak oxygen uptake.

Figure 2

(A) Ventilatory equivalents of oxygen ($\stackrel{.}{\text{VE}}·\stackrel{.}{\text{V}}{\text{O}}_2^{-1}$) and carbon dioxide ($\stackrel{.}{\text{VE}}·\stackrel{.}{\text{V}}{\text{CO}}_2^{-1}$) and (B) net efficiency (NE) and gross efficiency (GE) for the respective sub-techniques plotted against skiing speed and the average power output for the eight 4-min stages of submaximal treadmill roller-skiing (SUB₁₋₈) with diagonal stride (DS) and double poling (DP) at inclines of 6.5 and 1.5°, respectively. The values are presented as mean ± SD. Statistical comparisons were performed for GE and NE.^‡Statistically significantly different (SSD) from SUB₄₋₈. ^$SSD from SUB₅₋₈. ^†SSD from SUB₇₋₈. ^# SSD from SUB₈.

Figure 3

The various regression models between mean ± SD power output and metabolic rate relative to system mass of the skier during 8 × 4-min stages of continuous submaximal roller-skiing together with the estimated total metabolic requirements (open tilted squares) at the average power output (PO) attained during the 4-min time-trial (TT). (A) linear relationship for double poling (DP) at 1.5° using a Y-intercept (8+Y_LIN), dashed line, and excluding a Y-intercept value, solid line (8–Y_LIN); (B) the same data for DP as in (A) but using polynomial regressions; (C,D) diagonal stride (DS) roller-skiing at 6.5° using the same regression models as in (A,B). The dashed horizontal lines indicate the peak aerobic metabolic rate during the respective TTs.

Figure 4

Individual regressions for submaximal metabolic rate plotted against treadmill roller-skiing speed using the double poling (DP) at 1.5° and diagonal stride (DS) at 6.5°. 8+Y_LIN (A,E) and 8-Y_LIN (B,F), the 8 × 4-min linear regressions with the baseline metabolic rate as a Y-intercept either included (8+Y) or excluded (8–Y). 8+Y_POL (C,G) and 8–Y_POL (D,H), the 8 × 4-min polynomial (second degree) regressions with the baseline metabolic rate as a Y-intercept either included (8+Y) or excluded (8–Y).

Figure 5

(A,C) Average gross efficiency calculated from the four regression equations (GE_REG) based on submaximal power output and metabolic rate for the double poling (DP) and diagonal stride (DS) sub-techniques plotted against treadmill roller-skiing speed and the average power output. (B,D) The average percentage point difference (PP_DIFF) between GE_REG and the measured gross efficiency (GE) for the same sub-techniques and speeds. Where 8+Y_LIN and 8–Y_LIN are the 8 × 4-min linear regressions with the baseline metabolic rate as a Y-intercept either included (8+Y) or excluded (8–Y), while 8+Y_POL and 8–Y_POL are second-degree polynomial relationships based on the same data points. The gray horizontal line represents the identity line between GE_REG and GE.

Figure 6

Mean accumulated oxygen (ΣO₂) deficits and 95% confidence interval together with individual data (colored dots) determined during a 4-min roller-skiing time trial using (A) the double poling (DP) sub-technique at 1.5° and (B) the diagonal stride (DS) sub-technique at 6.5° using six different methods of calculation. 8+Y_LIN and 8-Y_LIN, the 8 × 4-linear methods with the baseline metabolic rate as a Y-intercept either included (8+Y) or excluded (8-Y); 8+Y_POL and 8-Y_POL, the 8 × 4-min polynomial methods with the baseline metabolic rate as a Y-intercept either included (8+Y) or excluded (8-Y); GE_AVG, the gross efficiency method based on the average of eight submaximal stages; GE_LAST, the gross efficiency method based on the last submaximal stage. The letters (^b−f) indicate statistically significant differences (SSD, P < 0.05) between the six methods of calculation: ^b = SSD from 8-Y_LIN, ^c = SSD from 8+Y_POL, ^d = SSD from 8-Y_POL, ^e = SSD from GE_AVG, ^f = SSD from GE_LAST.

Figure 7

Bland-Altman plots for the six various models of estimating the accumulated oxygen deficit (AOD) associated with the 4-min time trial using the double poling sub-technique (A–O). Bland-Altman plots represent the mean difference (MEAN_DIFF) in the AOD ± 95% (1.96 SD) limits of agreement between the methods. Abbreviations: AOD_DIFF, the difference in AOD; TE, typical error; ES, Hedges's g_av effect size, 8+Y_LIN and 8-Y_LIN, the 8 × 4-min linear maximal accumulated O₂ deficit methods with the baseline $\stackrel{.}{\text{V}}{\text{O}}_2$ as a Y-intercept either included (8+Y) or excluded (8-Y); 8+Y_POL and 8-Y_POL, the 8 × 4-min polynomial (second degree) maximal accumulated O2 deficit methods with the baseline $\stackrel{.}{\text{V}}{\text{O}}_2$ as a Y-intercept either included (8+Y) or excluded (8-Y); GE_AVG, the gross efficiency method based on the average of eight submaximal stages; GE_LAST, the gross efficiency method based on the last submaximal stage.

Figure 8

Bland-Altman plots for the six various models of estimating the accumulated oxygen deficit (AOD) associated with the 4-min time trial using the diagonal stride sub-technique (A–O). Bland-Altman plots represent the mean difference (MEAN_DIFF) in the AOD ± 95% (1.96 SD) limits of agreement between the methods. Abbreviations: AOD_DIFF, the difference in AOD; TE, typical error; ES, Hedges's g_av effect size, 8+Y_LIN and 8-Y_LIN, the 8 × 4-min linear maximal accumulated O₂ deficit methods with the baseline $\stackrel{.}{\text{V}}{\text{O}}_2$ as a Y-intercept either included (8+Y) or excluded (8-Y); 8+Y_POL and 8-Y_POL, the 8 × 4-min polynomial (second degree) maximal accumulated O2 deficit methods with the baseline $\stackrel{.}{\text{V}}{\text{O}}_2$ as a Y-intercept either included (8+Y) or excluded (8-Y); GE_AVG, the gross efficiency method based on the average of eight submaximal stages; GE_LAST, the gross efficiency method based on the last submaximal stage.

Figure 9

Scatter plots between the Y-intercept values for the 8 × 4-linear methods with the baseline metabolic rate (MR) as a Y-intercept either included (8+Y_LIN) or excluded (8-Y_LIN) in the model (x-axis) and the accumulated oxygen deficit difference (ΣO₂ deficit diff.) vs. the gross efficiency method based on the average of eight submaximal stages (GE_AVG) (y-axis). (A,B) show the results for double poling (DP) and (C,D) show results for diagonal stride (DS).