Aerobic power and lean mass are indicators of competitive sprint performance among elite female cross-country skiers

Abstract

The purpose of this study was to establish the optimal allometric models to predict International Ski Federation’s ski-ranking points for sprint competitions (FISsprint) among elite female cross-country skiers based on maximal oxygen uptake ( normalV˙O2max) and lean mass (LM). Ten elite female cross-country skiers (age: 24.5±2.8 years [mean ± SD]) completed a treadmill roller-skiing test to determine normalV˙O2max (ie, aerobic power) using the diagonal stride technique, whereas LM (ie, a surrogate indicator of anaerobic capacity) was determined by dual-emission X-ray anthropometry. The subjects’ FISsprint were used as competitive performance measures. Power function modeling was used to predict the skiers’ FISsprint based on normalV˙O2max, LM, and body mass. The subjects’ test and performance data were as follows: normalV˙O2max, 4.0±0.3 L min−1; LM, 48.9±4.4 kg; body mass, 64.0±5.2 kg; and FISsprint, 116.4±59.6 points. The following power function models were established for the prediction of FISsprint: 3.91×105⋅normalV˙O2max−6.00 and 6.95 × 1010 · LM−5.25; these models explained 66% (P=0.0043) and 52% (P=0.019), respectively, of the variance in the FISsprint. Body mass failed to contribute to both models; hence, the models are based on normalV˙O2max and LM expressed absolutely. The results demonstrate that the physiological variables that reflect aerobic power and anaerobic capacity are important indicators of competitive sprint performance among elite female skiers. To accurately indicate performance capability among elite female skiers, the presented power function models should be used. Skiers whose normalV˙O2max differs by 1% will differ in their FISsprint by 5.8%, whereas the corresponding 1% difference in LM is related to an FISsprint difference of 5.1%, where both differences are in favor of the skier with higher normalV˙O2max or LM. It is recommended that coaches use the absolute expression of these variables to monitor skiers’ performance-related training adaptations linked to changes in aerobic power and anaerobic capacity.

Anthropometric, Physiological, and Performance Developments in Cross-country Skiers

“…By contrast, the assessed variables could not predict distance or sprint points for the men, or sprint points for the women. This observation is contrary to a large body of previous work suggesting that distance and sprint XC skiing performance can be predicted from a range of laboratory-assessed anthropometric and physiological characteristics (3,9,10,(12)(13)(14)(15). The discrepancy in results is likely to be predominantly due to the MVDA methods (PCA and OPLS) employed in the present study, which differ from the simpler correlational and multiple linear regression analyses employed previously.…”

“…In terms of physiological developments, increases in V ̇O2peak (mL•kg -1 •min -1 ), TT relative PO and V ̇O2 and speed at a BLa of 2 and 4 mmol•L -1 were the most important changes (i.e., a VIP > 1) for predicting improvements in FIS distance points in women. These findings are supported by previous research showing aerobic capacity (3,12,13,17), speed at a BLa of 4 mmol•L -1 (3) and roller-ski TT performance (15) to correlate with competitive race performance. In the present study, GE was identified as being unimportant for predicting best or change in FIS distance points in female skiers (both VIP < 0.5 with large uncertainty), which is in contrast to previous research indicating that GE is an important predictor of performance in XC skiing (1,14,16).…”

“…Correlational and multiple linear regression statistical approaches have previously been used to predict FIS points (i.e., competitive performance) in XC skiers from physical and physiological qualities assessed in laboratory environments (3,9,10,(12)(13)(14)(15). These analyses have identified numerous variables that correlate with FIS points, including lean body mass (10,12,13), the speed at a blood lactate concentration (BLa) of 4 mmol•L -1 (3), gross efficiency (GE) (1,14,16), peak oxygen uptake (V ̇O2peak) (3,12,13,17) and roller-ski time-trial (TT) performance (15). Furthermore, multiple linear regression analyses have identified that successful performance in XC skiing is likely influenced by a combination of these factors, rather than by any one factor in isolation (9,12,15,18).…”

Uphill or Downhill Cross-Country Skiing Presentation of an Easy Applicable Performance Protocol for Youngsters

“…Reasons for these results can be detected in the high lactate concentrations (in some athletes up to 15 mmol/l after ascent, reducing downhill skiing abilities while descent implying that in the break between ascent and descent a complete reduction into lactate rest states of 2 mmol/l was not possible. Furthermore, the finding that heavier cross-country skiers were faster in downhill can be well connected with newer findings, whereby sprint cross-country skiers were heavier than on long courses (10,15,30,50 km races) [16,32]. Muscular usage is different (force versus endurance) and different requirements on organ systems evoke consequently.…”

“…Hereby it is to mention that body weight is not a sharp parameter and should not be used solely e.g. for measurements of lean mass seems to be as parameter for performance diagnostic more accurate [32]. Concerning rate of perceived exertion it can be mentioned, that the correlative relationship between heart rate and BORG-Scale (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) in ascent was 0.51 war and was reduced to 0.02 while descent.…”