Andersson E, Björklund G, Holmberg H-C, Ørtenblad N
Department of Health Sciences, Swedish Winter Sports Research Centre, Mid Sweden University, Östersund, Sweden.
Swedish Olympic Committee, Stockholm, Sweden.
Scand J Med Sci Sports. 2017 Apr;27(4):385-398. doi: 10.1111/sms.12666. Epub 2016 Feb 29.
To improve current understanding of energy contributions and determinants of sprint-skiing performance, 11 well-trained male cross-country skiers were tested in the laboratory for VO , submaximal gross efficiency (GE), maximal roller skiing velocity, and sprint time-trial (STT) performance. The STT was repeated four times on a 1300-m simulated sprint course including three flat (1°) double poling (DP) sections interspersed with two uphill (7°) diagonal stride (DS) sections. Treadmill velocity and VO were monitored continuously during the four STTs and data were averaged. Supramaximal GE during the STT was predicted from the submaximal relationships for GE against velocity and incline, allowing computation of metabolic rate and O deficit. The skiers completed the STT in 232 ± 10 s (distributed as 55 ± 3% DP and 45 ± 3% DS) with a mean power output of 324 ± 26 W. The anaerobic energy contribution was 18 ± 5%, with an accumulated O deficit of 45 ± 13 mL/kg. Block-wise multiple regression revealed that VO , O deficit, and GE explained 30%, 15%, and 53% of the variance in STT time, respectively (all P < 0.05). This novel GE-based method of estimating the O deficit in simulated sprint-skiing has demonstrated an anaerobic energy contribution of 18%, with GE being the strongest predictor of performance.
为了增进对短距离滑雪运动中能量贡献及成绩决定因素的现有理解,11名训练有素的男性越野滑雪运动员在实验室接受了VO₂、次最大总效率(GE)、最大轮滑速度和短距离计时赛(STT)成绩测试。在一条1300米的模拟短距离赛道上对STT进行了4次重复测试,该赛道包括3个平坦(1°)的双杖(DP)路段,其间穿插2个上坡(7°)的斜线滑行(DS)路段。在4次STT过程中持续监测跑步机速度和VO₂,并对数据进行平均。根据GE与速度和坡度的次最大关系预测STT期间的超最大GE,从而计算代谢率和氧亏。滑雪运动员完成STT的时间为232 ± 10秒(DP占55 ± 3%,DS占45 ± 3%),平均功率输出为324 ± 26瓦。无氧能量贡献为18 ± 5%,累积氧亏为45 ± 13毫升/千克。逐块多元回归分析表明,VO₂、氧亏和GE分别解释了STT时间变异的30%、15%和53%(所有P < 0.05)。这种基于GE的新型模拟短距离滑雪运动中氧亏估算方法显示无氧能量贡献为18%,其中GE是成绩的最强预测指标。