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血糖最小值可预测跑步时的最大乳酸稳态。

Blood glucose minimum predicts maximal lactate steady state on running.

作者信息

Sotero R C, Pardono E, Landwehr R, Campbell C S G, Simoes H G

机构信息

Department of Physical Education, Catholic University of Brasilia, Distrito Federal, Brazil.

出版信息

Int J Sports Med. 2009 Sep;30(9):643-6. doi: 10.1055/s-0029-1220729. Epub 2009 Jun 30.

Abstract

This study analyzed if the running speed corresponding to glucose minimum (GM) could predict the maximal lactate steady state (MLSS). Thirteen physically active men (25.2+/-4.2 years, 73.4+/-8.0 kg, 180.0+/-1.0 cm) completed three running tests on different days: 1) a 1 600-m time trial to calculate the average speed; 2) after 10-min of recovery from a 150-m sprint to elevate [lac], participants performed 6 series of 800-m respectively at 78, 81, 84, 87, 90 and 93% of the 1 600-m speed to identify the lactate minimum (LM) and GM speeds and 3) 2-4 constant intensity exercise sessions for the MLSS. Repeated measures ANOVA showed no differences between running speeds associated to the GM (201.7+/-23.8 m.min (-1)), LM (200.0+/-23.9 m.min (-1)) and MLSS (201.5+/-23.1 m.min (-1)), with high correlation between GM vs. LM (r=0.984), GM vs. MLSS (r=0.947) and LM vs. MLSS (r=0.961) (P<0.01). Bland and Altman plots showed good agreement [Bias (+/-95% CI)] for MLSS and GM [0.2(15.3) m.min (-1)], MLSS and LM [-1.4(13.2) m.min (-1)], as well as for LM and GM [1.7(8.5) m.min (-1)]. These running speeds occurred at approximately 84.4% of 1 600-m speed, which would have practical applications for exercise prescription. We concluded that GM running speed is a good predictor of the MLSS for physically active individuals.

摘要

本研究分析了对应于最低血糖(GM)的跑步速度是否能够预测最大乳酸稳态(MLSS)。13名身体活跃的男性(25.2±4.2岁,73.4±8.0千克,180.0±1.0厘米)在不同日期完成了三项跑步测试:1)一次1600米计时赛以计算平均速度;2)在从150米冲刺恢复10分钟以升高[乳酸]后,参与者分别以1600米速度的78%、81%、84%、87%、90%和93%进行6组800米跑,以确定最低乳酸(LM)和GM速度;3)进行2 - 4次恒定强度运动测试以确定MLSS。重复测量方差分析显示,与GM(201.7±23.8米·分钟⁻¹)、LM(200.0±23.9米·分钟⁻¹)和MLSS(201.5±23.1米·分钟⁻¹)相关的跑步速度之间无差异,GM与LM(r = 0.984)、GM与MLSS(r = 0.947)以及LM与MLSS(r = 0.961)之间具有高度相关性(P<0.01)。Bland和Altman图显示MLSS与GM [0.2(15.3)米·分钟⁻¹]、MLSS与LM [-1.4(13.2)米·分钟⁻¹]以及LM与GM [1.7(8.5)米·分钟⁻¹]之间具有良好的一致性[偏差(±95%CI)]。这些跑步速度约为1600米速度的84.4%,这对于运动处方具有实际应用价值。我们得出结论,对于身体活跃的个体,GM跑步速度是MLSS的良好预测指标。

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