Ludwig D A, Convertino V A, Goldwater D J, Sandler H
Department of Mathematics, University of North Carolina, Greensboro.
Aviat Space Environ Med. 1987 Nov;58(11):1057-61.
Small sample size (n less than 10) and inappropriate analysis of multivariate data have hindered previous attempts to describe which physiologic and demographic variables are most important in determining how long humans can tolerate acceleration. Data from previous centrifuge studies conducted at NASA/Ames Research Center, utilizing a 7-14 d bed rest protocol to simulate weightlessness, were included in the current investigation. After review, data on 25 women and 22 men were available for analysis. Study variables included gender, age, weight, height, percent body fat, resting heart rate, mean arterial pressure, VO2max, and plasma volume. Since the dependent variable was time to greyout (failure), two contemporary biostatistical modeling procedures (proportional hazard and logistic discriminant function) were used to estimate risk, given a particular subject's profile. After adjusting for pre-bed-rest tolerance time, none of the profile variables remained in the risk equation for post-bed-rest tolerance greyout. However, prior to bed rest, risk of greyout could be predicted with 91% accuracy. All of the profile variables except weight, MAP, and those related to inherent aerobic capacity (VO2max, percent body fat, resting heart rate) entered the risk equation for pre-bed-rest greyout. A cross-validation using 24 new subjects indicated a very stable model for risk prediction, accurate within 5% of the original equation. The result for the inherent fitness variables is significant in that a consensus as to whether an increased aerobic capacity is beneficial or detrimental has not been satisfactorily established. We conclude that tolerance to +Gz acceleration before and after simulated weightlessness is independent of inherent aerobic fitness.
小样本量(n小于10)以及对多变量数据的不恰当分析,阻碍了以往描述哪些生理和人口统计学变量在确定人类能耐受加速度的时长方面最为重要的尝试。美国国家航空航天局艾姆斯研究中心此前进行的离心机研究数据被纳入了当前调查,这些研究采用7 - 14天卧床休息方案来模拟失重状态。经过审查,有25名女性和22名男性的数据可供分析。研究变量包括性别、年龄、体重、身高、体脂百分比、静息心率、平均动脉压、最大摄氧量和血浆量。由于因变量是到出现灰视(衰竭)的时间,所以使用了两种当代生物统计学建模程序(比例风险模型和逻辑判别函数)来根据特定受试者的特征估计风险。在对卧床休息前的耐受时间进行调整后,没有任何一个特征变量保留在卧床休息后耐受灰视的风险方程中。然而,在卧床休息前,灰视风险的预测准确率可达91%。除体重、平均动脉压以及与固有有氧能力相关的变量(最大摄氧量、体脂百分比、静息心率)外,所有特征变量都进入了卧床休息前灰视的风险方程。使用24名新受试者进行的交叉验证表明,风险预测模型非常稳定,预测结果与原始方程的误差在5%以内。固有健康变量的结果具有重要意义,因为关于有氧能力增加是有益还是有害尚未达成令人满意的共识。我们得出结论,模拟失重前后对+Gz加速度的耐受性与固有有氧健康状况无关。