Physical characteristics of the participants.

We previously were able to predict the anaerobic mechanical power outputs using features taken from a maximal incremental cardiopulmonary exercise stress test (CPET). Since a standard aerobic exercise stress test (with electrocardiogram and blood pressure measurements) has no gas exchange measurement and is more popular than CPET, our goal, in the current paper, was to investigate whether features taken from a clinical exercise stress test (GXT), either submaximal or maximal, can predict the anaerobic mechanical power outputs to the same level as we found with CPET variables. We have used data taken from young healthy subjects undergoing CPET aerobic test and the Wingate anaerobic test, and developed a computational predictive algorithm, based on greedy heuristic multiple linear regression, which enabled the prediction of the anaerobic mechanical power outputs from a corresponding GXT measures (exercise test time, treadmill speed and slope). We found that for submaximal GXT of 85% age predicted HRmax, a combination of 3 and 4 variables produced a correlation of r = 0.93 and r = 0.92 with % error equal to 15 ± 3 and 16 ± 3 on the validation set between real and predicted values of the peak and mean anaerobic mechanical power outputs (p < 0.001), respectively. For maximal GXT (100% of age predicted HRmax), a combination of 4 and 2 variables produced a correlation of r = 0.92 and r = 0.94 with % error equal to 12 ± 2 and 14 ± 3 on the validation set between real and predicted values of the peak and mean anaerobic mechanical power outputs (p < 0.001), respectively. The newly developed model allows to accurately predict the anaerobic mechanical power outputs from a standard, submaximal and maximal GXT. Nevertheless, in the current study the subjects were healthy, normal individuals and therefore the assessment of additional subjects is desirable for the development of a test applicable to other populations.

此前本团队已借助极量递增心肺运动负荷试验（CPET）提取的特征，实现无氧机械功率输出的预测。由于标准有氧运动负荷试验（搭配心电图与血压测量）无需开展气体交换检测，且应用场景较CPET更为广泛，因此本文旨在探究：从临床运动负荷试验（GXT，涵盖次极量与极量模式）中提取的特征，能否达到与CPET变量相当的预测精度，实现无氧机械功率输出的精准预测。本研究纳入接受CPET有氧运动测试与温盖特无氧试验（Wingate anaerobic test）的健康青年受试者数据，基于贪婪启发式多元线性回归构建计算预测算法，可通过对应GXT检测指标（运动时长、跑台速度与坡度）预测无氧机械功率输出。研究结果显示：针对采用85%年龄预测最大心率（HRmax）的次极量GXT，选取3个与4个变量组合时，验证集上峰值与平均无氧机械功率输出的实测值与预测值的相关系数分别为r=0.93与r=0.92，百分比误差分别为15±3与16±3（p<0.001）；针对采用100%年龄预测最大心率（HRmax）的极量GXT，选取4个与2个变量组合时，验证集上峰值与平均无氧机械功率输出的实测值与预测值的相关系数分别为r=0.92与r=0.94，百分比误差分别为12±2与14±3（p<0.001）。本研究新建的模型可通过标准次极量、极量GXT准确预测无氧机械功率输出。但需注意，本研究受试者均为健康人群，因此未来需纳入更多不同人群的受试者，以开发适用于更广泛群体的检测方法。