Turner Jeffrey, Wagner Torrey, Langhals Brent
Data Analytics Certificate Program, Graduate School of Engineering and Management, Air Force Institute of Technology, Wright-Patterson AFB, OH 45433, USA.
The Perduco Group (a LinQuest Company), Dayton, OH 45433, USA.
Sports (Basel). 2022 Apr 6;10(4):54. doi: 10.3390/sports10040054.
Physical fitness is a pillar of U.S. Air Force (USAF) readiness and ensures that Airmen can fulfill their assigned mission and be fit to deploy in any environment. The USAF assesses the fitness of service members on a periodic basis, and discharge can result from failed assessments. In this study, a 21-feature dataset was analyzed related to 223 active-duty Airmen who participated in a comprehensive mental and social health survey, body composition assessment, and physical performance battery. Graphical analysis revealed pass/fail trends related to body composition and obesity. Logistic regression and limited-capacity neural network algorithms were then applied to predict fitness test performance using these biomechanical and psychological variables. The logistic regression model achieved a high level of significance (p < 0.01) with an accuracy of 0.84 and AUC of 0.89 on the holdout dataset. This model yielded important inferences that Airmen with poor sleep quality, recent history of an injury, higher BMI, and low fitness satisfaction tend to be at greater risk for fitness test failure. The neural network model demonstrated the best performance with 0.93 accuracy and 0.97 AUC on the holdout dataset. This study is the first application of psychological features and neural networks to predict fitness test performance and obtained higher predictive accuracy than prior work. Accurate prediction of Airmen at risk of failing the USAF fitness test can enable early intervention and prevent workplace injury, absenteeism, inability to deploy, and attrition.
体能是美国空军战备的支柱，可确保飞行员能够完成其指定任务，并适合在任何环境中部署。美国空军定期评估军人的体能，评估未通过可能导致被开除。在本研究中，分析了一个包含21个特征的数据集，该数据集与223名现役飞行员有关，他们参与了一项全面的心理和社会健康调查、身体成分评估以及体能测试。图形分析揭示了与身体成分和肥胖相关的通过/未通过趋势。然后应用逻辑回归和有限容量神经网络算法，使用这些生物力学和心理变量来预测体能测试表现。逻辑回归模型在保留数据集上达到了高度显著性（p < 0.01），准确率为0.84，AUC为0.89。该模型得出了重要推论，即睡眠质量差、近期有受伤史、BMI较高且体能满意度较低的飞行员在体能测试中失败的风险往往更大。神经网络模型在保留数据集上表现最佳，准确率为0.93，AUC为0.97。本研究首次应用心理特征和神经网络来预测体能测试表现，并且获得了比之前工作更高的预测准确率。准确预测有美国空军体能测试失败风险的飞行员，能够实现早期干预，并预防工作场所受伤、旷工、无法部署和人员流失。
