Physical Training Outcome Predictions With Biomechanics, Part I: Army Physical Fitness Test Modeling

Objectives: The U.S. Army Basic Combat Training (BCT) is the first step in preparing soldier trainees for the physical demands of the military. Unfortunately, a substantial number of trainees fail BCT due to failure on the final Army Physical Fitness Test (also known as the "end of cycle" APFT). Current epidemiological studies have used statistics to identify several risk factors for poor APFT performance, but these studies have had limited utility for guiding regimen design to maximize APFT outcome. This is because such studies focus on intrinsic risks to APFT failure and do not utilize detailed BCT activity data to build models which offer guidance for optimizing the training regimen to improve graduation rates. Methods: In this study, a phenomenological run performance model that accounts for physiological changes in fitness and fatigue due to training was applied to recruits undergoing U.S. Army BCT using high resolution (minute-by-minute) activity data. Results: The phenomenological model was better at predicting both the final as well as intermediate APFTs (R-2 range = 0.55-0.59) compared to linear regression models (LRMs) that used the same intrinsic input variables (R-2 range = 0.36-0.50). Conclusions: Unlike a statistical approach, a phenomenological model accounts for physiological changes and, therefore, has the potential to not only identify trainees at risk of failing BCT on novel training regimens, but offer guidance to regimen planners on how to change the regimen for maximizing physical performance. This paper is Part I of a 2-part series on physical training outcome predictions.