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INTRODUCTION: Personnel engaged in high-stakes occupations, such as military personnel, law enforcement, and emergency first responders, must sustain performance through a range of environmental stressors. To maximize the effectiveness of military personnel, an a priori understanding of traits can help predict their physical and cognitive performance under stress and adversity. This work developed and assessed a suite of measures that have the potential to predict performance during operational scenarios. These measures were designed to characterize four specific trait-based domains: cognitive, health, physical, and social-emotional. MATERIALS AND METHODS: One hundred and ninety-one active duty U.S. Army soldiers completed interleaved questionnaire-based, seated task-based, and physical task-based measures over a period of 3-5 days. Redundancy analysis, dimensionality reduction, and network analyses revealed several patterns of interest. RESULTS: First, unique variable analysis revealed a minimally redundant battery of instruments. Second, principal component analysis showed that metrics tended to cluster together in three to five components within each domain. Finally, analyses of cross-domain associations using network analysis illustrated that cognitive, health, physical, and social-emotional domains showed strong construct solidarity. CONCLUSIONS: The present battery of metrics presents a fieldable toolkit that may be used to predict operational performance that can be clustered into separate components or used independently. It will aid predictive algorithm development aimed to identify critical predictors of individual military personnel and small-unit performance outcomes.
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OBJECTIVES: The purpose of this study was to assess the feasibility of utilizing three dimensional whole body laser surface scanning (3DS) to obtain specific anthropometric measurements to estimate percent body fat (BF). METHODS: Percent BF estimates from 37 male volunteers, of age 18-62 yr, were determined by inputting manual anthropometric (MA) and 3DS anthropometric measurements into the current Army BF prediction equation for males. The results were compared with each other and to BF values from Dual Energy X-ray Absorptiometry (DXA), employed as a reference method. RESULTS: Mean percent BF estimates (+/-SD) derived from MA, 3DS and from DXA were 18.4(+/-3.8), 18.8(+/-3.9), and 18.9(+/-4.7), respectively. Analysis of Variance tests revealed no statistical difference between the mean values. Correlation analysis comparing MA and 3DS derived percent BF estimates to each other and to those measured by DXA revealed moderate to strong Pearson correlation coefficients (r), small to moderate standard errors of the estimate (SEE), and were statistically significant (p < 0.05). CONCLUSIONS: Correlation coefficients and SEE results for this sample were: (1) DXA vs 3DS; r = 0.74, SEE = 3.2, (2) MA vs DXA; r = 0.82, SEE = 2.8, and (3) MA vs 3DS; r = 0.96, SEE = 1.0. Lin's concordance analysis, including Bland-Altman limits of agreement (LOA), revealed statistically significant measurement agreement among the three measurement modalities (p < 0.05). The application of 3DS scanning to estimate percent BF from commonly used anthropometric measurements are in close agreement with BF estimates derived from analogous MA measurements and from DXA scanning.
