• Grace E Giles, Ester Navarro, Seth Elkin-Frankston, Tad T Brunyé, Wade R Elmore, Joseph F Seay, Kari L McKenzie, Kevin S O'Fallon, Stephanie A Brown, Joseph L Parham, Todd N Garlie, Linda DeSimone, Jose D Villa, Hyegjoo E Choi-Rokas, K Blake Mitchell, Kenneth Racicot, Jason W Soares, Christina Caruso, Debra Anderson, Julie A Cantelon, Aaron L Gardony, Tracey J Smith, J Philip Karl, Julianna M Jayne, John J Christopher, Maria K Talarico, Jennifer Neugebauer Sperlein, Angela C Boynton, Andrew Jensen, John W Ramsay, and Marianna D Eddy.
• United States Army Combat Capabilities Development Command Soldier Center, Natick, MA 01760, USA.
• Mil Med. 2023 Jul 22; 188 (7-8): e2275e2283e2275-e2283.

IntroductionPersonnel 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 MethodsOne 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.ResultsFirst, 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.ConclusionsThe 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.Published by Oxford University Press on behalf of the Association of Military Surgeons of the United States 2023. This work is written by (a) US Government employee(s) and is in the public domain in the US.

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