Details

Autor(en) / Beteiligte

• Giles, Grace E
• Navarro, Ester
• Elkin-Frankston, Seth
• Brunyé, Tad T
• Elmore, Wade R
• Seay, Joseph F
• McKenzie, Kari L
• O’Fallon, Kevin S
• Brown, Stephanie A
• Parham, Joseph L
• Garlie, Todd N
• DeSimone, Linda
• Villa, Jose D
• Choi-Rokas, Hyegjoo E
• Mitchell, K Blake
• Racicot, Kenneth
• Soares, Jason W
• Caruso, Christina
• Anderson, Debra
• Cantelon, Julie A
• Gardony, Aaron L
• Smith, Tracey J
• Karl, J Philip
• Jayne, Julianna M
• Christopher, John J
• Talarico, Maria K
• Sperlein, Jennifer Neugebauer
• Boynton, Angela C
• Jensen, Andrew
• Ramsay, John W
• Eddy, Marianna D

Titel

Characterizing Relationships Among the Cognitive, Physical, Social-emotional, and Health-related Traits of Military Personnel

Ist Teil von

• Military medicine, 2023-07, Vol.188 (7-8), p.e2275-e2283

Ort / Verlag

US: Oxford University Press

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• ABSTRACT 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.