Trait-level predictors of human performance outcomes in personnel engaged in stressful laboratory and field tasks.

Introduction: Personnel performance under stress hinges on various factors, including individual traits, training, context, mental and physiological states, and task demands. This study explored the link between the traits of military personnel and their performance outcomes in five domains: move, shoot, communicate, navigate, and sustain. Methods: A total of 387 U.S. Army soldiers participated in this study, undergoing trait assessments covering physical, cognitive, social-emotional, demographic/lifestyle, and health domains. Performance was measured through lab and field events assessing a broad range of individual and team-level skills under conditions demanding resilience to acute cognitive and physical stress exposure. Analysis used feature selection and elastic net regression. Results: Analyses revealed complex associations between traits and performance, with physical, cognitive, health-related, social-emotional, and lifestyle traits playing roles in guiding and constraining performance. Measures of resilience, emotion regulation, grit, and mindfulness were identified as relevant predictors of several performance-related outcomes. Discussion: Results carry implications for the selection, training, and operational effectiveness of personnel in high-stakes occupations including military and first response. Further research is necessary to explore the mechanisms underlying these associations and inform targeted interventions to boost personnel effectiveness.

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

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.

Nonlinear Analyses Distinguish Load Carriage Dynamics in Walking and Standing: A Systematic Review.

Load carriage experiments are typically performed from a linear perspective that assumes that movement variability is equivalent to error or noise in the neuromuscular system. A complimentary, nonlinear perspective that treats variability as the object of study has generated important results in movement science outside load carriage settings. To date, no systematic review has yet been conducted to understand how load carriage dynamics change from a nonlinear perspective. The goal of this systematic review is to fill that need. Relevant literature was extracted and reviewed for general trends involving nonlinear perspectives on load carriage. Nonlinear analyses that were used in the reviewed studies included sample, multiscale, and approximate entropy; the Lyapunov exponent; fractal analysis; and relative phase. In general, nonlinear tools successfully distinguish between unloaded and loaded conditions in standing and walking, although not in a consistent manner. The Lyapunov exponent and entropy were the most used nonlinear methods. Two noteworthy findings are that entropy in quiet standing studies tends to decrease, whereas the Lyapunov exponent in walking studies tends to increase, both due to added load. Thus, nonlinear analyses reveal altered load carriage dynamics, demonstrating promise in applying a nonlinear perspective to load carriage while also underscoring the need for more research.