Intensive, Real-Time Data Collection of Psychological and Physiological Stress During a 96-Hour Field Training Exercise at a Senior Military College: Feasibility and Acceptability Cohort Study
Bibliographic record
Abstract
BACKGROUND: Poor physical fitness, stress, and fatigue are factors impacting military readiness, national security, and economic burden for the United States Department of Defense. Improved accuracy of wearable biosensors and remote field biologic sample collection strategies could make critical contributions to understanding how physical readiness and occupational stressors result in on-the-job and environment-related injury, sleep impairments, diagnosis of mental health disorders, and reductions in performance in war-fighters. OBJECTIVE: This study aimed to evaluate the feasibility and acceptability of intensive biomarker and biometric data collection to understand physiological and psychological stress in Army Reserved Officer Training Corps cadets before, during, and after a 96-hour field training exercise (FTX). METHODS: A prospective pilot study evaluated the feasibility and acceptability of multimodal field data collection using passive drool saliva sampling, sweat sensors, accelerometry, actigraphy, and photoplethysmography. In addition, physical fitness (Army Combat Fitness Test), self-reported injury, and psychological resilience (Brief Resilience Scale) were measured. RESULTS: A total of 22 cadets were included. Two were lost to follow-up due to injury during FTX, for a retention rate of 91%. Assessments of performance and psychological resilience were completed for all remaining participants, resulting in 100% testing adherence. All participants provided saliva samples before the FTX, with 98% adherence at the second time point and 91% at the third. For sweat, data collection was not possible. Average daily wear time for photoplethysmography devices was good to excellent, meeting a 70% threshold with data collected for ≥80% of person-days at all time points. Of the participants who completed the FTX and 12 completed a post-FTX acceptability survey for a response rate of 60%. Overall, participant acceptance was high (≥80%) for all metrics and devices. CONCLUSIONS: This study demonstrates that wearable biosensors and remote field biologic sample collection strategies during a military FTX have the potential to be used in higher stakes tactical environments in the future for some, but not all, of the strategies. Overall, real-time biometric and biomarker sampling is feasible and acceptable during field-based training and provides insights and strategies for future interventions on military cadet and active-duty readiness, environmental stress, and recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".