Key result
HRV and body motion strongly differentiate active simulation from rest, with RMSSD tracking perceived demand.
Why the study?
Assessing task workload during military En Route Critical Care and other high-risk clinical operations remains challenging.
Does continuous monitoring of heart rate variability and body motion accurately assess task workload in clinicians during simulated critical care missions?
Observational (n=72)
Does continuous monitoring of heart rate variability and body motion accurately assess task workload in clinicians during simulated critical care missions?
Effect estimate: d = 0.54-1.22 for HRV
p-value: p=<0.001
Wearable sensors capturing HRV and body motion are feasible and valid digital biomarkers for assessing task workload in clinicians during high-stress critical care operations.
Supports wearable HRV monitoring for workload in simulations; leaves open real-world validation and outcome impact.
Assessing task workload during military En Route Critical Care (ERCC) and other high-risk clinical operations remains challenging. This study evaluated heart rate variability (HRV) and body motion as digital biomarkers of task workload among Critical Care Air Transport (CCAT) teams during simulated ERCC missions. Seventy-two U.S. Air Force clinicians participated in 26 high-fidelity simulations. HRV and body motion were continuously captured via a chest-strap wearable sensor, and the NASA Task Load Index (NASA-TLX) was used to assess self-reported workload. Results demonstrated high feasibility (>90% HRV, 100% motion retention) with high wearable comfort ratings. Both HRV and Motion metrics showed strong construct validity, significantly differentiating resting baseline from active simulation (all p < 0.001, effect sizes d = 0.54–1.22 for HRV, r = 0.92–1.00 for motion). Convergent validity was selective: only baseline-adjusted root mean square of successive differences (RMSSD) demonstrated significant associations with NASA-TLX mental demand (β = −0.250, p = 0.005) and temporal demand (β = −0.233, p = 0.003). Baseline-adjusted RMSSD effectively indexes perceived workload, while Motion metrics provide complementary information about physical task demands. These findings support multimodal approaches integrating physiological reactivity, motion patterns, and self-reported ratings for comprehensive workload assessment in complex operational settings.
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Dias et al. (2026) conducted an observational in Task workload during military En Route Critical Care (n=72). Heart rate variability and body motion monitoring vs. Resting baseline was evaluated on Differentiation of resting baseline from active simulation (d = 0.54-1.22 for HRV, p=<0.001). Heart rate variability and body motion metrics significantly differentiated active simulation from resting baseline (p<0.001), with RMSSD indexing perceived mental and temporal demand.
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