# Heart rate variability as a dual-use digital biomarker: integrating clinical, AI, and operational perspectives on human performance and resilience

## TL;DR

Heart rate variability (HRV) is a versatile biomarker that helps track health and performance in both medical and high-stress operational settings, supported by AI and ethical guidelines.

## Contribution

The paper introduces a cross-sector framework for HRV as a dual-use digital biomarker, integrating clinical, operational, and AI perspectives.

## Key findings

- HRV monitoring captures early autonomic changes before performance decline or clinical symptoms.
- AI models trained on HRV data can predict autonomic instability and risk in both clinical and operational contexts.
- Nocturnal HRV reductions consistently reflect accumulated stress during prolonged missions.

## Abstract

Heart rate variability (HRV) reflects autonomic regulation and has emerged as a dual-use digital biomarker across clinical care and operational performance. We sought to integrate evidence on HRV’s physiological basis, clinical utility, defense applications, and AI-enabled analytics, and to propose a cross-sector framework for predictive, ethical deployment.

We conducted a structured literature review in MEDLINE (PubMed), Embase, and Scopus between July 1st and August 31st, 2025, without language restriction. Eligible studies reported human HRV parameters measured in clinical, operational/defense, or AI contexts. Owing to heterogeneity, findings were summarized narratively across five domains: physiology, clinical applications, operational use, AI/predictive analytics, and ethics/standardization.

Evidence from military and operational studies supports HRV as a physiological indicator of stress accumulation, fatigue, and recovery during sustained workload and mission exposure. Across training environments, continuous HRV monitoring captured early autonomic changes preceding measurable performance decline or clinical symptoms. During prolonged field exercises, nocturnal HRV reductions consistently reflected accumulated allostatic load, while daily fluctuations in SDNN, RMSSD, and LF/HF ratios revealed real-time adaptations to physical exertion, sleep deprivation, and psychological strain. These dynamic shifts offered a quantifiable index of resilience, distinguishing between individuals able to sustain operational effectiveness and those approaching physiological or cognitive exhaustion. AI further enhances this capability by identifying non-linear and context-dependent HRV patterns that precede fatigue or decompensation. Machine-learning models trained on multimodal data streams enable early detection of autonomic instability and predictive risk stratification in both training and operational theaters.

HRV is not just a number—it is a real-time window into how our bodies respond to life’s challenges, from the doctor’s office to the most demanding missions. What makes HRV so unique is its “dual-use” quality: it matters just as much for medical professionals caring for patients as it does for those monitoring the wellbeing and performance of people working under stress, such as soldiers or first responders. By treating HRV as a dual-use tool, one can bridge the worlds of healthcare and operational performance. This means the same heartbeat data that helps predict heart problems for a patient can also warn a team leader when their crew might be on the edge of exhaustion. But making the most of HRV in both settings requires to collect data consistently, analyze it with trustworthy AI, protect privacy, and put clear guidelines in place. In doing so, HRV becomes more than a monitor—a practical, ethical way to support better decisions, whether saving lives in a hospital or keeping people safe and effective under pressure.