Mobile Health to Objectively Measure Stress in Real-Time

DI has created an algorithm, ca lled Operational Stress Index (OSI), that uses wearable physiological sensors to objectively measure stress in real-time for operational and training environments.

The OSI is a patent-pending predictive stress algorithm that uses non-invasive, wearable physiological sensors. It captures and processes real-time raw streaming physiological data in a mobile environment to determine an individual’s stress level in comparison to a previously established baseline. The suite of sensors includes blood volume pulse (BVP), electrodermal activity (EDA), temperature, and movement data from a 3 axis accelerometer, and transmits this information via Bluetooth to a mobile device or stored locally with the wearable biosensor. It has proven to have over a 95% accuracy rate in a lab-based study.

Recent estimates suggest that approximately one-third of individuals globally will experience mental health disorders in their lifetime. It can be used in high-stress level positions such as first responders, military personnel, athletes, CEOs, etc. This application can be used like a traditional wearable for mobile health (mHealth) such as the FitBit band.

The OSI was validated with and implemented in a USMC Infantry Small Unit Leader’s course to improve small unit training for adaptability and resilience in decision making. It was also validated during a clinical evaluation with the VA to support cognitive behavioral therapy for PTSD treatment. Patients who used the classifier and an associated mHealth application were less likely to discontinue therapy and significantly improved on measures of stress, anxiety, and anger.

When integrated into operational or training environments, the OSI will support readiness to succeed in the modern operational environment:

  • Adaptive training: Enable adaptations to training based upon stress response
  • Performance evaluation: Go beyond observable metrics and enhance the quality of assessment and feedback
  • Selection: Objectively identify the most naturally resilient trainee who will best perform under stress and who are least likely to develop stress disorders
  • Training system operational realism: Quantitatively compare how well a training system replicates the stress of the operational environment
  • Improved performance under stress: Develop targeted training based upon individual and team stress responses

Mobile health approaches have the potential to provide or augment treatment at low cost in the absence of in-person care. Wearable physiological sensors and associated mHealth applications have the potential to quantify biological metrics associated with stress , support remote monitoring, and alert the wearer or provider to real-time changes in emotional state.

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