SBIR Phase I: Leveraging smartphone data to improve clinical decisions in concussion care

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to develop a more objective measure of symptoms after a concussion. Each year, 42 million individuals worldwide suffer a concussion, and cost $1.3 billion per year in direct medical costs in the United States. Concussions represent a clinical scenario that can highly benefit from advanced remote monitoring tools. Symptom tracking (i.e. headaches, dizziness, fatigue, etc.) is the most relied upon assessment clinicians use for critical decisions regarding concussion diagnosis and rehabilitation. Unfortunately, symptoms can fluctuate based on the time of day, activity, sleep, or other non-concussion related factors. In addition, symptom evaluations often are also susceptible to recall bias. These limitations lead to incomplete and inaccurate symptom evaluations that hamper a clinicians ability to properly manage treatment strategies. This SBIR Phase I project proposes to develop software to remotely monitor concussion symptoms using an individual’s smartphone. This concept of digital phenotyping has been used for mental health disorders but has not yet been applied to concussions. Studies investigating digital phenotyping for mental health demonstrate improved diagnosis and treatment by reducing time to treatment and developing objective measures. Applying digital phenotyping to concussion symptoms can solve similar issues: 1) time to treatment and 2) objectivity. The proposed solution uses real-time monitoring to collect data from a smartphone’s sensors. Feature engineering and supervised machine learning techniques are applied to the sensor data to develop a model to predict concussion symptoms. The current proposal will leverage established techniques from the digital phenotyping literature but will evaluate other metrics and techniques for this novel application.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.