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

这个小型企业创新研究(SBIR)项目的更广泛的影响/商业潜力是开发一种更客观的脑震荡后症状衡量标准。每年，全球有4200万人遭受脑震荡，在美国，每年直接医疗费用为13亿美元。脑震荡代表了一种临床情景，可以从先进的远程监测工具中受益匪浅。症状跟踪(如头痛、头晕、疲劳等)是临床医生在有关脑震荡诊断和康复的关键决策中最依赖的评估。不幸的是，症状可能会根据一天中的时间、活动、睡眠或其他非脑震荡相关因素而波动。此外，症状评估往往也容易受到回忆偏差的影响。这些限制导致不完整和不准确的症状评估，从而阻碍了临床医生正确管理治疗策略的能力。这个SBIR第一阶段项目建议开发软件，使用个人的智能手机远程监控脑震荡症状。这种数字表型的概念已经被用于精神健康障碍，但还没有被应用于脑震荡。对心理健康数字表型的调查研究表明，通过减少治疗时间和制定客观措施，改善了诊断和治疗。将数字表型应用于脑震荡症状可以解决类似的问题：1)治疗时间和2)客观性。拟议的解决方案使用实时监控来从智能手机的传感器收集数据。将特征工程和有监督的机器学习技术应用于传感器数据，以建立预测脑震荡症状的模型。目前的提案将利用数字表型文献中已建立的技术，但将评估用于这一新应用的其他指标和技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.