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

SBIR 第一阶段：利用智能手机数据改善脑震荡护理的临床决策

• 批准号: 2051965
• 负责人: Kathryn Van Pelt
• 金额: $ 21.63万
• 依托单位: SYNAPTEK LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051965&HistoricalAwards=false
• 关键词: SBIR; Phase; Leveraging; smartphone; data

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.

每年的小型企业创新研究的更广泛的影响/商业，全世界有4200万个人遭受脑震荡，而达成的直接医疗费用则是13亿美元的脑震荡。不幸的是，从高级远程监控工具，头晕，疲劳等。这些限制通常会导致不完整的限制，并妨碍临床医生的症状。尚未应用于调查数字表型的研究表明诊断的改善，治疗tome Time Time Time to digital措施g数字表型为简心症状。时间启动从智能手机的传感器收集数据。 NSF'Sflutorts值得支持Thalugh评估，并使用基金会的功绩和更广泛的影响审查标准。