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.

这项小型企业创新研究（SBIR）项目的更广泛的影响/商业潜力是在咨询后对症状进行更客观的测量。每年，全世界有4200万个人遭受咨询，在美国，直接医疗费用每年的直接医疗费用为13亿美元。脑震荡代表了一种临床方案，可以从高级远程监控工具中受益匪浅。症状跟踪（即头痛，头晕，疲劳等）是评估临床医生在咨询诊断和康复方面使用的关键决定时最放松的。不幸的是，症状可能会根据一天中的时间，活动，睡眠或其他无抑制相关因素而波动。此外，症状评估通常也容易回忆起偏见。这些局限性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。这项SBIR I期项目提案旨在开发软件，以使用个人的智能手机远程监控咨询症状。这种数字表型的概念已用于心理健康障碍，但尚未应用于咨询。研究数字表型用于心理健康证明的研究通过减少治疗时间和开发客观测量的时间来改善诊断和治疗。将数字表型应用于咨询症状可以解决类似的问题：1）治疗时间和2）目标。提出的解决方案使用实时监控来从智能手机的传感器中收集数据。功能工程和监督的机器学习技术被应用于传感器数据，以开发一个模型以预测咨询符号。当前的提案将利用数字表型文献中建立的技术，但将评估此新颖应用的其他指标和技术。该奖项反映了NSF的法定任务，并通过基金会的智力优点和更广泛的影响来评估NSF的法定任务。