STTR Phase II: User-Friendly Spirometer and Mobile App for Self-Management and Home Monitoring of Asthma Patients

STTR 第二阶段：用户友好的肺活量计和移动应用程序，用于哮喘患者的自我管理和家庭监测

• 批准号: 1738560
• 负责人: Charvi Shetty
• 金额: $ 50万
• 依托单位: KNOX Medical Diagnostics Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738560&HistoricalAwards=false
• 关键词: STTR; Phase; II; User; Friendly

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is the development of a user-friendly asthma management solution. The asthma management tool consists of a portable medical device and mobile app combination that measures lung function with the consistency and accuracy of a trained lab technician, displaying current asthma status, and providing health insights to act upon. During the 2-year duration of this proposal, the applicant will further develop a machine-learning algorithm that obtains the same level of consistency and accuracy as if a trained lab technician were coaching and correcting the asthmatic patient on proper usage. Steps will be taken to establish the efficacy of such technology through verification of classification by pulmonologists. In order to engage users to continue using the product over several months, gamification elements will be implemented into the mobile app. By expanding on the machine-model through a longitudinal study, earlier detection of asthma exacerbations may be identified. Early detection leads to improved self-management as measured by the reduction of severe asthma attacks, the use of systemic corticosteroids, hospitalizations, emergency department or urgent care visits related to asthma.The proposed project aims to develop an asthma management tool that provides parents a simple way to reliably monitor their child's lung health, eliminating the guesswork associated with relying on symptoms alone. The rate of asthma continues to rise, with an increasing amount of healthcare utilization among asthmatic individuals. Effective technologies for proper management remain trapped within the hospital due to high costs and requirement of a skilled lab technician for proper measurement collection. This proposal aims to develop an algorithm alongside UCSF pediatric pulmonologists to gain consistent and accurate spirometry measurements, so that only proper measurements are acted upon. Our machine-learning algorithm will determine the cause of failure and prompt the user on corrective action to achieve a good quality measurement on a subsequent effort. By the end of this proposal period, the applicant will have a mobile app algorithm that is able to achieve the consistency of a trained lab technician, provide effective corrective feedback, engage users over the span of months for consistent lung monitoring, and potentially predict the onset of an asthma attack.

这个小企业技术转让（STTR）第二阶段项目的更广泛的影响/商业潜力是开发一个用户友好的哮喘管理解决方案。哮喘管理工具由便携式医疗设备和移动的应用程序组合组成，可测量肺功能，具有训练有素的实验室技术人员的一致性和准确性，显示当前哮喘状态，并提供健康见解以采取行动。在本提案的2年期限内，申请人将进一步开发机器学习算法，该算法将获得与训练有素的实验室技术人员指导和纠正哮喘患者正确使用相同的一致性和准确性水平。将采取步骤，通过肺科医生对分类的验证，确定这种技术的有效性。为了吸引用户在几个月内继续使用该产品，将在移动的应用程序中实施游戏化元素。通过纵向研究扩展机器模型，可以识别哮喘急性发作的早期检测。早期发现可以改善自我管理，减少严重哮喘发作、使用全身性皮质类固醇、住院、急诊或与哮喘相关的紧急护理访视。拟议项目旨在开发一种哮喘管理工具，为父母提供一种简单的方法来可靠地监测孩子的肺部健康，消除与仅依赖症状相关的猜测。哮喘的发病率持续上升，哮喘患者的医疗保健利用率不断增加。由于高成本和需要熟练的实验室技术人员进行适当的测量收集，用于适当管理的有效技术仍然被困在医院内。该提案旨在与UCSF儿科肺病学家一起开发一种算法，以获得一致和准确的肺功能测定测量，以便仅对适当的测量采取行动。我们的机器学习算法将确定故障原因，并提示用户采取纠正措施，以在后续工作中实现良好的质量测量。在本提案期结束时，申请人将拥有一个移动的应用程序算法，该算法能够实现训练有素的实验室技术人员的一致性，提供有效的纠正反馈，在数月内吸引用户进行一致的肺部监测，并有可能预测哮喘发作。