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

这一小型企业技术转让(STTR)第二阶段项目的更广泛影响/商业潜力是开发一种用户友好型哮喘管理解决方案。哮喘管理工具由便携式医疗设备和移动应用程序组合组成，该工具以训练有素的实验室技术人员的一致性和准确性测量肺功能，显示当前哮喘状态，并提供可供采取行动的健康见解。在本计划的两年期间，申请者将进一步开发一种机器学习算法，该算法获得相同水平的一致性和准确性，就像训练有素的实验室技术人员在指导和纠正哮喘患者的正确使用一样。将采取步骤，通过肺科医生对分类的核实来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，游戏化元素将被实施到移动应用程序中。通过纵向研究对机器模型进行扩展，可以更早地发现哮喘恶化。及早发现可以改善自我管理，减少严重哮喘发作、全身皮质类固醇的使用、住院、急诊科或与哮喘有关的紧急护理就诊。拟议的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他们孩子的肺部健康，消除仅依靠症状相关的猜测。哮喘的发病率继续上升，哮喘患者对医疗保健的使用量不断增加。由于高昂的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。这项建议旨在与加州大学旧金山分校的儿科肺科医生一起开发一种算法，以获得一致和准确的肺活量测量结果，以便只对适当的测量结果采取行动。我们的机器学习算法将确定失败的原因，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。在此建议书期末，申请者将拥有一个移动应用程序算法，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，在几个月的时间内吸引用户进行一致的肺部监测，并有可能预测哮喘发作的发作。

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.