Clinical study of an inhalation training and feedback device, user app, clinician portal and cloud based data analytics tool for self management and remote monitoring of respiratory conditions

全世界有超过3.25亿人--英国有540万人--患有哮喘，每年造成的损失超过3630亿美元。日益加剧的健康不平等、冠状病毒大流行、日益增长的患者负担和成本要求更好的哮喘管理。正确使用吸入器和坚持处方治疗对于最佳控制、避免病情恶化、降低死亡率和医疗费用至关重要。一项对吸入器使用情况的荟萃分析和系统回顾(Chrystin Et Al 2017)得出结论，86.6%的患者至少会犯一个错误，而Usmani(2018)发现吸入器错误、糟糕的疾病结果和更大的健康经济负担之间存在显著关联。开发智能吸入器是为了解决这个问题，但由于功能缺陷(主要关注依从性而不是技术)、成本高以及慢性疾病管理中数字支持工具的采用缓慢，影响有限。气候问题也推动了寻找降低吸入器，特别是计量吸入器的全球变暖潜力(GWP)的方法的需要。在2020-21年度，我们设计、构建和测试了第一次迭代的创新数字平台，整合了数据捕获设备(根据医疗设备标准构建)、用户应用程序、临床医生门户和数据分析工具来满足这一需求。它可提示用户“预防”用药依从性，并监控5个关键的吸入步骤，具有实时校正反馈、聚合临床、行为和环境(空气质量)数据，以及支持自我管理和远程临床医生监控的人工智能驱动的分析。优化每个药物剂量将减少吸入器的全球变暖潜力。该平台使用合成数据作为真实世界的数据集进行了成功的测试，该数据集结合了实际的吸入器技术数据和无法识别的环境数据。该项目建议在100名患者中进行随机对照临床试验，以评估该平台的能力，以改善哮喘症状控制，并通过记录和共享吸入技术数据来优化用药、自我管理和远程监测。这项研究产生的唯一真实患者数据集(吸入器类型、摇动持续时间、分配时间、吸入时间、吸入率和吸气量，与空气质量汇总)将识别吸入器行为/技术-空气质量-症状关系。这将使平台能够更准确地远程检测和通知高危用户(及其临床医生)，通过早期和适当的干预改进自我管理并降低系统成本。我们在哮喘英国-英国肺基金会的支持下共同设计了该平台，并让用户和专家临床医生参与研究设计。

Over 325M people around the world - and 5.4M in the UK - suffer from asthma, costing over $363B every year. Rising health inequalities, the coronavirus pandemic, growing patient burden and costs demand better asthma management. The correct use of inhaler devices and adherence to prescribed therapy are essential for optimal control, avoiding exacerbations, reducing mortality and healthcare costs. A meta-analysis and systematic review (Chrystyn et al 2017) of inhaler use concluded that 86.6% patients make at least one error, while Usmani (2018) identified a significant association between inhaler errors, poor disease outcomes and greater health-economic burden.'Smart inhalers' were developed to address this problem but have had limited impact due to functionality deficits (focused mainly on adherence rather than technique), high cost and slow adoption of digital support tools in chronic condition management. Climate concerns have also driven the need to find ways to reduce the global warming potential (GWP) of inhalers, in particular that of Metered Dose Inhalers.In 2020-21 we designed, built and tested a first iteration, innovative digital platform, incorporating a data capture device (built to medical device standards), user App, clinician portal, and data analytics tool to address this need. It prompts user 'preventer' medication adherence and monitors the 5 key inhalation steps, with real-time correction feedback, aggregating clinical, behavioural and environmental (air quality) data, with AI driven analytics to support self management, remote clinician monitoring. Optimising each medication dose will reduce the global warming potential of inhalers. The platform was successfully tested using synthetic data as a real-world data set that combines actual inhaler technique data with environmental data could not be identified.This project proposes to undertake a randomised controlled clinical trial in 100 patients to evaluate the platform capability to improve asthma symptom control and to optimise medication usage, self management and remote monitoring via the recording and sharing of inhalation technique data.The unique real patient dataset generated by this study (inhaler type, shake duration, time to dispense, time to inhalation, inhalation rate and volume inhaled, aggregated with air quality) will identify inhaler behaviour/technique-air quality-symptom relationships. This will permit more accurate platform capability to remotely detect and notify at-risk users (and their clinicians), improving self-management and lowering system costs via early and appropriate intervention.We've co-designed the platform with support from Asthma UK- British Lung Foundation, and had user and expert clinician input into the study design.