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，每年花费超过3630亿美元。健康不平等的加剧、新型冠状病毒大流行、患者负担和成本的增加要求更好的哮喘管理。正确使用吸入器装置和坚持处方治疗对于最佳控制、避免病情加重、降低死亡率和医疗费用至关重要。一项关于吸入器使用的荟萃分析和系统性综述（Chelyn et al 2017）得出结论，86.6%的患者至少发生一次错误，而Usmani（2018）发现吸入器错误、不良疾病结局和更大的健康经济负担之间存在显著相关性。“智能吸入器”的开发是为了解决这个问题，但由于功能缺陷（主要集中在坚持而不是技术），成本高，慢性病管理中数字支持工具的采用缓慢，影响有限。在2020-21年度，我们设计、构建和测试了第一个迭代的创新数字平台，该平台整合了数据采集设备（根据医疗器械标准构建）、用户应用程序、临床医生门户网站和数据分析工具，以满足这一需求。它提示用户“预防者”药物依从性并监控5个关键吸入步骤，实时纠正反馈，汇总临床，行为和环境（空气质量）数据，人工智能驱动的分析支持自我管理，远程临床医生监控。优化每种药物剂量将降低吸入器的全球变暖潜力。该平台使用合成数据进行了成功测试，因为无法识别实际吸入器技术数据与环境数据相结合的真实数据集。该项目建议在100名患者中进行随机对照临床试验，以评估该平台改善哮喘症状控制和优化药物使用的能力，通过记录和共享吸入技术数据进行自我管理和远程监测。本研究生成的唯一真实的患者数据集（吸入器类型、摇动持续时间、分配时间、吸入时间、吸入速率和吸入体积，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。这将允许更准确的平台功能远程检测和通知风险用户（及其临床医生），通过早期和适当的干预改善自我管理并降低系统成本。我们在英国哮喘基金会的支持下共同设计了该平台，并将用户和专家临床医生输入研究设计。

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.