Background The adherence to inhaled controller medications is of critical importance for achieving good clinical results in patients with chronic respiratory diseases. Self-management strategies can result in improved health outcomes and reduce unscheduled care and improve disease control. However, adherence assessment suffers from difficulties on attaining a high grade of trustworthiness given that patient self-reports of high-adherence rates are known to be unreliable.

Objective Aiming to increase patient adherence to medication and allow for remote monitoring by health professionals, a mobile gamified application was developed where a therapeutic plan provides insight for creating a patient-oriented self-management system. To allow a reliable adherence measurement, the application includes a novel approach for objective verification of inhaler usage based on real-time video capture of the inhaler's dosage counters.

Methods This approach uses template matching image processing techniques, an off-the-shelf machine learning framework, and was developed to be reusable within other applications. The proposed approach was validated by 24 participants with a set of 12 inhalers models.

Results Performed tests resulted in the correct value identification for the dosage counter in 79% of the registration events with all inhalers and over 90% for the three most widely used inhalers in Portugal. These results show the potential of exploring mobile-embedded capabilities for acquiring additional evidence regarding inhaler adherence.

Conclusion This system helps to bridge the gap between the patient and the health professional. By empowering the first with a tool for disease self-management and medication adherence and providing the later with additional relevant data, it paves the way to a better-informed disease management decision.

背景 坚持吸入控制药物对于慢性呼吸系统疾病患者取得良好的临床结果至关重要。自我管理策略可以改善健康结果，减少计划外护理并改善疾病控制。然而，鉴于患者自我报告的高依从率已知不可靠，依从性评估难以获得高等级的可信度。

目标 为了提高患者对药物的依从性并允许卫生专业人员进行远程监控，开发了一种移动游戏化应用程序，其中治疗计划为创建以患者为导向的自我管理系统提供了洞察力。为了实现可靠的依从性测量，该应用程序包括一种基于吸入器剂量计数器的实时视频捕获客观验证吸入器使用情况的新方法。

方法 这种方法使用模板匹配图像处理技术、现成的机器学习框架，并被开发为可在其他应用程序中重复使用。所提出的方法得到了 24 名参与者的验证，其中包含一组 12 个吸入器模型。

结果 执行的测试在所有吸入器的 79% 的注册事件中以及 90% 以上的葡萄牙三个最广泛使用的吸入器的注册事件中导致剂量计数器的正确值识别。这些结果显示了探索移动嵌入式功能以获取有关吸入器依从性的额外证据的潜力。

结论 该系统有助于弥合患者和卫生专业人员之间的差距。通过为第一个提供疾病自我管理和药物依从性的工具并为后者提供额外的相关数据，它为更明智的疾病管理决策铺平了道路。