Digital Health: OptiMuscle - Improving health outcomes through the optimization of muscle function

在英国，大约有10%的人表现出某种形式的呼吸功能障碍。该术语描述了一系列以呼吸的肌肉控制受损为特征的病症，这些病症可导致呼吸困难、换气过度，在某些情况下还可导致头晕。目前的临床评估技术和功能障碍性呼吸的治疗是低技术的，临床管理集中在使用简单的呼吸练习。虽然这些练习确实有一些效果，但我们建议患者可以从使用肌肉模式生物反馈来指导呼吸再教育的系统中获得更多益处。此外，如果这样的系统可以自动化，则应该可以向大量的人提供呼吸再教育，而不需要临床专家在场。这一点非常重要，因为呼吸再教育通常是其他健康状况（如哮喘、背痛和焦虑）临床管理的关键部分。该项目将为功能障碍性呼吸的临床管理提供一个全新的数字健康系统。该系统将使用视觉和听觉生物反馈来传达异常的肌肉功能，引导患者逐渐学习正确的肌肉呼吸控制过程。为了实现这种个性化系统，我们将开发软件，可以创建患者的个人化身，并使用它来实时可视化呼吸肌肉的动作。为了避免在临床环境中直接测量肌肉的需要，我们将开发可以从可以在临床环境中收集的简单传感器数据的输入预测肌肉激活的算法，例如廉价的3D相机。为了补充视觉生物反馈的使用，我们将使用听觉生物反馈来传达肌肉功能的微妙变化，并帮助加强对新肌肉模式的学习。我们的多模式（视觉和听觉）生物反馈系统将被整合到行为改变干预中，为患者提供改变其肌肉相关呼吸行为的能力、机会和动力。我们将通过与患者密切合作来开发我们的新干预措施，以了解他们对最终系统应如何运作的看法。一旦创建，我们将对呼吸功能障碍的人进行小型试验，以了解未来的潜力。如果这项测试提供了令人鼓舞的结果，那么我们将申请资助一个更大的NHS试验。

Approximately 10% of people in the UK population exhibit some form of dysfunctional breathing. This term describes a range of conditions which are characterised by an impairment in the muscular control of breathing and which can result in breathlessness, hyperventilation and in some cases dizziness. Current clinical assessment techniques and treatments for dysfunctional breathing are low-tech, with clinical management focused around the use of simple breathing exercises. Whilst these exercises do have some effect, we propose that patients would get more benefit from a system which uses biofeedback on muscle patterns to guide breathing re-education. Furthermore, if such a system could be automated, it should be possible to provide breathing re-education to large numbers of people without the need for a clinical specialist to be present. This is important because breathing re-education is often a key part of the clinical management of other health conditions, such as asthma, back pain and anxiety.This project will deliver a completely new digital health system for the clinical management of dysfunctional breathing. The system will use both visual and auditory biofeedback to communicate abnormal muscle function, guiding patients through a process in which they gradually learn the correct muscular control of breathing. To realise this personalised system, we will develop software which can create an individual avatar of the patient and use this to visualise the actions of the breathing muscle in real-time. To avoid the need to directly measure muscles in a clinical setting, we will develop algorithms which can predict muscle activations from an input of simple sensor data which can be collected in a clinical setting, e.g. inexpensive 3D camera. To complement the use of visual biofeedback, we will use auditory biofeedback to convey subtle changes in muscle function and help reinforce the learning of new muscle patterns. Our multimodal (visual and audio) biofeedback system will be integrated into a behaviour change intervention, providing patients with the capability, opportunity and motivation to change their muscle-related breathing behaviours. We will develop our new intervention by working closely with patients to understand their views on how the final system should operate. Once created, we will carry out a small trial on people with dysfunctional breathing to understand the future potential. If this testing provides encouraging results, then we will apply for funding for a larger NHS trial.