Effectiveness of myAirCoach: A mHealth Self-Management System in Asthma

Background: Self-management programs have beneficial effects on asthma control, but their implementation in clinical practice is poor. Mobile health (mHealth) could play an important role in enhancing self-management. Objective: To assess the clinical effectiveness and technology acceptance of myAirCoach-supported self-management on top of usual care in patients with asthma using inhalation medication. Methods: Patients were recruited in 2 separate studies. The myAirCoach system consisted of an inhaler adapter, an indoor air-quality monitor, a physical activity tracker, a portable spirometer, a fraction exhaled nitric oxide device, and an app. The primary outcome was asthma control; secondary outcomes were exacerbations, quality of life, and technology acceptance. In study 1, 30 participants were randomized to either usual care or myAirCoach support for 3 to 6 months; in study 2, 12 participants were provided with the myAirCoach system in a 3-month before-after study. Results: In study 1, asthma control improved in the intervention group compared with controls (Asthma Control Questionnaire difference, 0.70; P = .006). A total of 6 exacerbations occurred in the intervention group compared with 12 in the control group (hazard ratio, 0.31; P = .06). Asthma-related quality of life improved (mini Asthma-related Quality of Life Questionnaire difference, 0.53; P = .04), but forced expiratory volume in 1 second was unchanged. In study 2, asthma control improved by 0.86 compared with baseline (P = .007) and quality of life by 0.16 (P = .64). Participants reported positive attitudes toward the system. Discussion: Using the myAirCoach support system improves asthma control and quality of life, with a reduction in severe asthma exacerbations. Well-validated mHealth technologies should therefore be further studied

Inhaled aerosols: emerging clinical methods

The pattern of deposition of inhaled aerosols in the lungs of man in health and disease, in vivo, is reliant on multiple factors. The disease being targeted has a major influence on the eventual site of deposition. Obstructive lung disease alters the flow dynamics within the airways and restrictive lung diseases alters the ‘stiffness’ of the lungs and ability to inhale large volumes. There are multiple methods used to assess the fate of an inhaled aerosol within the lungs and the consequent clinical effect. Imaging allows visualization of inhaled aerosols via the use of labeling with radio-isotopes combined with imaging techniques such as planar scintigraphy, single photon emission computed tomography (SPECT) and positron emission tomography (PET). Computed tomography (CT), and magnetic resonance imaging (MRI) allow visualization of the structure of the lung and can also offer information of physiological dysfunction. Data from imaging can be related to physiological measurements of lung function and to clinical outcome. The combination of lung CT images with Computational Fluid and Particle Dynamics (CFPD) simulations has led to the development of personalized functional modeling of the airways to investigate disease in the small and large airways. All of these methods have advantages, disadvantages and limitations. None of these methods are able to directly visualize the small airways which is frequently the area of interest in lung disease. There are emerging methods of interest that may offer further data on the effects of inhaled therapeutic agents including novel MRI methods and use of micro-CT to investigate changes in small airway function. This chapter will summarize developments relating to inhaled aerosols and emerging clinical methods used to assess efficacy.</p

Is there room for further innovation in inhaled therapy for airways disease?

Inhaled medication is the cornerstone in the treatment of patients across a spectrum of respiratory diseases including asthma and chronic obstructive pulmonary disease. The benefits of inhaled therapy have long been recognised but the most important innovations have occurred over the past 60 years, beginning with the invention of the pressurised metered dose inhaler. However, despite over 230 different device and drug combinations currently being available, disease control is far from perfect. Here we look at how innovation in inhaler design may improve treatments for respiratory diseases and how new formulations may lead to treatments for diseases beyond the lungs. We look at the three main areas where innovation in inhaled therapy is most likely to occur: 1) device engineering and design; 2) chemistry and formulations; and 3) digital technology associated with inhalers. Inhaler design has improved significantly but considerable challenges still remain in order to continually innovate and improve targeted drug delivery to the lungs. Healthcare professionals want see innovations that motivate their patients to achieve their goal of improving their health, through better adherence to treatment. Patients want devices that are easy to use and to see that their efforts are rewarded by improvements in their condition. Key points The dictionary definition of innovation is the introduction of new things, ideas or ways of doing something. We show how this definition can be applied to inhaled therapy. We take a look at the past to see what drove innovation in inhaler design and how this has led to the current devices. We look at the current drivers of innovation in engineering, chemistry and digital technology and predict how this may translate to new devices. Can innovation help the healthcare professional manage their patients better? What does the patient expect from innovation in their device? Educational aimsTo understand the importance of inhaled medication in the treatment of lung diseases. To understand how innovation has helped advance some of the devices patients use today from basic and inefficient designs. To understand the obstacles that prevent patients from receiving optimal treatment from their inhalers. To understand how innovation in inhaler design can lead to improved treatment for patients and widen the range of diseases that can be treated via the inhaled route

Predicting the clinical effect of a short acting bronchodilator in individual patients using artificial neural networks

Artificial neural networks were used in this study to model the relationships between in vitro data, subject characteristics and in vivo outcomes from N = 18 mild–moderate asthmatics receiving monodisperse salbutamol sulphate aerosols of 1.5, 3 and 6 μm mass median aerodynamic diameter in a cumulative dosing schedule of 10, 20, 40 and 100 μg. Input variables to the model were aerodynamic particle size (APS), body surface area (BSA), age, pre-treatment forced expiratory volume in one-second (FEV1), forced vital capacity, cumulative emitted drug dose and bronchodilator reversibility to a standard salbutamol sulphate 200 μg dose MDI (REV(%)). These factors were used by the model to predict the bronchodilator response at 10 (T10) and 20 (T20) min after receiving each of the 4 doses for each of the 3 different particle sizes. Predictability was assessed using data from selected patients in this study, which were set aside and not used in model generation. Models reliably predicted ΔFEV1(%) in individual subjects with non-linear determinants (R2) of ≥0.8. The average error between predicted and observed ΔFEV1(%) for individual subjects was <4% across the cumulative dosing regimen. Increases in APS and drug dose gave improved ΔFEV1(%). Models also showed trends towards improved responses in younger patients and those having greater REV(%), whilst BSA was also shown to influence clinical effect. These data show that APS can be used to discriminate predictably between aerosols giving different bronchodilator responses across a cumulative dosing schedule, whilst patient characteristics can be used to reliably estimate clinical response in individual subjects

Additional file 1: of The topical study of inhaled drug (salbutamol) delivery in idiopathic pulmonary fibrosis

Particle generation and radiolabelling (DOCX 573 kb