Developing and evaluating a mobile phone-based early alert system using high resolution air quality forecast to improve asthma control in Malaysia

Research question: Is a high-resolution air quality forecast alert system designed within a mobile healthcare application (m-app) effective in improving asthma control? Background: Haze is common in Southeast Asia, including Malaysia. In earlier studies funded by RESPIRE in Klang, Malaysia (2018-2021), we found 66% of adults with asthma have poor control, with 51% reporting haze as a trigger. Exposure to haze was significantly associated with poor control (OR 1.51; 95% CI 1.13-2.01) and every 10 µg/m3 increase in PM10 increased the relative risk of an asthma exacerbation by 8.7% (RR 1.087; 95% CI 1.023-1.155). As exacerbations were likely to occur 2-days after haze exposure, there are opportunities for intervention. We propose to develop an air quality forecast alert system using an m-app for individuals with asthma and evaluate its effectiveness in improving asthma control. Methodology: There are three phases of work: 1: Develop a streetscale-resolution air quality forecast system using the Atmospheric Dispersion Modelling System Urban model. 2: Design an m-app as the platform for air quality forecast and asthma self-management. 3: Assess the usability and effectiveness of the m-app as an early alert system to support asthma self-management in improving asthma control through a pilot-feasibility study. Outcomes assessed include asthma control using Global Initiative for Asthma score as the primary outcome; and clinical (e.g., peak expiratory flow rate, emergency visits) and system (e.g., usability score, frequency of use) as the secondary outcomes. Questions to discuss: - How do we ensure participant’s adherence to using the m-app? - How do we sustain this app after study completion? - Can we assess if the spatial and temporal resolutions of the air quality forecast influence the effectiveness of the intervention? This may not be the main objective of this study, but the information would be helpful in designing the most effective forecast.