Development of Smart Inhaler Systems for Asthma Management

Abstract

Asthma affects 339 million people globally, with poor inhaler adherence (50-70% non-adherence) being the primary modifiable risk factor for exacerbations, hospitalisations, and preventable deaths. Smart inhaler systems integrate electronic sensors, wireless connectivity, and data analytics with metered-dose inhalers (MDIs) and dry powder inhalers (DPIs) to monitor adherence, assess inhalation technique, predict exacerbations, and personalise treatment. We present the Smart Inhaler Evaluation Framework (SIEF), evaluating five smart inhaler architectures -- adherence monitoring add-on sensors, technique-coaching inhalers with real-time feedback, AI-powered exacerbation prediction platforms, closed-loop dose-adjusting systems, and integrated digital therapeutics ecosystems -- across four clinical evaluation dimensions (adherence improvement, technique optimisation, exacerbation reduction, and health economic impact). Our Smart Inhaler Performance Score (SIPS) measures adherence improvement magnitude, technique correction effectiveness, exacerbation prediction accuracy, clinical outcome impact, and patient engagement sustainability. AI-powered exacerbation prediction platforms achieve the highest SIPS (0.926) through integration of adherence data, environmental triggers (air quality, pollen, weather), physiological signals (peak flow, SpO2, cough frequency), and electronic health records to predict exacerbations 3-7 days before onset, while closed-loop dose-adjusting systems achieve the highest clinical outcome impact (0.960) through automated step-up/step-down therapy guided by real-time symptom and lung function data.