Remote data collection to detect asthma exacerbations: A decentralized approach to clinical research in asthma

Decentralized trial designs can improve accessibility and continuity of research participation by enabling remote data collection. This manuscript describes our team's experiences with remote data collection to identify acute asthma exacerbations in a clinical study as well as practical insights that support the continued optimization of remote methodologies.

In this 12-month observational study, adolescents aged 12-21 years with persistent asthma and ≥1 exacerbation in the prior 24 months completed an initial in-person visit followed by monthly virtual visits. Participants used home spirometry, app-based symptom tracking, smart inhalers to monitor lung function and short-acting beta agonist (SABA) use, and self-collection of nasal epithelial lining fluid (NELF) samples. Exacerbations were defined a priori by symptom/SABA thresholds or ≥20% FEV₁ decline.

Forty participants enrolled; 73% completed all visits. Median adherence to performance of daily spirometry and symptom surveys was 44% and 38%, respectively. Seventy-eight percent experienced ≥1 exacerbation. Of 132 alerts, 80% represented true exacerbations, primarily due to ≥20% FEV₁ decline; erroneous alerts were linked to software errors and poor spirometry technique. Sixty-six NELF sample sets were collected and 50 were analyzed. Cytokine concentrations did not differ significantly between clinic-collected and self-collected samples. Technical challenges included device connectivity issues, erroneous alerts, and shipping delays.

Decentralized study designs with remote data collection requires further study as a means of conducting clinical research in asthma that increases participant accessibility, representation and generalizability of trial results. This approach presents numerous challenges and requires further optimization to address adherence, technical complexity, and staff burden while maintaining scientific rigor.