ArticleJournal of clinical and translational science2026
Remote data collection to detect asthma exacerbations: A decentralized approach to clinical research in asthma.
Article in Journal of clinical and translational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
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Abstract
Background: 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. Methods: 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 Results: 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% FEV Conclusions: 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.
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