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Updated: May 21, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Early detection of asthma exacerbations by using action points in self-management plans
Persijn J Honkoop1, D Robin Taylor, Andrew D Smith
1Dept of Medical Decision Making, Leiden University Medical Center, Leiden, The Netherlands. P.J.Honkoop@lumc.nl
Abstract:
Our aim was to validate optimal action points in written action plans for early detection of asthma exacerbations. We analysed daily symptoms and morning peak expiratory flows (PEFs) from two previous studies. Potential action points were based on analysis of symptom scores (standard deviations) percentage of personal best PEF, PEF variability in relation to a run-in period or combinations of these measures. Sensitivity and specificity for predicting exacerbations were obtained for each action point. The numbers needed to treat to prevent one exacerbation and the time interval between reaching action point criteria and the start of the exacerbation were calculated. Based on these parameters, the optimal action points for symptoms, PEF and PEF plus symptoms were determined, and their performance compared with published guidelines' action points. The optimal action points were, for symptoms, statistical variability (standard deviations) and, for PEF, <70% of personal best. The combination of PEF plus symptoms performed best, with improved specificity and earlier detection. The main benefits associated with using these action points was to reduce false positive rates for detecting exacerbations. Early detection of asthma exacerbations can be improved using a composite action point comprising symptoms and PEF measurements over 1 week.
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