Journal of Asthma and Allergy (Jul 2024)
iPREDICT: Characterization of Asthma Triggers and Selection of Digital Technology to Predict Changes in Disease Control
Abstract
Mario Castro,1 Merrill Zavod,2 Annika Rutgersson,3 Magnus Jörntén-Karlsson,4 Bhaskar Dutta,5 Lynn Hagger6 1Division of Pulmonary, Critical Care and Sleep Medicine, University of Kansas School of Medicine, Kansas City, KS, USA; 2User Experience, AstraZeneca, Wilmington, DE, USA; 3Digital Patient Products, AstraZeneca, Gothenburg, Mölndal, Sweden; 4Digital Implementation, Digital Health R&D, AstraZeneca, Gothenburg, Mölndal, Sweden; 5Patient Safety Center of Excellence, AstraZeneca, Gaithersburg, MD, USA; 6Content Strategy & Experience Design, Digital Global Commercial, AstraZeneca, Gaithersburg, MD, USACorrespondence: Mario Castro, Chief, Division of Pulmonary, Critical Care and Sleep Medicine, Vice Chair for Clinical and Translational Research, University of Kansas School of Medicine, 4000 Cambridge Street, MSN 3007, Kansas City, KS, 66160, USA, Tel +1 913 588 7529, Fax +1 913 588 4098, Email [email protected]: The iPREDICT program aimed to develop an integrated digital health solution capable of continuous data streaming, predicting changes in asthma control, and enabling early intervention.Patients and Methods: As part of the iPREDICT program, asthma triggers were characterized by surveying 221 patients (aged ≥ 18 years) with self-reported asthma for a risk–benefit analysis of parameters predictive of changes in disease control. Seventeen healthy volunteers (age 25– 65 years) tested 13 devices to measure these parameters and assessed their usability attributes.Results: Patients identified irritants such as chemicals, allergens, weather changes, and physical activity as triggers that were the most relevant to deteriorating asthma control. Device testing in healthy volunteers revealed variable data formats/units and quality issues, such as missing data and low signal-to-noise ratio. Based on user preference and data capture validity, a spirometer, vital sign monitor, and sleep monitor formed the iPREDICT integrated system for continuous data streaming to develop a personalized/predictive algorithm for asthma control.Conclusion: These findings emphasize the need to systematically compare devices based on several parameters, including usability and data quality, to develop integrated digital technology programs for asthma care.Keywords: asthma, devices, digital, predictive algorithm, sensors
