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. 2024 Jul 10:17:653-666.
doi: 10.2147/JAA.S458618. eCollection 2024.

iPREDICT: Characterization of Asthma Triggers and Selection of Digital Technology to Predict Changes in Disease Control

Mario Castro  1 , Merrill Zavod  2 , Annika Rutgersson  3 , Magnus Jörntén-Karlsson  4 , Bhaskar Dutta  5 , Lynn Hagger  6
Affiliations

iPREDICT: Characterization of Asthma Triggers and Selection of Digital Technology to Predict Changes in Disease Control

Mario Castro et al. J Asthma Allergy. .

Abstract

Purpose: 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.

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Conflict of interest statement

Mario Castro reports grants/research support from the NIH, ALA, PCORI, AstraZeneca, Gala Therapeutics, Genentech, GSK, Novartis, Pioneering Medicines, Pulmatrix, Sanofi-Aventis, Shionogi, and Theravance; consulting fees/honoraria from Allakos, Amgen, Arrowhead, AstraZeneca, Genentech, GSK, Merck, Novartis, OM Pharma, Regeneron, Sanofi, and Teva; and royalties from Aer Therapeutics and Elsevier. Bhaskar Dutta was an employee of AstraZeneca when this study was conducted. All other authors are employees of AstraZeneca. Annika Rutgersson and Magnus Jörntén-Karlsson are currently engaged by Evinova, a separate healthtech business within the AstraZeneca group, launched in 2023. The authors report no other conflicts of interest in this work.

Figures

Figure 1
Figure 1
Patient triggers survey: triggers of asthma episodes cited by patients.
Figure 2
Figure 2
Patient triggers survey: timing of trigger identification.
Figure 3
Figure 3
Patient triggers survey: patient behavior on receiving alerts from a predictive tool.
Figure 4
Figure 4
A risk–benefit analysis of sensory and environmental triggers to identify measurable parameters predictive of changes in asthma control (predictive power versus ease of collection).
Figure 5
Figure 5
Measurable parameters of the iPREDICT system.
Figure 6
Figure 6
Device usability testing: data properties for (A) missing data (smart band activity tracker), (B) background noise (wearable activity monitor), and (C) data out of physiologic range (wireless biometric patch).
Figure 7
Figure 7
Data properties of devices. (A) Missing measurement for respiratory rate observed at multiples of 10 for wireless biometric patch. (B) Missing activity and HR measurements observed for heart rate monitor.
See this image and copyright information in PMC

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