Consistent trajectories of rhinitis control and treatment in 16,177 weeks: The MASK-air® longitudinal study

Bernardo Sousa-Pinto, Holger J Schünemann, Ana Sá-Sousa, Rafael José Vieira, Rita Amaral, Josep M Anto, Ludger Klimek, Wienczyslawa Czarlewski, Joaquim Mullol, Oliver Pfaar, Anna Bedbrook, Luisa Brussino, Violeta Kvedariene, Désirée E Larenas-Linnemann, Yoshitaka Okamoto, Maria Teresa Ventura, Ioana Agache, Ignacio J Ansotegui, Karl C Bergmann, Sinthia Bosnic-AnticevichG Walter Canonica, Victoria Cardona, Pedro Carreiro-Martins, Thomas Casale, Lorenzo Cecchi, Tomas Chivato, Derek K Chu, Cemal Cingi, Elísio M Costa, Alvaro A Cruz, Stefano Del Giacco, Philippe Devillier, Patrik Eklund, Wytske J Fokkens, Bilun Gemicioglu, Tari Haahtela, Juan Carlos Ivancevich, Zhanat Ispayeva, Marek Jutel, Piotr Kuna, Igor Kaidashev, Musa Khaitov, Helga Kraxner, Daniel Laune, Brian Lipworth, Renaud Louis, Michael Makris, Riccardo Monti, Mario Morais-Almeida, Ralph Mösges, Jean Bousquet (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Data from mHealth apps can provide valuable information on rhinitis control and treatment patterns. However, in MASK-air®, these data have only been analyzed cross-sectionally, without considering changes of symptoms over time. We analyzed data from MASK-air® longitudinally, clustering weeks according to reported rhinitis symptoms.

Methods: We analyzed MASK-air® data, assessing weeks when patients answered a rhinitis daily questionnaire on all seven days. We firstly used k-means clustering algorithms for longitudinal data to define clusters of weeks according to the trajectories of reported daily rhinitis symptoms. Clustering was applied separately for weeks when medication was reported or not. We compared obtained clusters on symptoms and rhinitis medication patterns. We then used latent class mixture model to assess robustness of results.

Results: We analyzed 113,239 days (16,177 complete weeks) from 2,590 patients (mean age±SD=39.1±13.7 years). The first clustering algorithm identified ten clusters among weeks with medication use: seven with low variability in rhinitis control during the week, and three with highly-variable control. Clusters with poorly-controlled rhinitis displayed higher frequency of rhinitis co-medication, more frequent change of medication schemes, and more pronounced seasonal patterns. Six clusters were identified in weeks when no rhinitis medication was used, displaying similar control patterns. The second clustering method provided similar results. Moreover, patients displayed consistent levels of rhinitis control, reporting several weeks with similar levels of control.

Conclusions: We identified 16 patterns of weekly rhinitis control. Co-medication and medication change schemes were common in uncontrolled weeks, reinforcing the hypothesis that patients treat themselves according to their symptoms.

Original languageEnglish
JournalAllergy
Early online date3 Nov 2022
DOIs
Publication statusE-pub ahead of print - 3 Nov 2022

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