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 J. 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, Ricardo Monti, Mario Morais-Almeida, Ralph Mösges, Marek Niedoszytko, Nikolas G. Papadopoulos, Vincenzo Patella, Nhân Pham-Thi, Frederico S. Regateiro, Sietze Reitsma, Philip W. Rouadi, Boleslaw Samolinski, Aziz Sheikh, Milan Sova, Ana Todo-Bom, Luis Taborda-Barata, Sanna Toppila-Salmi, Joaquin Sastre, Ioanna Tsiligianni, Arunas Valiulis, Olivier Vandenplas, Dana Wallace, Susan Waserman, Arzu Yorgancioglu, Mihaela Zidarn, Torsten Zuberbier, Joao A. Fonseca, Jean Bousquet

Research output: Contribution to journalArticlepeer-review

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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 the 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 the weeks for which patients had answered a rhinitis daily questionnaire on all 7 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 the latent class mixture model to assess the robustness of results. Results: We analyzed 113,239 days (16,177 complete weeks) from 2590 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 a higher frequency of rhinitis co-medication, a 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
Pages (from-to)968-983
Number of pages16
JournalAllergy: European Journal of Allergy and Clinical Immunology
Volume78
Issue number4
DOIs
Publication statusPublished - Apr 2023
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • mobile health
  • patient-reported outcomes
  • real-world data
  • rhinitis

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