Consistent trajectories of rhinitis control and treatment in 16,177 weeks: The MASK‐air® longitudinal study
Authors: Sousa‐Pinto Bernardo , Schünemann Holger J. , Sá‐Sousa Ana , Vieira Rafael José , Amaral Rita , Anto Josep M. , Klimek Ludger , Czarlewski Wienczyslawa , Mullol Joaquim , Pfaar Oliver , Bedbrook Anna , Brussino Luisa , Kvedariene Violeta , Larenas‐Linnemann Désirée E. , Okamoto Yoshitaka , Ventura Maria Teresa , Agache Ioana , Ansotegui Ignacio J. , Bergmann Karl C. , Bosnic‐Anticevich Sinthia , Canonica G. Walter , Cardona Victoria , Carreiro‐Martins Pedro , Casale Thomas , Cecchi Lorenzo , Chivato Tomas , Chu Derek K. , Cingi Cemal , Costa Elísio M. , Cruz Alvaro A. , Del Giacco Stefano , Devillier Philippe , Eklund Patrik , Fokkens Wytske J. , Gemicioglu Bilun , Haahtela Tari , Ivancevich Juan Carlos , Ispayeva Zhanat , Jutel Marek , Kuna Piotr , Kaidashev Igor , Khaitov Musa , Kraxner Helga , Laune Daniel , Lipworth Brian , Louis Renaud , Makris Michael , Monti Riccardo , Morais‐Almeida Mario , Mösges Ralph , Niedoszytko Marek , Papadopoulos Nikolaos G. , Patella Vincenzo , Pham‐Thi Nhân , Regateiro Frederico S. , Reitsma Sietze , Rouadi Philip W. , Samolinski Boleslaw , Sheikh Aziz , Sova Milan , Todo‐Bom Ana , Taborda‐Barata Luis , Toppila‐Salmi Sanna , Sastre Joaquin , Tsiligianni Ioanna , Valiulis Arunas , Vandenplas Olivier , Wallace Dana , Waserman Susan , Yorgancioglu Arzu , Zidarn Mihaela , Zuberbier Torsten , Fonseca Joao A. , Bousquet Jean 
Published: November 2022

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.