title: Consistent trajectories of rhinitis control and treatment in 16,177 weeks: The MASK‐air® longitudinal study
Creators: name: Sousa‐Pinto, Bernardo
name: Schünemann, Holger J.
name: Sá‐Sousa, Ana
name: Vieira, Rafael José
name: Amaral, Rita
name: Anto, Josep M.
name: Klimek, Ludger
name: Czarlewski, Wienczyslawa
name: Mullol, Joaquim
name: Pfaar, Oliver
name: Bedbrook, Anna
name: Brussino, Luisa
name: Kvedariene, Violeta
name: Larenas‐Linnemann, Désirée E.
name: Okamoto, Yoshitaka
name: Ventura, Maria Teresa
name: Agache, Ioana
name: Ansotegui, Ignacio J.
name: Bergmann, Karl C.
name: Bosnic‐Anticevich, Sinthia
name: Canonica, G. Walter
name: Cardona, Victoria
name: Carreiro‐Martins, Pedro
name: Casale, Thomas
name: Cecchi, Lorenzo
name: Chivato, Tomas
name: Chu, Derek K.
name: Cingi, Cemal
name: Costa, Elísio M.
name: Cruz, Alvaro A.
name: Del Giacco, Stefano
name: Devillier, Philippe
name: Eklund, Patrik
name: Fokkens, Wytske J.
name: Gemicioglu, Bilun
name: Haahtela, Tari
name: Ivancevich, Juan Carlos
name: Ispayeva, Zhanat
name: Jutel, Marek
name: Kuna, Piotr
name: Kaidashev, Igor
name: Khaitov, Musa
name: Kraxner, Helga
name: Laune, Daniel
name: Lipworth, Brian
name: Louis, Renaud
name: Makris, Michael
name: Monti, Riccardo
name: Morais‐Almeida, Mario
name: Mösges, Ralph
name: Niedoszytko, Marek
name: Papadopoulos, Nikolaos G.
name: Patella, Vincenzo
name: Pham‐Thi, Nhân
name: Regateiro, Frederico S.
name: Reitsma, Sietze
name: Rouadi, Philip W.
name: Samolinski, Boleslaw
name: Sheikh, Aziz
name: Sova, Milan
name: Todo‐Bom, Ana
name: Taborda‐Barata, Luis
name: Toppila‐Salmi, Sanna
name: Sastre, Joaquin
name: Tsiligianni, Ioanna
name: Valiulis, Arunas
name: Vandenplas, Olivier
name: Wallace, Dana
name: Waserman, Susan
name: Yorgancioglu, Arzu
name: Zidarn, Mihaela
name: Zuberbier, Torsten
name: Fonseca, Joao A.
name: Bousquet, Jean
Publisher: name: Charité - Universitätsmedizin Berlin
Year: 2022
Subjects: subject: mobile health
subject: patient-reported outcomes
subject: real-world data
subject: rhinitis
classificationCode: 610
subject: Medizin und Gesundheit

description: 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.