Consistent trajectories of rhinitis control and treatment in 16, 177 weeks: The MASK‐air® longitudinal study. Issue 4 (20th November 2022)

• Record Type: Journal Article
• 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;
• Is Part Of: Allergy. Volume 78:Issue 4(2023)
• Page Start: 968
• Page End: 983
• Publication Date: 2022-11-20
• Subjects: mobile health -- patient‐reported outcomes -- real‐world data -- rhinitis; Allergy -- Periodicals; 616.97
• DOI: 10.1111/all.15574
• Languages: English
• ISSNs: 0105-4538

Abstract

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. Abstract : This study analyzed MASK‐air® data longitudinally, analysing and clustering (according to VAS levels) 16, 177 complete weeks, of which 55.5% involved the use of medication. Among weeks with medication use, 10 clusters were identified: 7 with low variability in rhinitis control during the week and 3 with highly‐variable control. Six clusters were identified in weeks with no rhinitis medication use. Clusters with poorly‐controlled rhinitis displayed higher frequency of co‐medication, more frequent changes in medication schemes and more pronounced seasonal patterns.Abbreviations: Aze‐Flu, azelastine‐fluticasone; C1‐C3, clusters with low median and maximum VAS global during weeks with medication; ICS, intranasal corticosteroid; MASK‐air®, Mobile Airways Sentinel NetworK for airway diseases; NC1‐NC2, clusters with low median and maximum VAS global during weeks without medication; NPC1‐NPC2, clusters with medium VAS global during weeks without medication; NU, cluster with high VAS global during weeks without medication; NV, cluster with large differences between minimum, median, and maximum VAS global during weeks without medication; PC1‐PC2, clusters with medium VAS global during weeks with medication; U1‐U2, clusters with high VAS global during weeks with medication; V1‐V3, clusters with large differences between minimum, median, and maximum VAS global during weeks with medication; VAS, visual analog scale