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

Simple item page
dc.contributor.authorSousa-Pinto B.
dc.contributor.authorSchünemann H.J.
dc.contributor.authorSá-Sousa A.
dc.contributor.authorVieira R.J.
dc.contributor.authorAmaral R.
dc.contributor.authorAnto J.M.
dc.contributor.authorKlimek L.
dc.contributor.authorCzarlewski W.
dc.contributor.authorMullol J.
dc.contributor.authorPfaar O.
dc.contributor.authorBedbrook A.
dc.contributor.authorBrussino L.
dc.contributor.authorKvedariene V.
dc.contributor.authorLarenas-Linnemann D.E.
dc.contributor.authorOkamoto Y.
dc.contributor.authorVentura M.T.
dc.contributor.authorAgache I.
dc.contributor.authorAnsotegui I.J.
dc.contributor.authorBergmann K.C.
dc.contributor.authorBosnic-Anticevich S.
dc.contributor.authorCanonica G.W.
dc.contributor.authorCardona V.
dc.contributor.authorCarreiro-Martins P.
dc.contributor.authorCasale T.
dc.contributor.authorCecchi L.
dc.contributor.authorChivato T.
dc.contributor.authorChu D.K.
dc.contributor.authorCingi C.
dc.contributor.authorCosta E.M.
dc.contributor.authorCruz A.A.
dc.contributor.authorDel Giacco S.
dc.contributor.authorDevillier P.
dc.contributor.authorEklund P.
dc.contributor.authorFokkens W.J.
dc.contributor.authorGemicioglu B.
dc.contributor.authorHaahtela T.
dc.contributor.authorIvancevich J.C.
dc.contributor.authorIspayeva Z.
dc.contributor.authorJutel M.
dc.contributor.authorKuna P.
dc.contributor.authorKaidashev I.
dc.contributor.authorKhaitov M.
dc.contributor.authorKraxner H.
dc.contributor.authorLaune D.
dc.contributor.authorLipworth B.
dc.contributor.authorLouis R.
dc.contributor.authorMakris M.
dc.contributor.authorMonti R.
dc.contributor.authorMorais-Almeida M.
dc.contributor.authorMösges R.
dc.contributor.authorNiedoszytko M.
dc.contributor.authorPapadopoulos N.G.
dc.contributor.authorPatella V.
dc.contributor.authorPham-Thi N.
dc.contributor.authorRegateiro F.S.
dc.contributor.authorReitsma S.
dc.contributor.authorRouadi P.W.
dc.contributor.authorSamolinski B.
dc.contributor.authorSheikh A.
dc.contributor.authorSova M.
dc.contributor.authorTodo-Bom A.
dc.contributor.authorTaborda-Barata L.
dc.contributor.authorToppila-Salmi S.
dc.contributor.authorSastre J.
dc.contributor.authorTsiligianni I.
dc.contributor.authorValiulis A.
dc.contributor.authorVandenplas O.
dc.contributor.authorWallace D.
dc.contributor.authorWaserman S.
dc.contributor.authorYorgancioglu A.
dc.contributor.authorZidarn M.
dc.contributor.authorZuberbier T.
dc.contributor.authorFonseca J.A.
dc.contributor.authorBousquet J.
dc.date.accessioned2024-07-22T08:02:54Z
dc.date.available2024-07-22T08:02:54Z
dc.date.issued2023
dc.description.abstractIntroduction: 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. © 2022 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.
dc.identifier.DOI-ID10.1111/all.15574
dc.identifier.issn01054538
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12049
dc.language.isoEnglish
dc.publisherJohn Wiley and Sons Inc
dc.rightsAll Open Access; Hybrid Gold Open Access
dc.subjectHumans
dc.subjectLongitudinal Studies
dc.subjectRhinitis
dc.subjectSurveys and Questionnaires
dc.subjectTelemedicine
dc.subjectantihistaminic agent
dc.subjectazelastine plus fluticasone propionate
dc.subjectadult
dc.subjectallergic rhinitis
dc.subjectArticle
dc.subjectasthma
dc.subjectclustering algorithm
dc.subjectcontrolled study
dc.subjectdata analysis
dc.subjectfemale
dc.subjecthuman
dc.subjectinformation processing
dc.subjectk means clustering
dc.subjectlongitudinal study
dc.subjectmajor clinical study
dc.subjectmale
dc.subjectpatient-reported outcome
dc.subjectquestionnaire
dc.subjectrhinitis
dc.subjectrhinoconjunctivitis
dc.subjectselection bias
dc.subjectvisual analog scale
dc.subjectquestionnaire
dc.subjectrhinitis
dc.subjecttelemedicine
dc.titleConsistent trajectories of rhinitis control and treatment in 16,177 weeks: The MASK-air® longitudinal study
dc.typeArticle

Files

Collections

Scopus Koleksiyonu