Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app

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dc.contributor.authorBousquet J.
dc.contributor.authorSousa-Pinto B.
dc.contributor.authorAnto J.M.
dc.contributor.authorAmaral R.
dc.contributor.authorBrussino L.
dc.contributor.authorCanonica G.W.
dc.contributor.authorCruz A.A.
dc.contributor.authorGemicioglu B.
dc.contributor.authorHaahtela T.
dc.contributor.authorKupczyk M.
dc.contributor.authorKvedariene V.
dc.contributor.authorLarenas-Linnemann D.E.
dc.contributor.authorLouis R.
dc.contributor.authorPham-Thi N.
dc.contributor.authorPuggioni F.
dc.contributor.authorRegateiro F.S.
dc.contributor.authorRomantowski J.
dc.contributor.authorSastre J.
dc.contributor.authorScichilone N.
dc.contributor.authorTaborda-Barata L.
dc.contributor.authorVentura M.T.
dc.contributor.authorAgache I.
dc.contributor.authorBedbrook A.
dc.contributor.authorBergmann K.C.
dc.contributor.authorBosnic-Anticevich S.
dc.contributor.authorBonini M.
dc.contributor.authorBoulet L.-P.
dc.contributor.authorBrusselle G.
dc.contributor.authorBuhl R.
dc.contributor.authorCecchi L.
dc.contributor.authorCharpin D.
dc.contributor.authorChaves-Loureiro C.
dc.contributor.authorCzarlewski W.
dc.contributor.authorde Blay F.
dc.contributor.authorDevillier P.
dc.contributor.authorJoos G.
dc.contributor.authorJutel M.
dc.contributor.authorKlimek L.
dc.contributor.authorKuna P.
dc.contributor.authorLaune D.
dc.contributor.authorPech J.L.
dc.contributor.authorMakela M.
dc.contributor.authorMorais-Almeida M.
dc.contributor.authorNadif R.
dc.contributor.authorNiedoszytko M.
dc.contributor.authorOhta K.
dc.contributor.authorPapadopoulos N.G.
dc.contributor.authorPapi A.
dc.contributor.authorYeverino D.R.
dc.contributor.authorRoche N.
dc.contributor.authorSá-Sousa A.
dc.contributor.authorSamolinski B.
dc.contributor.authorShamji M.H.
dc.contributor.authorSheikh A.
dc.contributor.authorSuppli Ulrik C.
dc.contributor.authorUsmani O.S.
dc.contributor.authorValiulis A.
dc.contributor.authorVandenplas O.
dc.contributor.authorYorgancioglu A.
dc.contributor.authorZuberbier T.
dc.contributor.authorFonseca J.A.
dc.date.accessioned2024-07-22T08:02:32Z
dc.date.available2024-07-22T08:02:32Z
dc.date.issued2023
dc.description.abstractBackground: The self-reporting of asthma frequently leads to patient misidentification in epidemiological studies. Strategies combining the triangulation of data sources may help to improve the identification of people with asthma. We aimed to combine information from the self-reporting of asthma, medication use and symptoms to identify asthma patterns in the users of an mHealth app. Methods: We studied MASK-air® users who reported their daily asthma symptoms (assessed by a 0-100 visual analogue scale – “VAS Asthma”) at least three times (either in three different months or in any period). K-means cluster analysis methods were applied to identify asthma patterns based on: (i) whether the user self-reported asthma; (ii) whether the user reported asthma medication use and (iii) VAS asthma. Clusters were compared by the number of medications used, VAS asthma levels and Control of Asthma and Allergic Rhinitis Test (CARAT) levels. Findings: We assessed a total of 8,075 MASK-air® users. The main clustering approach resulted in the identification of seven groups. These groups were interpreted as probable: (i) severe/uncontrolled asthma despite treatment (11.9-16.1% of MASK-air® users); (ii) treated and partly-controlled asthma (6.3-9.7%); (iii) treated and controlled asthma (4.6-5.5%); (iv) untreated uncontrolled asthma (18.2-20.5%); (v) untreated partly-controlled asthma (10.1-10.7%); (vi) untreated controlled asthma (6.7-8.5%) and (vii) no evidence of asthma (33.0-40.2%). This classification was validated in a study of 192 patients enrolled by physicians. Interpretation: We identified seven profiles based on the probability of having asthma and on its level of control. mHealth tools are hypothesis-generating and complement classical epidemiological approaches in identifying patients with asthma. © 2022 Sociedade Portuguesa de Pneumologia
dc.identifier.DOI-ID10.1016/j.pulmoe.2022.10.005
dc.identifier.issn25310429
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/11872
dc.language.isoEnglish
dc.publisherElsevier Espana S.L.U
dc.rightsAll Open Access; Gold Open Access
dc.subjectAsthma
dc.subjectHumans
dc.subjectMobile Applications
dc.subjectResearch Design
dc.subjectRhinitis, Allergic
dc.subjectantihistaminic agent
dc.subjectcarnitine acetyltransferase
dc.subjectcorticosteroid
dc.subjectfluticasone
dc.subjectomalizumab
dc.subjectallergic rhinitis
dc.subjectArticle
dc.subjectasthma
dc.subjectbronchoconstriction
dc.subjectcluster analysis
dc.subjectdyspnea
dc.subjectforced expiratory volume
dc.subjecthealth care facilities and services
dc.subjectlung function test
dc.subjectmedication compliance
dc.subjectpersonalized medicine
dc.subjectquestionnaire
dc.subjectsix minute walk test
dc.subjectvisual analog scale
dc.subjectallergic rhinitis
dc.subjectasthma
dc.subjecthuman
dc.subjectmethodology
dc.titleIdentification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app
dc.typeArticle

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