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

Data de publicação: 01/07/2023 Data Ahead of Print: 01/07/2023

Autores da FMUP

Bernardo Manuel De Sousa Pinto

Autor
Rita Da Silva Amaral

Autor
Ana Isabel Alves De Sá E Sousa

Autor
João De Almeida Lopes Da Fonseca

Autor

Participantes de fora da FMUP

Bousquet, J
Anto, JM
Brussino, L
Canonica, GW
Cruz, AA
Gemicioglu, B
Haahtela, T
Kupczyk, M
Kvedariene, V
Larenas Linnemann, DE
Louis, R
Pham Thi, N
Puggioni, F
Regateiro, FS
Romantowski, J
Sastre, J
Scichilone, N
Taborda Barata, L
Ventura, MT
Agache, I
Bedbrook, A
Bergmann, KC
Bosnic Anticevich, S
Bonini, M
Boulet, LP
Brusselle, G
Buhl, R
Cecchi, L
Charpin, D
Chaves Loureiro, C
Czarlewski, W
de Blay, F
Devillier, P
Joos, G
Jutel, M
Klimek, L
Kuna, P
Laune, D
Pech, JL
Makela, M
Morais Almeida, M
Nadif, R
Niedoszytko, M
Ohta, K
Papadopoulos, NG
Papi, A
Yeverino, DR
Roche, N
Samolinski, B
Shamji, MH
Sheikh, A
Ulrik, CS
Usmani, OS
Valiulis, A
Vandenplas, O
Yorgancioglu, A
Zuberbier, T

Unidades de investigação

Laboratório Associado RISE- Rede de Investigação em Saúde

Investigador

Fernando Carlos De Landér Schmitt
Medicina da Comunidade, Informação e Decisão em Saúde
RISE-Health

Investigador

Fernando Carlos De Landér Schmitt

Abstract

Background: The self-reporting of asthma frequently leads to patient misidentification in epi-demiological 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 & REG; 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 pat-terns 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 & REG; users. The main clustering approach resulted in the identification of seven groups. These groups were interpreted as probable: (i) severe/uncon-trolled asthma despite treatment (11.9-16.1% of MASK-air & REG; users); (ii) treated and partly-con-trolled 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 classi-fication 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 epidemio-logical approaches in identifying patients with asthma. & COPY; 2022 Sociedade Portuguesa de Pneumologia. Published by Elsevier Espana, S.L.U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Dados da publicação

ISSN/ISSNe:: 2531-0429, 2531-0437
Tipo:: Article
Páginas:: 292-305
DOI:: 10.1016/j.pulmoe.2022.10.005
Link para outro recurso:: www.scopus.com

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Keywords

Asthma; Rhinitis; Cluster analysis; Treatment; Control