Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app
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 us...
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| Published in: | Pediatric pulmonology Vol. 29; no. 4; pp. 292 - 305 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Spain
Elsevier España, S.L.U
01.07.2023
Wiley Elsevier Espana S.L.U Taylor & Francis Group |
| Subjects: | |
| ISSN: | 2531-0437, 2531-0429, 8755-6863, 2531-0437, 1099-0496 |
| Online Access: | Get full text |
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| Abstract | 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.
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.
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.
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. |
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| AbstractList | Background 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. 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. 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. 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. 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. 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.BACKGROUNDThe 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.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.METHODSWe 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.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.FINDINGSWe 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.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.INTERPRETATIONWe 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. AbstractBackgroundThe 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. MethodsWe 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. FindingsWe 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. InterpretationWe 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. Background: 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 |
| Author | Buhl, R. Bousquet, J. Roche, N. Cruz, A.A. Valiulis, A. Cecchi, L. Brussino, L. Morais-Almeida, M. Samolinski, B. Puggioni, F. Bedbrook, A. Jutel, M. Laune, D. Kupczyk, M. Romantowski, J. Haahtela, T. Nadif, R. Boulet, L.-P. Louis, R. Taborda-Barata, L. Sheikh, A. Bergmann, K.C. de Blay, F. Gemicioglu, B. Klimek, L. Pham-Thi, N. Bosnic-Anticevich, S. Sastre, J. Chaves-Loureiro, C. Pech, J.L. Ohta, K. Zuberbier, T. Anto, J.M. Bonini, M. Brusselle, G. Czarlewski, W. Vandenplas, O. Regateiro, F.S. Fonseca, J.A. Canonica, G.W. Charpin, D. Amaral, R. Ventura, M.T. Papadopoulos, N.G. Sousa-Pinto, B. Yorgancioglu, A. Makela, M. Kvedariene, V. Kuna, P. Papi, A. Sá-Sousa, A. Niedoszytko, M. Shamji, M.H. Suppli Ulrik, C. Usmani, O.S. Scichilone, N. Yeverino, D.R. Larenas-Linnemann, D.E. Joos, G. Devillier, P. Agache, I. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36428213$$D View this record in MEDLINE/PubMed https://hal.science/hal-04260452$$DView record in HAL |
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| ContentType | Journal Article |
| Copyright | 2022 Sociedade Portuguesa de Pneumologia Sociedade Portuguesa de Pneumologia Copyright © 2022 Sociedade Portuguesa de Pneumologia. Published by Elsevier España, S.L.U. All rights reserved. Distributed under a Creative Commons Attribution 4.0 International License |
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| References_xml | – volume: 73 start-page: 1622 year: 2018 end-page: 1631 ident: bib0013 article-title: Daily allergic multimorbidity in rhinitis using mobile technology: a novel concept of the MASK study publication-title: Allergy – volume: 11 start-page: e12062 year: 2021 ident: bib0024 article-title: Validity, reliability, and responsiveness of daily monitoring visual analog scales in MASK-air(R) publication-title: Clin Transl Allergy – volume: 28 start-page: 409 year: 1999 end-page: 417 ident: bib0002 article-title: Comparison of self-report data and medical records data: results from a case-control study on prostate cancer publication-title: Int J Epidemiol – volume: 15 start-page: e20 year: 2019 end-page: e27 ident: bib0004 article-title: Over- and under-diagnosis in asthma publication-title: Breathe – volume: 61 start-page: 897 year: 2016 end-page: 901 ident: bib0003 article-title: Perception of exercise-induced bronchoconstriction in college athletes publication-title: Respir Care – volume: 36 start-page: 1019 year: 2019 end-page: 1031 ident: bib0018 article-title: [Adaptation of the general data protection regulation (GDPR) to a smartphone app for rhinitis and asthma (MASK-air(R))] publication-title: Rev Mal Respir – volume: 77 start-page: 1600 year: 2022 end-page: 1602 ident: bib0021 article-title: Patient-reported outcome measures (PROMs) using the MASK-air(R) app in severe asthma publication-title: Allergy – volume: 10 start-page: 14999 year: 2020 ident: bib0012 article-title: A data-driven typology of asthma medication adherence using cluster analysis publication-title: Sci Rep – volume: 198 start-page: 1012 year: 2018 end-page: 1020 ident: bib0005 article-title: Underdiagnosis and overdiagnosis of asthma publication-title: Am J Respir Crit Care Med – volume: 2 start-page: 16 year: 2012 ident: bib0016 article-title: Control of allergic rhinitis and asthma test (CARAT) can be used to assess individual patients over time publication-title: Clin Transl Allergy – volume: 10 start-page: 62 year: 2020 ident: bib0020 article-title: Treatment of allergic rhinitis during and outside the pollen season using mobile technology. A MASK study publication-title: Clin Transl Allergy – volume: 74 start-page: 698 year: 2019 end-page: 708 ident: bib0010 article-title: Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes publication-title: Allergy – volume: 51 start-page: 956 year: 2014 end-page: 963 ident: bib0007 article-title: Reliability in reporting asthma history and age at asthma onset publication-title: J Asthma – volume: 199 start-page: 1358 year: 2019 end-page: 1367 ident: bib0011 article-title: Multiview cluster analysis identifies variable corticosteroid response phenotypes in severe asthma publication-title: Am J Respir Crit Care Med – reference: GINA report 2021. – volume: 65 start-page: 1042 year: 2010 end-page: 1048 ident: bib0019 article-title: Validation of a questionnaire (CARAT10) to assess rhinitis and asthma in patients with asthma publication-title: Allergy – volume: 74 start-page: 953 year: 2019 end-page: 963 ident: bib0009 article-title: Data-driven adult asthma phenotypes based on clinical characteristics are associated with asthma outcomes twenty years later publication-title: Allergy – volume: 73 start-page: 77 year: 2017 end-page: 92 ident: bib0015 article-title: Transfer of innovation on allergic rhinitis and asthma multimorbidity in the elderly (MACVIA-ARIA) - reference site twinning (EIP on AHA) publication-title: Allergy – volume: 178 start-page: 218 year: 2008 end-page: 224 ident: bib0008 article-title: Cluster analysis and clinical asthma phenotypes publication-title: Am J Respir Crit Care Med – volume: 75 start-page: 3248 year: 2020 end-page: 3260 ident: bib0025 article-title: A novel whole blood gene expression signature for asthma, dermatitis, and rhinitis multimorbidity in children and adolescents publication-title: Allergy – volume: 59 start-page: 90 year: 2006 end-page: 93 ident: bib0006 article-title: Self-reported asthma was biased in relation to disease severity while reported year of asthma onset was accurate publication-title: J Clin Epidemiol – volume: 49 start-page: 442 year: 2019 end-page: 460 ident: bib0014 article-title: Adherence to treatment in allergic rhinitis using mobile technology. The MASK study publication-title: Clin Exp Allergy – volume: 75 start-page: 2958 year: 2020 end-page: 2961 ident: bib0022 article-title: Validation of the MASK-air App for assessment of allergic rhinitis publication-title: Allergy – volume: 10 start-page: 343 year: 2022 end-page: 345 ident: bib0023 article-title: Assessment of the control of allergic rhinitis and asthma test (CARAT) using MASK-air publication-title: J Allergy Clin Immunol Pract – reference: . 2021. – volume: 9 start-page: 211 year: 2016 end-page: 217 ident: bib0001 article-title: Information bias in health research: definition, pitfalls, and adjustment methods publication-title: J Multidiscip Healthc – ident: e_1_3_2_21_1 doi: 10.1186/s13601-020-00342-x – ident: e_1_3_2_3_1 doi: 10.1093/ije/28.3.409 – ident: e_1_3_2_10_1 doi: 10.1111/all.13697 – ident: e_1_3_2_13_1 doi: 10.1038/s41598-020-72060-0 – ident: e_1_3_2_12_1 doi: 10.1164/rccm.201808-1543OC – ident: e_1_3_2_17_1 doi: 10.1186/2045-7022-2-16 – ident: e_1_3_2_14_1 doi: 10.1111/all.13448 – ident: e_1_3_2_5_1 doi: 10.1183/20734735.0362-2018 – ident: e_1_3_2_8_1 doi: 10.3109/02770903.2014.930480 – ident: e_1_3_2_19_1 doi: 10.1016/j.rmr.2019.08.003 – ident: e_1_3_2_16_1 doi: 10.1111/all.13218 – ident: e_1_3_2_18_1 – ident: e_1_3_2_25_1 doi: 10.1002/clt2.12062 – ident: e_1_3_2_9_1 doi: 10.1164/rccm.200711-1754OC – ident: e_1_3_2_6_1 doi: 10.1164/rccm.201804-0682CI – ident: e_1_3_2_4_1 doi: 10.4187/respcare.04553 – ident: e_1_3_2_20_1 doi: 10.1111/j.1398-9995.2009.02310.x – ident: e_1_3_2_22_1 doi: 10.1111/all.15248 – ident: e_1_3_2_23_1 doi: 10.1111/all.14415 – ident: e_1_3_2_2_1 doi: 10.2147/JMDH.S104807 – ident: e_1_3_2_11_1 doi: 10.1111/all.13670 – ident: e_1_3_2_26_1 doi: 10.1111/all.14314 – ident: e_1_3_2_15_1 doi: 10.1111/cea.13333 – ident: e_1_3_2_24_1 doi: 10.1016/j.jaip.2021.09.012 – ident: e_1_3_2_7_1 doi: 10.1016/j.jclinepi.2005.03.019 |
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| SubjectTerms | Asthma Asthma - diagnosis Asthma - epidemiology Cardiovascular & respiratory systems Cluster analysis Control Human health sciences Humanities and Social Sciences Humans Internal Medicine Life Sciences Mobile Applications Pulmonary/Respiratory Research Design Rhinitis Rhinitis, Allergic - diagnosis Rhinitis, Allergic - epidemiology Sciences de la santé humaine Systèmes cardiovasculaire & respiratoire Treatment |
| Title | Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app |
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