Browsing by Author "Sá-Sousa, A"

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    Comparison of rhinitis treatments using MASK-air® data and considering the minimal important difference
    Sousa-Pinto, B; Schünemann, HJ; Sá-Sousa, A; Vieira, RJ; Amaral, R; Anto, JM; Klimek, L; Czarlewski, W; Mullol, J; Pfaar, O; Bedbrook, A; Brussino, L; Kvedariene, V; Larenas-Linnemann, D; Okamoto, Y; Ventura, MT; Agache, I; Ansotegui, IJ; Bergmann, KC; Bosnic-Anticevich, S; Brozek, J; Canonica, GW; Cardona, V; Carreiro-Martins, P; Casale, T; Cecchi, L; Chivato, T; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; Del Giacco, S; Devillier, P; Eklund, P; Fokkens, WJ; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Ispayeva, Z; Jutel, M; Kuna, P; Kaidashev, I; Khaitov, M; Kraxner, H; Laune, D; Lipworth, B; Louis, R; Makris, M; Monti, R; Morais-Almeida, M; Mösges, R; Niedoszytko, M; Papadopoulos, NG; Patella, V; Pham-Thi, N; Regateiro, FS; Reitsma, S; Rouadi, PW; Samolinski, B; Sheikh, A; Sova, M; Todo-Bom, A; Taborda-Barata, L; Toppila-Salmi, S; Sastre, J; Tsiligianni, I; Valiulis, A; Vandenplas, O; Wallace, D; Waserman, S; Yorgancioglu, A; Zidarn, M; Zuberbier, T; Fonseca, JA; Bousquet, J
    Background Different treatments exist for allergic rhinitis (AR), including pharmacotherapy and allergen immunotherapy (AIT), but they have not been compared using direct patient data (i.e., real-world data). We aimed to compare AR pharmacological treatments on (i) daily symptoms, (ii) frequency of use in co-medication, (iii) visual analogue scales (VASs) on allergy symptom control considering the minimal important difference (MID) and (iv) the effect of AIT. Methods We assessed the MASK-air (R) app data (May 2015-December 2020) by users self-reporting AR (16-90 years). We compared eight AR medication schemes on reported VAS of allergy symptoms, clustering data by the patient and controlling for confounding factors. We compared (i) allergy symptoms between patients with and without AIT and (ii) different drug classes used in co-medication. Results We analysed 269,837 days from 10,860 users. Most days (52.7%) involved medication use. Median VAS levels were significantly higher in co-medication than in monotherapy (including the fixed combination azelastine-fluticasone) schemes. In adjusted models, azelastine-fluticasone was associated with lower average VAS global allergy symptoms than all other medication schemes, while the contrary was observed for oral corticosteroids. AIT was associated with a decrease in allergy symptoms in some medication schemes. A difference larger than the MID compared to no treatment was observed for oral steroids. Azelastine-fluticasone was the drug class with the lowest chance of being used in co-medication (adjusted OR = 0.75; 95% CI = 0.71-0.80). Conclusion Median VAS levels were higher in co-medication than in monotherapy. Patients with more severe symptoms report a higher treatment, which is currently not reflected in guidelines.
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    Consistent trajectories of rhinitis control and treatment in 16,177 weeks: The MASK-air® longitudinal study
    Sousa-Pinto, B; Schünemann, HJ; Sá-Sousa, A; Vieira, RJ; Amaral, R; Anto, JM; Klimek, L; Czarlewski, W; Mullol, J; Pfaar, O; Bedbrook, A; Brussino, L; Kvedariene, V; Larenas-Linnemann, DE; Okamoto, Y; Ventura, MT; Agache, I; Ansotegui, IJ; Bergmann, KC; Bosnic-Anticevich, S; Canonica, GW; Cardona, V; Carreiro-Martins, P; Casale, T; Cecchi, L; Chivato, T; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; Del Giacco, S; Devillier, P; Eklund, P; Fokkens, WJ; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Ispayeva, Z; Jutel, M; Kuna, P; Kaidashev, I; Khaitov, M; Kraxner, H; Laune, D; Lipworth, B; Louis, R; Makris, M; Monti, R; Morais-Almeida, M; Mösges, R; Niedoszytko, M; Papadopoulos, NG; Patella, V; Nhan, PT; Regateiro, FS; Reitsma, S; Rouadi, PW; Samolinski, B; Sheikh, A; Sova, M; Todo-Bom, A; Taborda-Barata, L; Toppila-Salmi, S; Sastre, J; Tsiligianni, I; Valiulis, A; Vandenplas, O; Wallace, D; Waserman, S; Yorgancioglu, A; Zidarn, M; Zuberbier, T; Fonseca, JA; Bousquet, J
    Introduction: Data from mHealth apps can provide valuable information on rhinitis control and treatment patterns. However, in MASK-air (R), these data have only been analyzed cross-sectionally, without considering the changes of symptoms over time. We analyzed data from MASK-air (R) longitudinally, clustering weeks according to reported rhinitis symptoms. Methods: We analyzed MASK-air (R) data, assessing the weeks for which patients had answered a rhinitis daily questionnaire on all 7days. 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. [GRAPHICS] .
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    Allergen immunotherapy in MASK-air users in real-life: Results of a Bayesian mixed-effects model
    Sousa-Pinto, B; Azevedo, LF; Sá-Sousa, A; Vieira, RJ; Amaral, R; Klimek, L; Czarlewski, W; Anto, JM; Bedbrook, A; Kvedariene, V; Ventura, MT; Ansotegui, IJ; Bergmann, KC; Brussino, L; Canonica, GW; Cardona, V; Carreiro-Martins, P; Casale, T; Cecchi, L; Chivato, T; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; De Feo, G; Devillier, P; Fokkens, WJ; Gaga, M; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Ispayeva, Z; Jutel, M; Kuna, P; Kaidashev, I; Kraxner, H; Larenas-Linnemann, DE; Laune, D; Lipworth, B; Louis, R; Makris, M; Monti, R; Morais-Almeida, M; Mösges, R; Mullol, J; Odemyr, M; Okamoto, Y; Papadopoulos, NG; Patella, V; Nhân, PT; Regateiro, FS; Reitsma, S; Rouadi, PW; Samolinski, B; Sova, M; Todo-Bom, A; Taborda-Barata, L; Tomazic, PV; Toppila-Salmi, S; Sastre, J; Tsiligianni, I; Valiulis, A; Wallace, D; Waserman, S; Yorgancioglu, A; Zidarn, M; Zuberbier, T; Fonseca, JA; Bousquet, J; Pfaar, O
    Background Evidence regarding the effectiveness of allergen immunotherapy (AIT) on allergic rhinitis has been provided mostly by randomised controlled trials, with little data from real-life studies. Objective To compare the reported control of allergic rhinitis symptoms in three groups of users of the MASK-air(R) app: those receiving sublingual AIT (SLIT), those receiving subcutaneous AIT (SCIT), and those receiving no AIT. Methods We assessed the MASK-air(R) data of European users with self-reported grass pollen allergy, comparing the data reported by patients receiving SLIT, SCIT and no AIT. Outcome variables included the daily impact of allergy symptoms globally and on work (measured by visual analogue scales-VASs), and a combined symptom-medication score (CSMS). We applied Bayesian mixed-effects models, with clustering by patient, country and pollen season. Results We analysed a total of 42,756 days from 1,093 grass allergy patients, including 18,479 days of users under AIT. Compared to no AIT, SCIT was associated with similar VAS levels and CSMS. Compared to no AIT, SLIT-tablet was associated with lower values of VAS global allergy symptoms (average difference = 7.5 units out of 100; 95% credible interval [95%CrI] = -12.1;-2.8), lower VAS Work (average difference = 5.0; 95%CrI = -8.5;-1.5), and a lower CSMS (average difference = 3.7; 95%CrI = -9.3;2.2). When compared to SCIT, SLIT-tablet was associated with lower VAS global allergy symptoms (average difference = 10.2; 95%CrI = -17.2;-2.8), lower VAS Work (average difference = 7.8; 95%CrI = -15.1;0.2), and a lower CSMS (average difference = 9.3; 95%CrI = -18.5;0.2). Conclusion In patients with grass pollen allergy, SLIT-tablet, when compared to no AIT and to SCIT, is associated with lower reported symptom severity. Future longitudinal studies following internationally-harmonised standards for performing and reporting real-world data in AIT are needed to better understand its 'real-world' effectiveness.
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    Behavioural patterns in allergic rhinitis medication in Europe: A study using MASK-air(R) real-world data
    Sousa-Pinto, B; Sá-Sousa, A; Vieira, RJ; Amaral, R; Klimek, L; Czarlewski, W; Antó, JM; Pfaar, O; Bedbrook, A; Kvedariene, V; Ventura, MT; Ansotegui, IJ; Bergmann, KC; Brussino, L; Canonica, GW; Cardona, V; Carreiro-Martins, P; Casale, T; Cecchi, L; Chivato, T; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; De Feo, G; Devillier, P; Fokkens, WJ; Gaga, M; Gemicioglu, B; Haahtela, T; Ivancevich, JC; Ispayeva, Z; Jutel, M; Kuna, P; Kaidashev, I; Kraxner, H; Larenas-Linnemann, DE; Laune, D; Lipworth, B; Louis, R; Makris, M; Monti, R; Morais-Almeida, M; Mösges, R; Mullol, J; Odemyr, M; Okamoto, Y; Papadopoulos, NG; Patella, V; Pham-Thi, N; Regateiro, FS; Reitsma, S; Rouadi, PW; Samolinski, B; Sova, M; Todo-Bom, A; Taborda-Barata, L; Tomazic, PV; Toppila-Salmi, S; Sastre, J; Tsiligianni, I; Valiulis, A; Vandenplas, O; Wallace, D; Waserman, S; Yorgancioglu, A; Zidarn, M; Zuberbier, T; Fonseca, JA; Bousquet, J
    Background Co-medication is common among patients with allergic rhinitis (AR), but its dimension and patterns are unknown. This is particularly relevant since AR is understood differently across European countries, as reflected by rhinitis-related search patterns in Google Trends. This study aims to assess AR co-medication and its regional patterns in Europe, using real-world data. Methods We analysed 2015-2020 MASK-air(R) European data. We compared days under no medication, monotherapy and co-medication using the visual analogue scale (VAS) levels for overall allergic symptoms ('VAS Global Symptoms') and impact of AR on work. We assessed the monthly use of different medication schemes, performing separate analyses by region (defined geographically or by Google Trends patterns). We estimated the average number of different drugs reported per patient within 1 year. Results We analysed 222,024 days (13,122 users), including 63,887 days (28.8%) under monotherapy and 38,315 (17.3%) under co-medication. The median 'VAS Global Symptoms' was 7 for no medication days, 14 for monotherapy and 21 for co-medication (p < .001). Medication use peaked during the spring, with similar patterns across different European regions (defined geographically or by Google Trends). Oral H-1-antihistamines were the most common medication in single and co-medication. Each patient reported using an annual average of 2.7 drugs, with 80% reporting two or more. Conclusions Allergic rhinitis medication patterns are similar across European regions. One third of treatment days involved co-medication. These findings suggest that patients treat themselves according to their symptoms (irrespective of how they understand AR) and that co-medication use is driven by symptom severity.
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    Relevance of individual bronchial symptoms for asthma diagnosis and control in patients with rhinitis: A MASK-air study
    Sousa-Pinto, B; Louis, G; Vieira, RJ; Czarlewski, W; Anto, JM; Amaral, R; Sá-Sousa, A; Brussino, L; Canonica, GW; Loureiro, CC; Cruz, AA; Gemicioglu, B; Haahtela, T; Kupczyk, M; Kvedariene, V; Larenas-Linnemann, DE; Pham-Thi, N; Puggioni, F; Regateiro, FS; Romantowski, J; Sastre, J; Scichilone, N; Taborda-Barata, L; Ventura, MT; Agache, I; Bedbrook, A; Benfante, A; Bergmann, KC; Bosnic-Anticevich, S; Bonini, M; Boulet, LP; Brusselle, G; Buhl, R; Cecchi, L; Charpin, D; Costa, EM; Del Giacco, S; Jutel, M; Klimek, L; Kuna, P; Laune, D; Makela, M; Morais-Almeida, M; Nadif, R; Niedoszytko, M; Papadopoulos, NG; Papi, A; Pfaar, O; Rivero-Yeverino, D; Roche, N; Samolinski, B; Shamji, MH; Sheikh, A; Ulrik, CS; Usmani, OS; Valiulis, A; Yorgancioglu, A; Zuberbier, T; Fonseca, JA; Pétré, B; Louis, R; Bousquet, J
    RationaleIt is unclear how each individual asthma symptom is associated with asthma diagnosis or control.ObjectivesTo assess the performance of individual asthma symptoms in the identification of patients with asthma and their association with asthma control.MethodsIn this cross-sectional study, we assessed real-world data using the MASK-air (R) app. We compared the frequency of occurrence of five asthma symptoms (dyspnea, wheezing, chest tightness, fatigue and night symptoms, as assessed by the Control of Allergic Rhinitis and Asthma Test [CARAT] questionnaire) in patients with probable, possible or no current asthma. We calculated the sensitivity, specificity and predictive values of each symptom, and assessed the association between each symptom and asthma control (measured using the e-DASTHMA score). Results were validated in a sample of patients with a physician-established diagnosis of asthma.Measurement and Main ResultsWe included 951 patients (2153 CARAT assessments), with 468 having probable asthma, 166 possible asthma and 317 no evidence of asthma. Wheezing displayed the highest specificity (90.5%) and positive predictive value (90.8%). In patients with probable asthma, dyspnea and chest tightness were more strongly associated with asthma control than other symptoms. Dyspnea was the symptom with the highest sensitivity (76.1%) and the one consistently associated with the control of asthma as assessed by e-DASTHMA. Consistent results were observed when assessing patients with a physician-made diagnosis of asthma.ConclusionsWheezing and chest tightness were the asthma symptoms with the highest specificity for asthma diagnosis, while dyspnea displayed the highest sensitivity and strongest association with asthma control.
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    Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app
    Bousquet, J; Sousa-Pinto, B; Anto, JM; Amaral, R; 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; Sá-Sousa, A; Samolinski, B; Shamji, MH; Sheikh, A; Ulrik, CS; Usmani, OS; Valiulis, A; Vandenplas, O; Yorgancioglu, A; Zuberbier, T; Fonseca, JA
    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.MethodsWe studied MASK-air (R) 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 (R) 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 (R) 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.
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    Development and validation of an electronic daily control score for asthma (e-DASTHMA): a real-world direct patient data study
    Sousa-Pinto, B; Jácome, C; Pereira, AM; Regateiro, FS; Almeida, R; Czarlewski, W; Kulus, M; Shamji, MH; Boulet, LP; Bonini, M; Brussino, L; Canonica, GW; Cruz, AA; Gemicioglu, B; Haahtela, T; Kupczyk, M; Kvedariene, V; Larenas-Linnemann, D; Louis, R; Niedoszytko, M; Nhan, PT; Puggioni, F; Romantowski, J; Sastre, J; Scichilone, N; Taborda-Barata, L; Ventura, MT; Vieira, RJ; Agache, I; Bedbrook, A; Bergmann, KC; Amaral, R; Azevedo, LF; Bosnic-Anticevich, S; Brusselle, G; Buhl, R; Cecchi, L; Charpin, D; Loureiro, CC; de Blay, F; Del Giacco, S; Devillier, P; Jassem, E; Joos, G; Jutel, M; Klimek, L; Kuna, P; Laune, D; Pech, JL; Makela, M; Morais-Almeida, M; Nadif, R; Neffen, HE; Ohta, K; Papadopoulos, NG; Papi, A; Pétré, B; Pfaar, O; Yeverino, DR; Cordeiro, CR; Roche, N; Sá-Sousa, A; Samolinski, B; Sheikh, A; Ulrik, CS; Usmani, OS; Valiulis, A; Vandenplas, O; Vieira-Marques, P; Yorgancioglu, A; Zuberbier, T; Anto, JM; Fonseca, JA; Bousquet, J
    Background Validated questionnaires are used to assess asthma control over the past 1-4 weeks from reporting. However, they do not adequately capture asthma control in patients with fluctuating symptoms. Using the Mobile Airways Sentinel Network for airway diseases (MASK-air) app, we developed and validated an electronic daily asthma control score (e-DASTHMA). Methods We used MASK-air data (freely available to users in 27 countries) to develop and assess different daily control scores for asthma. Data-driven control scores were developed based on asthma symptoms reported by a visual analogue scale (VAS) and self-reported asthma medication use. We included the daily monitoring data from all MASK-air users aged 16-90 years (or older than 13 years to 90 years in countries with a lower age of digital consent) who had used the app in at least 3 different calendar months and had reported at least 1 day of asthma medication use. For each score, we assessed construct validity, test-retest reliability, responsiveness, and accuracy. We used VASs on dyspnoea and work disturbance, EQ-5D-VAS, Control of Allergic Rhinitis and Asthma Test (CARAT), CARAT asthma, and Work Productivity and Activity Impairment: Allergy Specific (WPAI:AS) questionnaires as comparators. We performed an internal validation using MASK-air data from Jan 1 to Oct 12, 2022, and an external validation using a cohort of patients with physician-diagnosed asthma (the INSPIRERS cohort) who had had their diagnosis and control (Global Initiative for Asthma [GINA] classification) of asthma ascertained by a physician. Findings We studied 135 635 days of MASK-air data from 1662 users from May 21, 2015, to Dec 31, 2021. The scores were strongly correlated with VAS dyspnoea (Spearman correlation coefficient range 0.68-0.82) and moderately correlated with work comparators and quality-of-life-related comparators (for WPAI:AS work, we observed Spearman correlation coefficients of 0.59-0.68). They also displayed high test-retest reliability (intraclass correlation coefficients range 0.79-0.95) and moderate-to-high responsiveness (correlation coefficient range 0.69-0.79; effect size measures range 0.57-0.99 in the comparison with VAS dyspnoea). The best-performing score displayed a strong correlation with the effect of asthma on work and school activities in the INSPIRERS cohort (Spearman correlation coefficients 0.70; 95% CI 0.61-0.78) and good accuracy for the identification of patients with uncontrolled or partly controlled asthma according to GINA (area under the receiver operating curve 0.73; 95% CI 0.68-0.78). Interpretation e-DASTHMA is a good tool for the daily assessment of asthma control. This tool can be used as an endpoint in clinical trials as well as in clinical practice to assess fluctuations in asthma control and guide treatment optimisation. Funding None. Copyright (c) 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license.
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    Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app
    Bousquet, J; Sousa-Pinto, B; Anto, JM; Amaral, R; 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; Sá-Sousa, A; Samolinski, B; Shamji, MH; Sheikh, A; Ulrik, CS; Usmani, OS; Valiulis, A; Vandenplas, O; Yorgancioglu, A; Zuberbier, T; Fonseca, JA
    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/).
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    Real-world data using mHealth apps in rhinitis, rhinosinusitis and their multimorbidities
    Sousa-Pinto, B; Anto, A; Berger, M; Dramburg, S; Pfaar, O; Klimek, L; Jutel, M; Czarlewski, W; Bedbrook, A; Valiulis, A; Agache, I; Amaral, R; Ansotegui, IJ; Bastl, K; Berger, U; Bergmann, KC; Bosnic-Anticevich, S; Braido, F; Brussino, L; Cardona, V; Casale, T; Canonica, GW; Cecchi, L; Charpin, D; Chivato, T; Chu, DK; Cingi, C; Costa, EM; Cruz, AA; Devillier, P; Durham, SR; Ebisawa, M; Fiocchi, A; Fokkens, WJ; Gemicioglu, B; Gotua, M; Guzmán, MA; Haahtela, T; Ivancevich, JC; Kuna, P; Kaidashev, I; Khaitov, M; Kvedariene, V; Larenas-Linnemann, DE; Lipworth, B; Laune, D; Matricardi, PM; Morais-Almeida, M; Mullol, J; Naclerio, R; Neffen, H; Nekam, K; Niedoszytko, M; Okamoto, Y; Papadopoulos, NG; Park, HS; Passalacqua, G; Patella, V; Pelosi, S; Nhan, PT; Popov, TA; Regateiro, FS; Reitsma, S; Rodriguez-Gonzales, M; Rosario, N; Rouadi, PW; Samolinski, B; Sá-Sousa, A; Sastre, J; Sheikh, A; Ulrik, CS; Taborda-Barata, L; Todo-Bom, A; Tomazic, PV; Toppila-Salmi, S; Tripodi, S; Tsiligianni, I; Valovirta, E; Ventura, MT; Valero, AA; Vieira, RJ; Wallace, D; Waserman, S; Williams, S; Yorgancioglu, A; Zhang, L; Zidarn, M; Zuberbier, J; Olze, H; Antó, JM; Zuberbier, T; Fonseca, JA; Bousquet, J
    Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences. eHealth includes mHealth apps, which offer the potential to redesign aspects of healthcare delivery. The capacity of apps to collect large amounts of longitudinal, real-time, real-world data enables the progression of biomedical knowledge. Apps for rhinitis and rhinosinusitis were searched for in the Google Play and Apple App stores, via an automatic market research tool recently developed using JavaScript. Over 1500 apps for allergic rhinitis and rhinosinusitis were identified, some dealing with multimorbidity. However, only six apps for rhinitis (AirRater, AllergyMonitor, AllerSearch, Husteblume, MASK-air and Pollen App) and one for rhinosinusitis (Galenus Health) have so far published results in the scientific literature. These apps were reviewed for their validation, discovery of novel allergy phenotypes, optimisation of identifying the pollen season, novel approaches in diagnosis and management (pharmacotherapy and allergen immunotherapy) as well as adherence to treatment. Published evidence demonstrates the potential of mobile health apps to advance in the characterisation, diagnosis and management of rhinitis and rhinosinusitis patients.
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    Adherence to inhaled corticosteroids and long-acting β2-agonists in asthma: A MASK-air study
    Sousa-Pinto, B; Louis, R; Anto, JM; Amaral, R; Sá-Sousa, A; Czarlewski, W; Brussino, L; Canonica, GW; Loureiro, CC; Cruz, AA; Gemicioglu, B; Haahtela, T; Kupczyk, M; Kvedariene, V; Larenas-Linnemann, DE; Okamoto, Y; Ollert, M; Pfaar, O; Pham-Thi, N; Puggioni, F; Regateiro, FS; Romantowski, J; Sastre, J; Scichilone, N; Taborda-Barata, L; Ventura, MT; Agache, I; Bedbrook, A; Becker, S; Bergmann, KC; Bosnic-Anticevich, S; Bonini, M; Boulet, LP; Brusselle, G; Buhl, R; Cecchi, L; Charpin, D; de Blay, F; Del Giacco, S; Ivancevich, JC; Jutel, M; Klimek, L; Kraxner, H; Kuna, P; Laune, D; Makela, M; Morais-Almeida, M; Nadif, R; Niedoszytko, M; Papadopoulos, NG; Papi, A; Patella, V; Pétré, B; Yeverino, DR; Cordeiro, CR; Roche, N; Rouadi, PW; Samolinski, B; Savouré, M; Shamji, MH; Sheikh, A; Ulrik, CS; Usmani, OS; Valiulis, A; Yorgancioglu, A; Zuberbier, T; Fonseca, JA; Costa, EM; Bousquet, J
    Introduction Adherence to controller medication is a major problem in asthma management, being difficult to assess and tackle. mHealth apps can be used to assess adherence. We aimed to assess the adherence to inhaled corticosteroids+long-acting beta 2-agonists (ICS+LABA) in users of the MASK-air((R)) app, comparing the adherence to ICS+formoterol (ICS+F) with that to ICS+other LABA. Materials and methods We analysed complete weeks of MASK-air((R)) data (2015-2022; 27 countries) from patients with self-reported asthma and ICS+LABA use. We compared patients reporting ICS+F versus ICS+other LABA on adherence levels, symptoms and symptom-medication scores. We built regression models to assess whether adherence to ICS+LABA was associated with asthma control or short-acting beta-agonist (SABA) use. Sensitivity analyses were performed considering the weeks with no more than one missing day. Results In 2598 ICS+LABA users, 621 (23.9%) reported 4824 complete weeks and 866 (33.3%) reported weeks with at most one missing day. Higher adherence (use of medication =80% of weekly days) was observed for ICS+other LABA (75.1%) when compared to ICS+F (59.3%), despite both groups displaying similar asthma control and work productivity. The ICS+other LABA group was associated with more days of SABA use than the ICS+F group (median=71.4% versus 57.1% days). Each additional weekly day of ICS+F use was associated with a 4.1% less risk in weekly SABA use (95%CI=-6.5;-1.6%;p=0.001). For ICS+other LABA, the percentage was 8.2 (95%CI=-11.6;-5.0%;p<0.001). Conclusions In asthma patients adherent to the MASK-air app, adherence to ICS+LABA was high. ICS+F users reported lower adherence but also a lower SABA use and a similar level of control.