# Behavioural patterns in allergic rhinitis medication in Europe: A study using MASK-air ® real-world data

Bernardo Sousa-Pinto, Ana Sá-Sousa, Rafael José Vieira, Rita Amaral, Ludger Klimek, Wienczyslawa Czarlewski, Josep Antó, Oliver Pfaar, Anna Bedbrook, Violeta Kvedariene, et al.

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To cite this version:

Bernardo Sousa-Pinto, Ana Sá-Sousa, Rafael José Vieira, Rita Amaral, Ludger Klimek, et al. Behavioural patterns in allergic rhinitis medication in Europe: A study using MASK-air ® real-world data. Allergy, 2022, 77 (9), pp.2699-2711. 10.1111/all.15275. hal-04260515

HAL Id: hal-04260515

https://hal.science/hal-04260515v1

Submitted on 22 May 2025

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Distributed under a Creative Commons Attribution 4.0 International License
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ORIGINAL ARTICLE

Rhinitis, Sinusitis, and Upper Airway Disease

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WILEY

# Behavioural patterns in allergic rhinitis medication in Europe:
A study using MASK-air® real-world data

Bernardo Sousa-Pinto<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID

Ana Sá-Sousa<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> | Rafael José Vieira<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID

Rita Amaral<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID

Ludger Klimek<fnref n="4" />,<fnref n="5" /> ID | Wienczyslawa Czarlewski<fnref n="6" />,<fnref n="7" /> ID

Josep M. Antó<fnref n="8" />,<fnref n="9" />,<fnref n="10" />,<fnref n="11" /> ID

Oliver Pfaar¹² ID | Anna Bedbrook<fnref n="7" /> ID | Violeta Kvedariene¹³ ID

Maria Teresa Ventura¹⁴ ID

Ignacio J. Ansotegui¹⁵ ID | Karl-Christian Bergmann¹⁶,¹⁷ ID

Luisa Brussino¹⁸ ID

G. Walter Canonica¹⁹,²⁰ ID | Victoria Cardona²¹ ID

Pedro Carreiro-Martins²²,²³ ID

Tomas Casale²⁴ ID | Lorenzo Cecchi²⁵ ID | Tomás Chivato²⁶ ID

Derek K. Chu²⁷ ID

Cemal Cingi²⁸ ID | Elísio M. Costa²⁹ ID | Alvaro A. Cruz³⁰ ID

Giulia De Feo³¹ ID

Philippe Devillier³² ID | Wytske J. Fokkens³³ ID | Mina Gaga³⁴ ID

Bilun Gemicioğlu³⁵ ID

Tari Haahtela³⁶ ID | Juan Carlos Ivancevich³⁷ ID | Zhanat Ispayeva³⁸ ID

Marek Jutel³⁹,⁴⁰ ID

Piotr Kuna⁴¹ ID | Igor Kaidashev⁴² ID | Helga Kraxner⁴³ ID

Désirée E. Larenas-Linnemann⁴⁴ ID

Daniel Laune⁴⁵ ID | Brian Lipworth⁴⁶ ID

Renaud Louis⁴⁷ ID

Michael Makris⁴⁸ ID | Riccardo Monti⁴⁹ ID | Mario Morais-Almeida⁵⁰ ID

Ralph Mösges⁵¹ ID

Joaquim Mullol⁵² ID | Mikaëla Odemyr⁵³ ID | Yoshitaka Okamoto⁵⁴ ID

Nikolaos G. Papadopoulos⁵⁵ ID

Vincenzo Patella⁵⁶ ID | Nhân Pham-Thi⁵⁷ ID

Frederico S. Regateiro⁵⁸ ID

Sietze Reitsma³³ ID | Philip W. Rouadi⁵⁹ ID

Boleslaw Samolinski⁶⁰ ID

Milan Sova⁶¹ ID | Ana Todo-Bom⁵⁹ ID | Luis Taborda-Barata⁶²,⁶³,⁶⁴ ID

Peter Valentin Tomazic⁶⁵ ID

Sanna Toppila-Salmi⁶⁶ ID | Joaquin Sastre⁶⁷ ID

Ioanna Tsiligianni⁶⁸ ID

Arunas Valiulis⁶⁹ ID | Olivier Vandenplas⁷⁰ ID | Dana Wallace⁷¹ ID

Susan Waserman⁷² ID

Arzu Yorgancioglu⁷³ ID | Mihaela Zidarn⁷⁴ ID | Torsten Zuberbier¹⁶,¹⁷ ID

João A. Fonseca<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID

Jean Bousquet¹⁶,¹⁷,⁷⁵ ID

<footnote n="1">¹MEDCIDS – Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal</footnote>

<footnote n="2">²CINTESIS – Center for Health Technology and Services Research, University of Porto, Porto, Portugal</footnote>

<footnote n="3">³RISE – Health Research Network; University of Porto, Porto, Portugal</footnote>

<footnote n="4">⁴Department of Otolaryngology, Head and Neck Surgery, Universitätsmedizin Mainz, Mainz, Germany</footnote>

<footnote n="5">⁵Center for Rhinology and Allergology, Wiesbaden, Germany</footnote>

<footnote n="6">⁶Medical Consulting Czarlewski, Levallois, France</footnote>

<footnote n="7">⁷MASK-air, Montpellier, France</footnote>

<footnote n="8">⁸ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain</footnote>

<footnote n="9">⁹IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain</footnote>

<footnote n="10">¹⁰Universitat Pompeu Fabra (UPF), Barcelona, Spain</footnote>

<footnote n="11">¹¹CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
© 2022 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.</footnote>
¹²Department of Otorhinolaryngology, Head and Neck Surgery, Section of Rhinology and Allergy, University Hospital Marburg, Philipps-Universität Marburg, Marburg, Germany

¹³Institute of Biomedical Sciences, Department of Pathology, Faculty of Medicine, Vilnius University and Institute of Clinical Medicine, Clinic of Chest Diseases and Allergology, Faculty of Medicine, Vilnius University, Vilnius, Lithuania

¹⁴University of Bari Medical School, Unit of Geriatric Immunoallergology, Bari, Italy

¹⁵Department of Allergy and Immunology, Hospital Quironsalud Bizkaia, Bilbao, Spain

¹⁶Institute of Allergology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

¹⁷Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany

¹⁸Department of Medical Sciences, Allergy and Clinical Immunology Unit, University of Torino & Mauriziano Hospital, Torino, Italy

¹⁹Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy

²⁰Personalized Medicine, Asthma and Allergy, Humanitas Clinical and Research Center IRCCS, Rozzano, Italy

²¹Allergy Section, Department of Internal Medicine, Hospital Vall d'Hebron & ARADYAL Research Network, Barcelona, Spain

²²Serviço de Imunoalergologia, Hospital de Dona Estefânia, Centro Hospitalar Universitário de Lisboa Central, Lisbon, Portugal

²³NOVA Medical School/Comprehensive Health Research Center (CHRC), Lisbon, Portugal

²⁴Division of Allergy/Immunology, University of South Florida, Tampa, Florida, USA

²⁵SOS Allergology and Clinical Immunology, USL Toscana Centro, Prato, Italy

²⁶School of Medicine, University CEU San Pablo, Madrid, Spain

²⁷Department of Medicine and Health Research Methods, Evidence & Impact, McMaster University, Hamilton, ON, Canada

²⁸Eskisehir Osmangazi University, Medical Faculty, ENT Department, Eskisehir, Turkey

²⁹UCIBIO, REQUINTE, Faculty of Pharmacy and Competence Center on Active and Healthy Ageing of University of Porto (Porto4Ageing), Porto, Portugal

³⁰Fundação ProAR, Federal University of Bahia and GARD/WHO Planning Group, Salvador, Brazil

³¹Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Salerno, Italy

³²VIM Suresnes, UMR_0892, Pôle des Maladies des Voies Respiratoires, Hôpital Foch, Université Paris-Saclay, Suresnes, France

³³Department of Otorhinolaryngology, Amsterdam University Medical Centres, Location AMC, Amsterdam, The Netherlands

³⁴ERS President 2017-2018, 7th Resp Med Department and Asthma Center, Athens Chest Hospital, Athens, Greece

³⁵Department of Pulmonary Diseases, Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Istanbul, Turkey

³⁶Skin and Allergy Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland

³⁷Servicio de Alergia e Immunologia, Clinica Santa Isabel, Buenos Aires, Argentina

³⁸Department of Allergology and Clinical Immunology of the Kazakh National Medical University, Almaty, Kazakhstan

³⁹Department of Clinical Immunology, Wrocław Medical University, Wrocław, Poland

⁴⁰Department of Clinical Immunology, ALL-MED Medical Research Institute, Wrocław, Poland

⁴¹Division of Internal Medicine, Asthma and Allergy, Barlicki University Hospital, Medical University of Lodz, Lodz, Poland

⁴²Poltava State Medical University, Poltava, Ukraine

⁴³Department of Otorhinolaryngology, Head and Neck Surgery, Semmelweis University, Budapest, Hungary

⁴⁴Center of Excellence in Asthma and Allergy, Médica Sur Clinical Foundation and Hospital, México City, Mexico

⁴⁵Kyomed INNOV, Montpellier, France

⁴⁶Scottish Centre for Respiratory Research, Cardiovascular & Diabetes Medicine, Medical Research Institute, Ninewells Hospital, University of Dundee, Dundee, UK

⁴⁷Department of Pulmonary Medicine, CHU Sart-Tilman, and GIGA I3 Research Group, Liege, Belgium

⁴⁸Allergy Unit "D Kalogeromitros", 2nd Department of Dermatology and Venereology, National & Kapodistrian University of Athens, "Attikon" University Hospital, Athens, Greece

⁴⁹Department of Medical Sciences, Allergy and Clinical Immunology Unit, University of Torino & Mauriziano Hospital, Torino, Italy

⁵⁰Allergy Center, CUF Descobertas Hospital, Lisbon, Portugal

⁵¹IMSB, Medical Faculty, University of Cologne, and ClinCompetence Cologne GmbH, Cologne, Germany

⁵²Rhinology Unit & Smell Clinic, ENT Department, Hospital Clínic, Clinical & Experimental Respiratory Immunoallergy, IDIBAPS, CIBERES, University of Barcelona, Barcelona, Spain

⁵³EFA European Federation of Allergy and Airways Diseases Patients' Associations, Brussels, Belgium

⁵⁴Department of Otorhinolaryngology, Chiba University Hospital, Chiba, Japan

⁵⁵Allergy Department, 2nd Pediatric Clinic, University of Athens, Athens, Greece

⁵⁶Division of Allergy and Clinical Immunology, Department of Medicine, Agency of Health ASL Salerno, "Santa Maria della Speranza" Hospital, Battipaglia, Salerno, Italy

⁵⁷Ecole Polytechnique Palaiseau, IRBA (Institut de Recherche bio-Médicale des Armées), Bretigny, France

⁵⁸Allergy and Clinical Immunology Unit, Centro Hospitalar e Universitário de Coimbra, and Institute of Immunology, Faculty of Medicine, University of Coimbra, and Coimbra Institute for Clinical and Biomedical Research (ICBR), Faculty of Medicine, University of Coimbra, Coimbra, Portugal
⁵⁹Department of Otolaryngology-Head and Neck Surgery, Eye and Ear University Hospital, Beirut, Lebanon, and ENT Department, Dar Al Shifa Hospital, Salmiya, Kuwait

⁶⁰Department of Prevention of Environmental Hazards, Allergology and Immunology, Medical University of Warsaw, Warsaw, Poland

⁶¹Department of Pulmonary medicine and tuberculosis, University Hospital Brno, Brno, Czech Republic

⁶²Faculty of Health Sciences, University of Beira Interior, Covilha, Portugal

⁶³UBIAir - Clinical & Experimental Lung Centre, University of Beira Interior, Covilha, Portugal

⁶⁴Department of Immunoallergology, Cova da Beira University Hospital Centre, Covilhã, Portugal

⁶⁵Department of General ORL, H&NS, Medical University of Graz, ENT-University Hospital Graz, Graz, Austria

⁶⁶Skin and Allergy Hospital, Helsinki University Hospital, and University of Helsinki, Helsinki, Finland

⁶⁷Fundacion Jimenez Diaz, CIBERES, Faculty of Medicine, Autonoma University of Madrid, Madrid, Spain

⁶⁸Health Planning Unit, Department of Social Medicine, Faculty of Medicine, University of Crete, Crete, Greece, and International Primary Care Respiratory Group IPCRG, Aberdeen, Scotland

⁶⁹Institute of Clinical Medicine and Institute of Health Sciences, Medical Faculty of Vilnius University, Vilnius, Lithuania

⁷⁰Department of Chest Medicine, Centre Hospitalier Universitaire UCL Namur, Université Catholique de Louvain, Yvoir, Belgium

⁷¹Nova Southeastern University, Fort Lauderdale, Florida, USA

⁷²Department of Medicine, Clinical Immunology and Allergy, McMaster University, Hamilton, Ontario, Canada

⁷³Department of Pulmonology, Celal Bayar University, Manisa, Turkey

⁷⁴University Clinic of Respiratory and Allergic Diseases, Golnick, and University of Ljubljana, Faculty of Medicine, Ljubljana, Slovenia

⁷⁵University Hospital Montpellier, Montpellier, France

**Correspondence**

Jean Bousquet, Institute of Allergology,
Charité – Universitätsmedizin Berlin,
Corporate Member of Freie Universität
Berlin and Humboldt-Universität zu Berlin,
Berlin, Germany
Email: jean.bousquet@orange.fr

**Funding information**

MASK.

**Abstract**

**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® 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 H₁-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.

**KEYWORDS**

allergic rhinitis, Co-medication, MASK-air, visual analogue scale
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**GRAPHICAL ABSTRACT**

This study aims to assess allergic rhinitis co-medication and its regional patterns in Europe, using real-world data. Patients tend to treat themselves for allergic rhinitis according to their symptoms rather than according to guideline indications. Medication patterns in patients with allergic rhinitis appear to be similar across European regions.

**1 | INTRODUCTION**

Allergic rhinitis (AR) is the most common disease in Europe,¹ but it appears to be understood differently by individuals from different countries. An infodemiology study using Google Trends showed that different keywords defining AR (hay fever, allergy or pollen) are used differently between European countries. In fact, there are clusters of online search patterns on rhinitis-related terms, suggesting cultural differences.² Interestingly, whichever keyword is used, a clear seasonal pattern can be observed in online searches following pollen exposure.²⁻⁴

Cultural differences in AR assessment - along with differences in drug availability and cost - may be reflected in differences in medication use. A recent study showed major differences between countries in terms of rhino-conjunctivitis medication usage.⁵ Despite the existence of AR treatment guidelines, their indications appear to be insufficiently followed by patients.⁶ Patients increase their medications when needed in order to control their symptoms.⁷,⁸ However, the seasonality and patterns of co-medication have not yet been investigated. Assessing real-world patterns of medication use is, therefore, essential for drafting recommendations that are evidence-based and that take patients' needs into account.⁹

Mobile health apps can be a valuable source of real-world data. In AR, MASK-air® is an example of such an app. It comprises a daily monitoring questionnaire in which patients are requested to quantify the impact of their AR symptoms on that day, as well as to provide information on the medication used.¹⁰,¹¹ MASK-air® is available in 18 European countries, allowing for regional differences to be assessed. There are many unanswered questions that can be addressed using MASK-air® data, including the seasonal patterns of medication use in different European countries.

Therefore, in this study, we aimed to use real-world data from MASK-air® to assess AR medication patterns in Europe in order to assess whether such patterns were similar across European regions and were in accordance with AR guidelines. In particular, we assessed (i) the most commonly-used medication classes and the frequency of co-medication use, (ii) seasonal patterns of medication use and (iii) variation of medication use between European countries, grouped either by geographical region or by clusters associated with cultural differences (i.e., clusters of online search patterns of rhinitis-related terms).

**2 | METHODS**

**2.1 | Study design**

We performed a cross-sectional descriptive analysis of the different medication schemes reported to be used in the daily monitoring of MASK-air® in European regions. We described the frequency of use of each medication group, and of co-medication. To assess whether medication use followed any seasonal pattern, we assessed the monthly use of the different medication groups. We estimated the number of different drugs at the patient level throughout a period of 12 months.

**2.2 | Setting and participants**

MASK-air® has been available since 2015 and can be downloaded via the Apple App and Google Play Stores of 27 countries (www mask-air.com).¹⁰ According to methods previously described,¹²,¹³ we included the daily monitoring data from European MASK-air® users
(18 different countries) from 21 May 2015 to 6 December 2020. The users were aged 16–90 years and had a self-reported diagnosis of allergic rhinitis. There were no exclusion criteria.

Countries in Europe were grouped using two different classifications, namely (i) three different European regions (Southern, Central-West and Northern-East) according to their seasonal exposure to allergens and (ii) four groups based on a previous study using Google Trends by cultural clusters.²

## 2.3 | Ethics

MASK-air® has a CE1 marking. It follows the GDPR regulations.¹⁴ All data are anonymised (including data related to geolocation) using k-anonymity.¹⁵ An independent review board approval was not required since the study is observational, and users agreed to the analysis of their data in the terms of use.

## 2.4 | Data sources and variables

### 2.4.1 | MASK-air®

The MASK-air® daily monitoring questionnaire comprises five mandatory questions assessing the impact of AR by means of visual analogue scales (VASs) ranging from 0 to 100 (Table S1). In addition, users are asked to enter their daily medications using a regularly updated scroll list that contains country-specific prescribed and OTC medications (the International Nonproprietary Names (INN) classification is used for drug nomenclature¹⁶). This enables medications to be clustered in groups (e.g., oral H1-antihistamines—OAH, intranasal corticosteroids—INCS). When responding to the MASK-air® daily monitoring questionnaire, it is not possible to skip any of the questions, and data are saved to the data set only after the final answer. This therefore precludes any missing data.

### 2.4.2 | Selection of medications

In order to more closely follow the patients' perspectives, monotherapy was defined as days when only one single drug formulation for AR was reported, even if with more than one active compound⁷,⁸,¹³ (for example, nasal azelastine-fluticasone—AzeFlu—contains two drugs but, as it is a fixed combination, it was considered as monotherapy). Co-medication was defined as days with two or more drug formulations for AR. Asthma medications were not considered in co-medication. Allergic rhinitis medications were classified into seven groups (Table S2).

## 2.5 | Size of the study

In this study, data from all registered European users were included. No sample size calculation was performed.

## 2.6 | Biases

There are potential information biases related to the self-reported nature of data collection. Potential selection biases might be introduced because app users are not representative of all patients with AR (e.g., there may be an overrepresentation of users suffering from moderate-to-severe AR,⁸ and of younger individuals who may be more familiar with apps). Finally, it is not known whether users fill in the MASK-air® daily monitoring questionnaire before or after treatment for a given day.

## 2.7 | Analysis of the data

As previously published,⁷,⁸,¹³ we studied the full dataset of users meeting the eligibility criteria. We computed the frequency of three medication schemes: observations under no medication, monotherapy (including frequency of use of each medication group) and co-medication. For each medication group or combination, we computed the median VAS Global allergy symptoms (VAS assessing on a 0–100 scale how much allergic symptoms are bothering the patient on that day), as well as the median VAS Work (VAS assessing on a 0–100 scale the impact of allergic symptoms on work that day) and the respective confidence intervals.

To assess whether medication use followed any seasonal pattern, we analysed the monthly use of different medication groups and co-medication schemes.

To address across-Europe diversity, we performed separate analyses with data from Southern European countries (Greece, Italy, Portugal and Spain), Central-Western European countries (Belgium, France and Switzerland) and Northern-Eastern European countries (Austria, Czech Republic, Denmark, Finland, Germany, Lithuania, Netherlands, Poland, Slovenia, Sweden and the United Kingdom). In addition, we performed separate analyses with European countries grouped as defined by clusters on search patterns (assessed by Google Trends) for rhinitis and related terms (such clusters are displayed in Table S3 and had been previously defined by Bousquet et al.²).

Subsequently, we compared the frequency of the different medication groups and co-medication schemes during and outside the pollen season (as defined by Bédard et al.).⁸ Between-season differences were compared by computing Cohen's h.¹⁷ We assumed that values between 0.2 and 0.5 correspond to small effect sizes (differences), values between 0.5 and 0.8 to moderate differences and values over 0.8 to large differences.

Finally, we analysed medication schemes at the patient level, assessing the frequency of different medication groups within the period of 1 year. For this analysis, we included observations from patients who had registered medication use in at least four different months over the period of 1 year (namely over the first 12 months after starting the MASK-air® app). Observations of the remaining patients were excluded, being too scarce or too concentrated in a small amount of time, potentially biasing results.
Categorical variables were analysed using absolute and relative frequencies. Continuous variables were analysed using medians and interquartile ranges (IQR), with comparisons being performed using hierarchical regression models clustered by patient. *p*-values <.05 were considered to indicate statistically-significant associations. All analyses were performed using the software R (version 4.0.0.).

**3 | RESULTS**

We analysed 222,025 days, of which 102,202 (46.0%) involved the use of at least one medication. These days were provided by a total of 13,122 users aged 16–90 years. There were 113,401 (51.1%) observations from women, and the mean age of users was 39.3 ± 13.5 years (Figure S1). Most days were from Northern-East Europe (n = 114,488; 51.6%), followed by Southern Europe (n = 87,577; 39.4%) and Central-West Europe (n = 19,960; 9.0%) (Table S4).

**3.1 | Medication use**

On 119,823 days (54.0% of total days), no medication was used, compared to 63,887 days (28.8%) of monotherapy and 38,315 days (17.3%) of co-medication.

Monotherapy days most commonly involved the use of OAH monotherapy (34,481 days; 54.0%), INCS monotherapy (13,050 days; 20.4%) or AzeFlu (8928 days; 14.0%) (Table 1). Regarding co-medication, the use of OAH and INCS on the same day (10,974 days; 28.6%) occurred more frequently than other combinations. Frequencies of the medication groups by region are available in Table S5.

The median VAS Global allergy symptoms for co-medication days was 21 (IQR = 36) vs. 14 (IQR = 23) for monotherapy days and 7 (IQR = 22) for non-medication days (*p* < .001). A similar pattern was observed for VAS Work, with a median value of 15 (IQR = 30) for co-medication days, compared to 10 (IQR = 23) for monotherapy days and 4 (IQR = 16) for non-medication days (*p* < .001).

**3.2 | Seasonality of medication**

By analysing monthly data, we observed that the peak of medication use occurred during the spring. The percentage of days in which medication was reported reached its maximum in June (54.5%) and its minimum in January (34.9%) (Figures 1 and 2A; Figure S2). The lowest percentage of days with co-medication was registered in January (11.6%), and the highest in April (21.7%) (Figure 2). Consistent results were observed when analysing data separately (i) from the different geographical regions, (ii) from different regions as defined by the online search patterns of rhinitis-related terms (Figure S3) and (iii) from individual countries (Figure 3).

During the pollen season, the frequency of days without any medication was lower than outside the pollen season (47.0% vs. 60.3%; Cohen's *h* = 0.27) (Table S6).

**3.3 | Medication use at patient level**

A total of 975 patients were identified as having reported medications in at least 4 months over the period of 1 year (Table 2; Table S7 for results presented by region). During that period, each patient reported 2.7 ± 1.5 (mean ± standard deviation) different drugs. Two or more different drugs were reported by 779 (79.9%) patients. Approximately three-quarters of the patients (n = 729; 74.5%) reported drugs from more than one group (mean ± standard deviation: 2.1 ± 0.9). Most assessed patients (n = 709; 72.5%) reported at least 1 day with co-medication.

**4 | DISCUSSION**

In this study, we observed that in Europe, for AR treatment, (i) OAH are the most commonly used medications in monotherapy, with their use peaking in spring, (ii) co-medication is common and follows a seasonal pattern that resembles that of OAH monotherapy, (iii) the reporting of the major medication groups follows similar patterns across the different European regions (despite cultural differences reflected in online search patterns for rhinitis-related terms) and (iv) the use of different medications (of the same or of a different group) by the same patient throughout the year is common, suggesting self-medication and that patients overall do not follow guideline indications.

The first major finding of the study is the disconnection between the patients' variable perception of AR across countries and their similar therapeutic response to pollen exposure. Internet data are being increasingly integrated into health research and are becoming a useful tool for exploring human behaviour. The most popular tool for examining online behaviour is Google Trends.¹⁸,¹⁹ A previous study assessing Google Trends terms related to allergy and rhinitis in European countries (2011–2016)² identified an annual and clear seasonality of queries in most countries during spring. However, the keywords 'hay fever', 'allergy' and 'pollen' were found to demonstrate seasonality differently depending on the country, suggesting cultural differences.² In the present study, we found no major differences in medication seasonality in the different clusters of countries when considered in cultural clusters determined by Google Trends (or in geographical clusters).² One may hypothesise that the similar behaviour of MASK-air® users towards medication use across Europe during increased pollen exposure might be related to similarities in medication prescriptions. However, in a previous study, specifically designed to understand physicians' prescription patterns in different European countries, we observed that prescribed medications were significantly different between France (high INCS), Poland and Spain (medium INCS prescriptions), and Germany and
<table><caption>TABLE 1 Characteristics of observations/days under each medication scheme</caption><thead><tr><th></th><th>N observations (%)</th><th>VAS global symptoms - median [95% CI] (IQR)</th><th>VAS work - median [95% CI] (IQR)</th></tr></thead><tbody><tr><td colspan="4"><strong>Medication scheme</strong></td></tr><tr><td>No medication</td><td>119,823 (54.0)</td><td>7 [7-7] (22)<sup>a</sup></td><td>4 [4-4] (16)<sup>b</sup></td></tr><tr><td>Monotherapy</td><td>63,887 (28.8)</td><td>14 [14-14] (23)<sup>a</sup></td><td>10 [9-10] (23)<sup>b</sup></td></tr><tr><td>INAH<sup>c</sup></td><td>529 (0.2)</td><td>26 [25-28] (43)</td><td>20 [18-22] (27)</td></tr><tr><td>Ocular AH<sup>c</sup></td><td>637 (0.3)</td><td>19 [17-22] (32)</td><td>13 [10-15] (27)</td></tr><tr><td>OAH<sup>c</sup></td><td>34,481 (15.5)</td><td>16 [15-16] (31)</td><td>11 [10-11] (24)</td></tr><tr><td>INCS</td><td>13,050 (5.9)</td><td>11 [11-12] (20)</td><td>9 [9-10] (19)</td></tr><tr><td>Oral steroid</td><td>575 (0.3)</td><td>33 [31-35] (26)</td><td>22 [16-28] (39)</td></tr><tr><td>AzeFlu formulation</td><td>8928 (4.0)</td><td>13 [12-13] (23)</td><td>7 [7-8] (21)</td></tr><tr><td>Remaining classes<sup>d</sup></td><td>5687 (2.6)</td><td>15 [15-16] (28)</td><td>9 [9-10] (24)</td></tr><tr><td>Co-medication</td><td>38,315 (17.3)</td><td>21 [20-21] (36)<sup>a</sup></td><td>15 [15-15] (30)<sup>b</sup></td></tr><tr><td>Involving only OAH and INCS</td><td>10,974 (4.9)</td><td>16 [16-17] (33)</td><td>12 [11-12] (28)</td></tr><tr><td>Involving OAH, INCS and drugs of other classes</td><td>6900 (3.1)</td><td>22 [22-23] (33)</td><td>17 [16-18] (28)</td></tr><tr><td>Involving only oral OAH and AzeFlu</td><td>5813 (2.6)</td><td>17 [17-18] (25)</td><td>12 [11-13] (24)</td></tr><tr><td>Involving OAH, AzeFlu and drugs of other classes</td><td>1516 (0.7)</td><td>28 [27-29] (28)</td><td>25 [24-28] (29)</td></tr><tr><td>Involving only OAH and drugs of the 'remaining classes'd</td><td>8180 (3.7)</td><td>27 [27-27] (44)</td><td>17 [16-19] (36)</td></tr><tr><td>Involving only INCS and drugs of the 'remaining classes'd</td><td>2271 (1.0)</td><td>20 [18-21] (33)</td><td>16 [14-18] (31)</td></tr><tr><td>Involving only AzeFlu and drugs of the 'remaining classes'd</td><td>1241 (0.6)</td><td>21 [20-22] (28)</td><td>20 [19-23] (26)</td></tr><tr><td>Any co-medication scheme involving oral steroids</td><td>1540 (0.7)</td><td>45 [42-48] (45)</td><td>30 [27-35] (46)</td></tr><tr><td>Other co-medication schemes</td><td>1420 (0.6)</td><td>27 [25-29] (43)</td><td>19 [16-23] (41)</td></tr><tr><td colspan="4"><strong>Number of simultaneous medications in co-medication days</strong></td></tr><tr><td>2 medications</td><td>28,154</td><td>19 [18-19] (34)</td><td>13 [13-14] (29)</td></tr><tr><td>3 medications</td><td>8231</td><td>24 [24-25] (35)</td><td>19 [18-20] (29)</td></tr><tr><td>4 medications</td><td>1483</td><td>26 [24-29] (41)</td><td>18 [16-22] (40)</td></tr><tr><td>&gt;4 medications</td><td>447</td><td>57 [51-63] (47)</td><td>45 [35-52] (51)</td></tr></tbody></table>

Abbreviations: AH, antihistamine; AzeFlu, Azelastine-Fluticasone; CI, Confidence interval; INAH, Intranasal antihistamine; INCS, Intranasal steroid; IQR, Interquartile range; OAH, Oral antihistamine; VAS Global Symptoms, MASK-air® visual analogue scale assessing the severity of overall allergic symptoms on that day; Work VAS, MASK-air® visual analogue scale assessing the work impact of allergic symptoms on that day.

Italy (low INCS prescriptions). That is, in countries with similar reported medication use behaviour during the pollen season (as observed in this study), different prescription patterns appear to be observed, suggesting that patients may not completely follow the physicians' prescriptions.

Secondly, as proposed earlier, but better assessed in this study,⁷,⁸,²⁰ patients attempt to control their disease by increasing medications, and by self-medicating with OTC medications. In this study, with a larger number of participants, the median level of VAS Global allergy symptoms increased from no treatment to one medication and multiple medications, with the highest value being associated with the use of over 4 medications on the same day. During the course of their disease, patients often use several medications of the same group. These two results reinforce

the conclusion that patients do not follow the physicians' prescriptions and attempt to control their disease by increasing medications.⁶,²¹,²²

Thirdly, most patients do not appear to follow the recommendations stated in guidelines. In fact, guidelines indicate that a combination of OAH and INCS does not increase the efficacy of INCS. Moreover, although OAH are important in patients with mild-to-moderate disease, and control a large group of allergic patients, they are not usually very effective in more severe cases. Thus, the largest increase in OAH during the pollen season with or without INCS suggests that patients do not follow guidelines. Overall, these findings imply that, across Europe, AR medication use is not guideline-driven, but rather symptom-driven (i.e., patients appear to use, increase or try different medications when feeling worse), although medication

<footnote>a$p < .001$ for the comparison of VAS Global Symptoms between no medication, monotherapy and co-medication days.</footnote>

<footnote>b$p < .001$ for the comparison of VAS Work between no medication, monotherapy and co-medication days.</footnote>

<footnote>cThere were 35,647 observations with AH1 use of any type in monotherapy (median VAS Global Symptoms = 16; median VAS Work = 11).</footnote>

<footnote>dIncluding any non-antihistamine non-steroid formulation (namely, decongestants, mast cell stabilisers, antileukotrienes, saline solutions), or any antihistamine of unspecified route of administration, or any unspecified respiratory steroid in patients not reporting asthma.</footnote>
<figure><img src="image_6.png" /><figcaption>FIGURE 1 Monthly absolute number of observations under different medication schemes throughout the years. AH, Antihistamines; INCS, Intranasal corticosteroids</figcaption></figure>

costs and healthcare access—which we were not able to assess—may
in some cases also influence medication patterns. This finding has
important implications for clinical practice. Clinicians should have
this in mind when discussing the therapeutic management of AR
with their patients, so as to increase the chances of a more effective
therapeutic plan. Of note, the observed erratic medication pattern
does not appear to increase control. This is difficult to truly assess
with this methodology, but there is an urgent need to revise guide-
lines using real-world data in order to align the patients' needs and
behaviour with recommendations. This was recently proposed by
ARIA.⁹ Moreover, value-added medicine is needed for a change in
AR management, stressing the importance of *pro re nata* treatment
vs. long-term treatment that is almost never used by AR patients.²³

Our findings also point to unmet needs in research. For example,
trials in a pollen exposure chamber can be performed to determine
whether a subpopulation of more severely affected patients will
benefit more from co-medication or up-dosing. Furthermore, real-
life studies are needed to assess whether there is a trend of more
patients presenting with more severe symptoms. This is related to
the concept of 'severe chronic upper airways diseases' (associated
with different forms of upper airways diseases such as AR and non-
allergic rhinitis),²⁴ in which patients have impaired quality-of-life, so-
cial functioning, sleep and school/work performance, prompting the
need for a precise diagnosis. A further assessment on the influence
of asthma medication on rhinitis medication use should also be the
focus of future studies. On a preliminary analysis, despite similarities

in seasonal patterns, the use of AR medication (particularly in co-
medication) appears to be more common in days when asthma med-
ication is also used, with the latter also tending to be significantly
associated with lower VASs compared to no asthma medication days
(Table S8).

This study has limitations that are worth noting. Due to its
cross-sectional design, we are not able to establish a temporal re-
lationship between reported symptoms and medication use. That
is, we do not know whether users provide information in their daily
monitoring questionnaire on symptoms concerning only the period
prior to medication use. Considering this limitation, and the need
for specific methods addressing confounders and multiple obser-
vations reported by individual patients, assessing the effectiveness
of specific medication groups was beyond the scope of this study.
Nevertheless, future studies using such methods may be performed
aiming at comparing differences in VAS across medication groups.
Another limitation concerns the possibility of information biases,
associated not only with the fact that AR in MASK-air® has been
self-reported but also with the underreporting of medication use.
While possibly resulting in an underestimation of co-medication
days, this latter phenomenon is probably non-differential in
terms of region or season. An underestimation of medication/co-
medication use may also stem from the exclusion of cases for which
there was no information on whether steroids were used for AR or
asthma (e.g., cases for which the only information available was the
active compound of a steroid that can be used both in a nasal or
<figure><img src="image_7.png" /><figcaption>(A)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(B)</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**FIGURE 2** Monthly frequency of observations under different medication schemes (A) and co-medication schemes (B). Results are expressed as a percentage of the total number of days under medication for each geographical region. Combined data for the 2016-2020 period for each month are presented. AH, Antihistamines; INCS, Intranasal corticosteroids</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**FIGURE 3** Monthly frequency of observations under different medication schemes (left panel) and relative search volumes on 'allergy', 'allergic rhinitis' and 'pollen' (as assessed by Google Trends) (right panel) for three individual countries. AH, Antihistamines; AzeFlu, Azelastine-Fluticasone; INCS, Intranasal corticosteroids</figcaption></figure>

inhaled formulation). Also, European MASK-air® users are not fully representative of European patients with AR, posing generalisability concerns. For example, geriatric patients may need some support and training to use mobile apps, which may affect the results of the study for this age group. Finally, there are limitations related to app adherence among MASK-air® users, with each user reporting an average of 17 days. To overcome such limitations, we performed a sensitivity analysis restricted to users reporting at least 50 days of data in MASK-air® (980 users; 148,891 days; average of

152 days per user). We thus observed similar seasonal patterns and frequency of medication group use compared to the main analysis (Table S9).

This study also has important strengths. In fact, it used real-world data to assess the medication patterns of AR patients in different European countries. This aim has led us to consider among mono-therapy days those when patients use a single AR drug formulation with more than one active compound. In the patient perspective, on those days, he/she is using a single drug. The daily monitoring
<table><caption>TABLE 2 Number of different drugs and drug groups used throughout a period of 12 months by patients who had reported medication use at least four times</caption><thead><tr><th></th><th>N (%)</th></tr></thead><tbody><tr><td>N different drugs</td><td></td></tr><tr><td>1</td><td>196 (20.1)</td></tr><tr><td>2</td><td>317 (32.5)</td></tr><tr><td>3</td><td>256 (26.3)</td></tr><tr><td>4</td><td>114 (11.7)</td></tr><tr><td>5</td><td>47 (4.8)</td></tr><tr><td>6</td><td>27 (2.8)</td></tr><tr><td>≥7<sup>a</sup></td><td>18 (1.8)</td></tr><tr><td>N different drug groups</td><td></td></tr><tr><td>1</td><td>246 (25.2)</td></tr><tr><td>2</td><td>409 (41.9)</td></tr><tr><td>3</td><td>249 (25.5)</td></tr><tr><td>4</td><td>54 (5.5)</td></tr><tr><td>5</td><td>13 (1.3)</td></tr><tr><td>6</td><td>4 (0.4)</td></tr></tbody></table>

questionnaire of MASK-air® provides a comprehensive list of available medications for each country (including commercial names), reducing the risk of information biases. In addition, the structure of the MASK-air® app precludes the existence of missing data within each daily monitoring questionnaire. Finally, the VASs used in MASK-air® have been compared to validated questionnaires and were found to have moderate-high validity, reliability and responsiveness.²⁵

In conclusion, medication patterns in patients with AR appear to be similar across European regions, with co-medication being common and presenting a seasonal pattern (particularly frequent during spring). All these findings suggest that AR patients treat themselves according to their symptoms, not following the guideline indications for AR treatment. However, they also suggest that patients apparently need co-medication according to the severity of their symptoms. This paper is essential for the future ARIA guidelines as they will be based on real-world data and evidence-based medicine, rather than only on evidence-based medicine. This study carried out in different European countries shows that guidelines are not used. Thus, this paper has a major clinical impact. Physicians should be aware that most of their patients do not follow their prescriptions and should adapt shared decision-making when discussing treatment options with them.

## ACKNOWLEDGMENTS

This study was funded by ARIA. MASK-air® has been supported by EU grants (EU Structural and Development grant, POLLAR: EIT Health and Structural and Development Funds) and educational grants from Mylan-Viatris, ALK, GSK, Novartis and Uriach. Open Access funding enabled and organized by Projekt DEAL.

## CONFLICT OF INTEREST

IAnsotegui reports personal fees from Hikma, Roxall, Astra Zeneca, Menarini, UCB, Faes Farma, Sanofi, Mundipharma, Bial, Amgen, Stallergenes, Abbott, BayerJB reports personal fees from Chiesi, Cipla, Hikma, Menarini, Mundipharma, Mylan, Novartis, Sanofi-Aventis, Takeda, Teva, Uriach, other from KYomed-Innov, personal fees from PurinaVC reports personal fees from Allergopharma, GSK, grants from Thermo fisher. AC reports grants and personal fees from GSK, personal fees from AstraZeneca, Sanofi, Boehriger Ingelheim, Eurofarma. TH reports personal fees from GSK, Mundipharma, Orion Pharma and Sanofi. JCI reports personal fees from Laboratorios Casasco, Abbott Ecuador, Sanofi, Bago Bolivia, Eurofarma Argentina. MJ reports personal fees from ALK-Abello, Allergopharma, Stallergenes, Anergis, Allergy Therapeutics, Leti, HAL, GSK, Novartis, Teva, Takeda, Chiesi. PK reports personal fees from Adamed, AstraZeneca, Berlin Chemie Menarini, Boehringer Ingelheim, Lekam, Mylan, Novartis, GSK, Polpharma. VK reports other from Berlin CHemie Menarini, Norameda. DLL reports personal fees from Allakos, Amstrong, AstraZeneca, DBV Technologies, Grunenthal, GSK, Mylan/Viatris, Menarini, MSD, Novartis, Pfizer, Sanofi, Siegfried, UCB, Alakos, Gossamer, Carnot, grants from Sanofi, AstraZeneca, Novartis, Circassia, UCB, GSK, Purina institute. RL reports grants from GSK, grants and personal fees from AZ, Novartis, Chiesi. MM reports personal fees from Menarini, Mylan, GSK, Astra Zeneca, Novartis, Chiesi, Sanofi, Pfizer. RM reports personal fees from ALK, allergopharma, Allergy Therapeutics, Friulchem, Hexal, Servier, Klosterfrau, Bayer, FAES, GSK, MSD, Johnson&Johnson, Meda, Stada, UCB, Nuvo, Menarini, Mundipharma, Pohl-Boskamp, grants from ASIT biotech, Leti, Optima, BitopAG, Hulka, Ursapharm, Inmunotek, grants and personal fees from Bencard, Stallergenes, grants, personal fees and non-financial support from Lofarma, non-financial support from Roxall, Atmos, Bionorica, Otonomy, Ferrero, personal fees and non-financial support from Novartis. JM reports personal fees and other from SANOFI-GENZYME & REGENERON, NOVARTIS, ALLAKOS, grants and personal fees from MYLAN Pharma, URIACH Group, personal fees from Mitsubishi-Tanabe, Menarini, UCB, AstraZeneca, GSK, MSD. OP reports grants and personal fees from ALK-Abelló, Allergopharma, g Stallergenes Greer, HAL Allergy Holding B.V./HAL Allergie GmbH, Bencard Allergie GmbH/Allergy Therapeutics, Lofarma, grants and personal fees from ASIT Biotech Tools S.A., Laboratorios LETI/LETI Pharma, Anergis S.A., GlaxoSmithKline, personal fees from Astellas Pharma Global, personal fees from EUFOREA, ROXALL Medizin, Novartis, Sanofi-Aventis and Sanofi-Genzyme, Med Update Europe GmbH, streamedup! GmbH, Mobile Chamber Experts (a GA2LEN Partner), Indoor Biotechnologies, MEDA Pharma/MYLAN, John Wiley and Sons, AS, Paul-Martini-Stiftung (PMS), Ingress-Health HWM, Regeneron Pharmaceuticals Inc., grants from Pohl-Boskamp, Inmunotek S.L., Biomay, Circassia. FSR reports speaker and advisory fees from AstraZeneca, Novartis, Sanofi, GSK, Teva and Lusomedicamenta, all outside the submitted work. JS reports grants and personal fees from SANOFI, personal fees from GSK, NOVARTIS, ASTRA ZENECA, MUNDIPHARMA, FAES FARMA. ATB reports grants and personal fees from Novartis, Mundipharma,

<footnote><sup>a</sup>Includes use of 7 different drugs (N = 11), 8 different drugs (N = 3), 9 different drugs (N = 2) and 10 different drugs (N = 2).</footnote>
Teva Pharma, GSK (GlaxoSmithKline), AstraZeneca, grants from Leti, personal fees from Bial. STS reports personal fees from ERT, Roche products, Novartis, Sanofi Pharma, AstraZeneca, ALK-Abelló, grants from Glaxo Smith Kline, IT reports personal fees from Honoraria for educational activities, speaking engagements, advisory boards from Boehringer Ingelheim, Astra Zeneca, GSK, Novartis, MSD and grants from GSK Hellas Astra Zeneca and Elpen. DW reports personal fees from ALK, Optinose, and was a primary author on the JTFPP Rhinitis Practice Parameter Update 2020. TZ reports Organizational affiliations: Committee member: WHO-Initiative 'Allergic Rhinitis and Its Impact on Asthma' (ARIA), Member of the Board: German Society for Allergy and Clinical Immunology (DGAKI), Head: European Centre for Allergy Research Foundation (ECARF), President: Global Allergy and Asthma European Network (GA2LEN), Member: Committee on Allergy Diagnosis and Molecular Allergology, World Allergy Organization (WAO).

## AUTHOR CONTRIBUTION

BSP, ASS, RJV and RA performed the analyses. LK, OP, VK, MTV, KCB, LB, GWC, VC, PCM, LC, CC, AAC, GDF, WJF, BG, TH, JCI, PK, HK, DELL, RL, MM, RM, MMA, RM, JM, YO, NGP, VP, NPT, FSR, SR, PWR, BS, MS, ATB, LTB, PVT, STS, SJ, AV, SW, AY and MZ participated in the patient recruitment. JB, WC, JMA, AB, TZ and JAF designed the study. JB and BSP wrote the paper. IJA, TC, TC, DKC, EMC, PD, MG, ZI, MJ, IK, DL, BL, MO, IT, OV and DW are members of the think tank of MASK-air.

## ORCID

Bernardo Sousa-Pinto https://orcid.org/0000-0002-1277-3401

Rafael José Vieira https://orcid.org/0000-0003-1834-3055

Rita Amaral https://orcid.org/0000-0002-0233-830X

Ludger Klimek https://orcid.org/0000-0002-2455-0192

Oliver Pfaar https://orcid.org/0000-0003-4374-9639

Victoria Cardona https://orcid.org/0000-0003-2197-9767

Pedro Carreiro-Martins https://orcid.org/0000-0002-4129-133X

Lorenzo Cecchi https://orcid.org/0000-0002-0658-2449

Cemal Cingi https://orcid.org/0000-0003-3934-5092

Alvaro A. Cruz https://orcid.org/0000-0002-7403-3871

Philippe Devillier https://orcid.org/0000-0001-6054-1886

Tari Haatela https://orcid.org/0000-0003-4757-2156

Désirée E. Larenas-Linnemann https://orcid.org/0000-0002-5713-5331

Mario Morais-Almeida https://orcid.org/0000-0003-1837-2980

Nikolaos G. Papadopoulos https://orcid.org/0000-0002-4448-3468

Vincenzo Patella https://orcid.org/0000-0001-5640-6446

Philip W. Rouadi https://orcid.org/0000-0002-5365-9568

Peter Valentin Tomazic https://orcid.org/0000-0001-6445-4800

Olivier Vandenplas https://orcid.org/0000-0002-4608-3310

Torsten Zuberbier https://orcid.org/0000-0002-1466-8875

Jean Bousquet https://orcid.org/0000-0002-4061-4766

## REFERENCES

1. Bousquet J, Anto JM, Bachert C, et al. Allergic rhinitis. *Nat Rev Dis Primers*. 2020;6(1):95.

2. Bousquet J, Agache I, Anto JM, et al. Google trends terms reporting rhinitis and related topics differ in European countries. *Allergy*. 2017;72(8):1261-1266.

3. Bousquet J, Agache I, Berger U, et al. Differences in reporting the ragweed pollen season using google trends across 15 countries. *Int Arch Allergy Immunol*. 2018;176(3-4):181-188.

4. Bousquet J, Onorato GL, Oliver G, et al. Google trends and pollen concentrations in allergy and airway diseases in France. *Allergy*. 2019;74(10):1910-1919.

5. Bousquet J, Schroder-Bernhardi D, Bachert C, et al. Heterogeneity of the pharmacologic treatment of allergic rhinitis in Europe based on MIDAS and OTCims platforms. *Clin Exp Allergy*. 2021;51(8):1033-1045.

6. Dykewicz MS, Wallace DV, Amrol DJ, et al. Rhinitis 2020: a practice parameter update. *J Allergy Clin Immunol*. 2020;146(4):721-767.

7. Bedard A, Basagana X, Anto JM, et al. Mobile technology offers novel insights into the control and treatment of allergic rhinitis: the MASK study. *J Allergy Clin Immunol*. 2019;144(1):135-143 e136.

8. Bedard A, Basagana X, Anto JM, et al. Treatment of allergic rhinitis during and outside the pollen season using mobile technology. A MASK study. *Clin Transl Allergy*. 2020;10(1):62.

9. Bousquet J, Schunemann HJ, Togias A, et al. Next-generation allergic rhinitis and its impact on asthma (ARIA) guidelines for allergic rhinitis based on grading of recommendations assessment, development and evaluation (GRADE) and real-world evidence. *J Allergy Clin Immunol*. 2020;145(1):70-80 e73.

10. Bousquet J, Anto JM, Bachert C, et al. ARIA digital anamorphosis: digital transformation of health and care in airway diseases from research to practice. *Allergy*. 2021;76(1):168-190.

11. Bousquet J, Bedbrook A, Czarlewski W, et al. Guidance to 2018 good practice: ARIA digitally-enabled, integrated, person-centred care for rhinitis and asthma. *Clin Transl Allergy*. 2019;9:16.

12. Bousquet J, Bewick M, Arnavielhe S, et al. Work productivity in rhinitis using cell phones: the MASK pilot study. *Allergy*. 2017;72(10):1475-1484.

13. Bousquet J, Arnavielhe S, Bedbrook A, et al. Treatment of allergic rhinitis using mobile technology with real world data: the MASK observational pilot study. *Allergy*. 2018;73(9):1763-1774.

14. Laune D, Arnavielhe S, Viart F, et al. Adaptation of the general data protection regulation (GDPR) to a smartphone app for rhinitis and asthma (MASK-air(R)). *Rev Mal Respir*. 2019;36(9):1019-1031.

15. Samreth D, Arnavielhe S, Ingenrieth F, et al. Geolocation with respect to personal privacy for the allergy diary app - a MASK study. *World Allergy Organ J*. 2018;11(1):15.

16. Kopp-Kubel S. International nonproprietary names (INN) for pharmaceutical substances. *Bull World Health Organ*. 1995;73(3):275-279.

17. Cohen J. *Statistical Power Analysis for the Behavioral Sciences*. Taylor and Francis Group; 1988.

18. Mavragani A, Ochoa G. Google trends in infodemiology and in-foveillance: methodology framework. *JMIR Public Health Surveill*. 2019;5(2):e13439.

19. Dreher PC, Tong C, Ghiraldi E, Friedlander JI. Use of google trends to track online behavior and interest in kidney stone surgery. *Urology*. 2018;121:74-78.

20. Bousquet J, Devillier P, Arnavielhe S, et al. Treatment of allergic rhinitis using mobile technology with real-world data: the MASK observational pilot study. *Allergy*. 2018;73(9):1763-1774.

21. Brozek JL, Bousquet J, Agache I, et al. Allergic rhinitis and its impact on asthma (ARIA) guidelines-2016 revision. *J Allergy Clin Immunol*. 2017;140(4):950-958.
22. Dykewicz MS, Wallace DV, Baroody F, et al. Treatment of seasonal allergic rhinitis: an evidence-based focused 2017 guideline update. *Ann Allergy Asthma Immunol*. 2017;119(6):489-511 e441.

23. Bousquet J, Klimek L, Kuna P, Mullol J, Toppila-Salmi S. The debate: regular versus as-needed use of intranasal corticosteroids for a patient-centered approach. *J Allergy Clin Immunol Pract*. 2021;9(3):1374-1375.

24. Bousquet J, Bachert C, Canonica GW, et al. Unmet needs in severe chronic upper airway disease (SCUAD). *J Allergy Clin Immunol*. 2009;124(3):428-433.

25. Sousa-Pinto B, Eklund P, Pfaar O, et al. Validity, reliability, and responsiveness of daily monitoring visual analog scales in MASK-air®. *Clin Transl Allergy*. 2021;11(7):e12062.

SUPPORTING INFORMATION

Additional supporting information may be found in the online version of the article at the publisher's website.

How to cite this article: Sousa-Pinto B, Sá-Sousa A, Vieira RJ, et al. Behavioural patterns in allergic rhinitis medication in Europe: A study using MASK-air® real-world data. *Allergy*. 2022;77:2699-2711. doi:10.1111/all.15275