Skip to main content
Download PDF

Treatment of allergic rhinitis during and outside the pollen season using mobile technology. A MASK study

Clinical and Translational Allergy volume 10, Article number: 62 (2020) Cite this article

Abstract

Background

The analysis of mobile health (mHealth) data has generated innovative insights into improving allergic rhinitis control, but additive information is needed. A cross-sectional real-world observational study was undertaken in 17 European countries during and outside the estimated pollen season. The aim was to collect novel information including the phenotypic characteristics of the users.

Methods

The Allergy Diary–MASK-air–mobile phone app, freely available via Google Play and App, was used to collect the data of daily visual analogue scales (VASs) for overall allergic symptoms and medication use. Fluticasone Furoate (FF), Mometasone Furoate (MF), Azelastine Fluticasone Proprionate combination (MPAzeFlu) and eight oral H1-antihistamines were studied. Phenotypic characteristics were recorded at entry. The ARIA severity score was derived from entry data. This was an a priori planned analysis.

Results

9037 users filled in 70,286 days of VAS in 2016, 2017 and 2018. The ARIA severity score was lower outside than during the pollen season. Severity was similar for all treatment groups during the pollen season, and lower in the MPAzeFlu group outside the pollen season. Days with MPAzeFlu had lower VAS levels and a higher frequency of monotherapy than the other treatments during the season. Outside the season, days with MPAzeFlu also had a higher frequency of monotherapy. The number of reported days was significantly higher with MPAzeFlu during and outside the season than with MF, FF or oral H1-antihistamines.

Conclusions

This study shows that the overall efficacy of treatments is similar during and outside the pollen season and indicates that medications are similarly effective during the year.

Background

Observationl real-life studies using mobile technology can complement randomized control trials (RCTs) and improve the positioning of allergic rhinitis (AR) medications in care pathways. MASK-air (Mobile Airways Sentinel NetworK) is an information and communication technology (ICT) system which is centred around the patient. It uses a treatment scroll list which includes all medications customized for each country as well as a visual analogue scale (VAS) to assess rhinitis control [1,2,3]. Two studies in over 9000 users and 22 countries enabled differentiation between AR treatments [3, 4] and showed that the assessment of daily data was useful in the understanding of treatment patterns. Most allergic rhinitis (AR) patients use on-demand treatment when they are suboptimally controlled. As in resistant hypertension, defined by the number of medications used to control the disease [5], many patients have a worse control when increasing their medications [3, 4]. Differences in efficacy between intranasal corticosteroids (INCS) and intra-nasal MPAzeFlu were observed [3, 4]. These studies were carried out across the year and it is possible that the results differ during and outside the pollen season as the allergen exposure differs and the disease may not be the same in terms of phenotypes [6, 7] and costs [8]. Another MASK study in 12,143 users and 23 countries found that very few patients reporting data for several days were adherent [9]. These studies combined propose novel concepts for AR treatment. However, they failed to show certain key facts including the phenotypic characteristics of the patients at entry and whether the conclusions raised are applicable during and outside the pollen season.

The present analysis is a follow-up of previous MASK studies attempting to answer unresolved questions to provide novel real-world data information. A new cross-sectional observational study undertaken in 9037 users and 17 European countries examined AR treatments during and outside the pollen seasons (2016, 2017 and 2018). Two-thirds of the participants were already enrolled in previous studies, but analyses differed. The aim of the study was (i) to assess the participants' characteristics to better assess their phenotypes, (ii) to study whether the same trends in treatment efficacy are found during high and low allergen loads, assessed according to a recent study [10], and (iii) to investigate whether the trends in treatment efficacy were associated with the severity of the disease at entry. The study was focussed on the most commonly used intra-nasal medications containing corticosteroids: Fluticasone Furoate (FF), Mometasone Furoate (MF) and MPAzeFlu [3, 4], reported as monotherapy or co-medication [3, 4, 11]. It also focussed on the most common oral H1-antihistamines (OAH) reported as monotherapy: Bilastine, Cetirizine (CET), Desloratadine (DL), Ebastine, Fexofenadine (FEXO), Levocetirizine (LEVOCET), Loratadine (Lora) and Rupatadine. We did not study OAH reported as co-medication, as they are usually associated with INCS. Untreated days were used as a control group.

Methods

Users

All users of the app in Europe in 2016, 2017 and 2018 were included with no exclusion criteria and according to methods previously described [4, 11] .

Setting

Users from 17 countries filled in the Allergy Diary (Tables  1 and 2).

Table 1 Country and number of users recording Visual Analogue Scale score using MASK-air® during the pollen season
Full size table
Table 2 Country and number of users recording Visual Analogue Scale score using MASK-air® outside the pollen season
Full size table

Ethics and privacy

The Allergy Diary is CE1 registered. By using k-anonymity, the data were all anonymized including the data related to geolocalization [12]. MASK-air® is in line with the General Data Protection Regulation (GDPR) EU Directive 95/46/EC [13]. Independent Review Board approval was not required since the study is observational and users agree to having their data analysed (terms of use).

Allergy diary (MASK-air®)

Geolocalized users self-assessed their daily symptom control using the touchscreen functionality on their smart phone to click on a VAS score (ranging from 0 to 100) for overall symptoms (global VAS). Some users reported VAS scores more than once a day. In previous studies, we found that the highest reported value should be used and we followed this. According to previous studies, severity was defined as “no symptoms” (VAS ranging from 0 to 20), “mild” (20 to 50) and “severe” (≥ 50) [1, 14].

Users input their daily medications using a scroll list which contains all country-specific OTC and prescribed medications available for each country. Both the active ingredient and the marketed OTC and prescribed medications are listed. The list has been populated using IMS data. Days with or without treatment were reported by users. The present study is another MASK study. Some of the raw data used in the other papers (up to December 2017) were used in this study [4]. However, new data have been included, many of the analyses are different and estimated allergen exposure was not previously analyzed.

Time of the study

We did not study all individual locations as only around 60% of subjects agreed to be geolocalized and we knew only the country of origin in the non-geolocalized users. We therefore estimated the pollen season for each country using Google Trends and terms previously defined [15, 16]. We found that overall, across Europe, the season covered March 15 to the end of June. We have published a series of papers on Google Trends to better understand pollen seasons and the drawbacks of the method [15, 17,18,19,20,21,22,23]. They can roughly appreciate the season. On the other hand, pollen counts cannot be used on a daily basis. Moreover, they are not available for all locations. Thus, they cannot be used in the current study. To assess the pollen season precisely, personal samplers should be used but it would be impossible to use them in thousands of patients and, due to privacy, they cannot be used in this study.

We estimated the period outside the pollen season as August 1 to December 31. We therefore avoided the early tree pollen season (January-March) and excluded days recorded in Austria and France between August 1 and September 15 to avoid the ragweed pollen season. In a recent paper, the same approach was used to assess impact of pollution on the pollen season [24].

Selection of medications

The International Nonproprietary Names (INN) classification was used for drug nomenclature [25]. Monotherapy was defined as days when only one single medication for rhinitis was reported. MPAzeFlu contains two drugs but, being a fixed combination, it was considered as monotherapy. Co-medication was defined as days with two or more medications for rhinitis. Asthma medications were not considered in co-medication.

Characteristics at entry

According to a previous study, we considered AR symptoms recorded upon the first use of the app (rhinorrhea, sneezing, nasal congestion, nasal itching, ocular symptoms) [26]. On the same day (i.e. at entry), we assessed the ARIA severity score calculated by using the four questions regarding impact on sleep, daily activities, work/school attendance, and bothersome symptoms. Each of these four items was ascribed a score of 1 (“Yes”) or 0 (“No”). The total ARIA score ranged from 0 (no impairment) to 4 (severe impairment). This score was found to correlate with EQ-5D and WPAI-AS using MASK [27] and was used in an epidemiological study [28].

Size of the study

In this study, all registered users were included to obtain the best possible estimates for the specified time window. From previous studies, the  numbers tested largely exceed those needed to find significant differences in the full set analysis [4].

Stratification of the users

The stratification was determined by season of enrolment (i.e. during or outside the pollen season).

Statistical methods

A non-Gaussian distribution was found for the data. Non-parametric tests and medians (and percentiles) were used.

Analysis of the data

All analyses were conducted separately for users who were enrolled and used the app (i) during the pollen season (discarding days reported outside the pollen season by those users) and (ii) outside the pollen season (discarding days reported during the pollen season by those users).

All analyses were conducted by comparing the days when app users reported the use of INCS treatment (FF, MPAzeFlu, MF), the use of OAH in monotherapy, and the days when users did not report any treatment (days with other treatment were excluded from the analyses).

According to the treatment (FF, MPAzeFlu, MF, OAH in monotherapy or no treatment) reported at entry day (thereafter called Day 1), we compared (i) characteristics reported by the user on Day 1 (i.e. AR symptoms, impact of symptoms and ARIA score), (ii) the distribution of global VAS reported by the users on Day 1, and (iii) the proportion of monotherapy versus comedication reported for the use of that treatment.

The comparison analyses described in (ii) and (iii) were also conducted on all the days of App use, i.e. for all the days of App use, we compared the distribution of global VAS, as well as the proportion of monotherapy versus comedication, according to the treatment reported on that day.

Finally, for each treatment, we compared the average number of days of treatment reported per user, estimated by dividing the total number of days for which the use of a medication was reported by the total number of users reporting that medication at least once.

To investigate the consistency of our results during and outside the pollen season, we compared characteristics at entry between during and outside the pollen season.

The ARIA score on Day 1 (ranging from 0 to 4) was considered either as a continuous or a categorical variable. Global VAS was considered either as a continuous, or a categorical variable - using three cutoffs: VAS < 20/100 (controlled days), VAS 20-49 (days with moderate control), VAS ≥ 50 (days with poor control) [4, 11]. Chi square tests were used to compare the distribution of categorical variables (i.e. symptoms and impact of symptoms on Day 1, ARIA score on Day 1, global VAS categories). Kruskal–Wallis tests were used to compare the distribution of continuous variables (i.e. ARIA score, global VAS).

Results

Demographic characteristics

The study included 9037 users (i.e. 6939 who started to use the app during the pollen season and 2098 who started outside the season). Roughly 5% of users did not report their age or reported an age of below 10. Users ranged from zero to 91 years-old (mean, SD: 33.5 ± 15.5 years). There were 53.5% of women and 46.5% of men.

A total of 211,003 days were recorded between 2016 and 2018. Duplicates or multiplicates for the same day were found in 4397 days. 49,566 days were recorded by the 6939 users during the pollen season. There were 23,377 (54.4%) days without treatment and 19,568 (45.6%) days with the targetted INCS or OAH. 20,720 days were recorded by the 2098 users outside the pollen season. There were 13,130 (69.5%) days without treatment and 5756 (30.5%) days with treatment (Fig. 1).

Fig. 1
figure 1

Flow chart of the study population

Full size image

Characteristics on Day 1

Characteristics on Day 1 are given in Tables 3 and 4 for the pollen season and in Tables 3 and 5 for outside the pollen season.

Table 3 Comparison of characteristics and VAS levels at entry recorded during and outside the pollen season
Full size table
Table 4 Results for all participants recruited during the pollen season
Full size table
Table 5 Results for all participants recruited outside the pollen season
Full size table

During the pollen season (Table 4), 69 to 78% of users reported rhinorrhoea on Day 1. Other nasal symptoms were reported in 55 to 87% of users, and ocular symptoms in 42 to 74%. Most users reported bothersome symptoms (74–78%). Impact on sleep, daily activities and work/school attendance was reported in 27–50% of users. The ARIA score was similar in all five groups of users.

Outside the pollen season (Table 5), 55 to 72% of users reported rhinorrhoea on Day 1. Other nasal symptoms were reported in 49 to 84% of users and ocular symptoms in 21 to 62%. Most users reported bothersome symptoms (55–84%). Impact on sleep, daily activities and work/school attendance was reported in 24 to 47% of users. The use of MPAzeFlu on Day 1 was significantly associated with fewer symptoms, compared to the use of FF or MF. App users who reported the use of MPAzeFlu on Day 1 were less likely to report a severe impact of symptoms, compared to users of FF or MF on Day 1. However, the difference was only borderline significant.

Significantly more symptoms on Day 1 were reported during the pollen season than outside the pollen season, and the ARIA severity score was significantly higher outside the pollen season. Similar trends were found when restricting the population to users not reporting treatment on Day 1 (results not shown).

Treatment efficacy

During the pollen season, on Day 1, VAS levels were reported by 3736 users without treatment, 1414 users with OAH in monotherapy and 841 users with INCS treatment (Table 4). No statistically significant difference in VAS levels was observed between INCS treatments on Day 1. When all VAS days were studied, we observed significantly lower VAS levels in MPAzeFlu days compared to other INCS (FF or MF) days (p = 0.0001, Table 4).

Outside the pollen season, on Day 1, VAS levels were reported by 1299 users without treatment, 275 users with OAH in monotherapy and 274 users with INCS treatment (Table 5). No statistically significant difference in VAS levels was observed between INCS treatments on Day 1. When all VAS days were studied, we observed non-significant differences between MPAzeFlu use compared to other INCS use or OAH.

Monotherapy versus co-medication according to INCS use

During the pollen season, monotherapy was significantly more reported in users who reported the use of MPAzeFlu on Day 1 (44%) compared to app users who reported the use of FF or MF on Day 1 (i.e. between 30 and 35%) (p < 0.01). Similar results were found when all VAS days were studied (p < 0.001) (Fig. 2).

Fig. 2
figure 2

Proportion of INCS treatment groups on all days during and outside the pollen season

Full size image

Outside the pollen season, monotherapy was significantly more reported in users who reported the use of MPAzeFlu on Day 1 (71%) compared to those who reported the use of FF or MF on Day 1 (40 to 50%) (p < 0.001). Similar results were found when all VAS days were studied (p < 0.001) (Fig. 2).

Number of days with treatment

In untreated users, the estimated average number of days of reporting per user increased from 5.1 (during pollen season) to 8.5 (outside). Both during and outside the pollen season, there was a similar trend of days reported from OAH, FF, MF to MPAzeFlu (Tables 4 and 5). The average number of days of reporting per user was low for OAH (4.5 days) and increased to around 10 days for MPAzeFlu (during and outside pollen season).

Discussion

Two MASK studies [4, 11] have shown that, in real life, the assessment of days can provide (i) information on patients’ treatment (ii) novel insight into the behaviour of AR patients towards treatment and (iii) novel concepts for change management of AR [29]. In the present study (Table 3), we show that the ARIA severity score is (i) lower outside than during the pollen season (ii) similar for all INCS treatment groups on Day 1 during the pollen season and (iii) lower in the MPAzeFlu group than in the MF and FF groups outside the pollen season. We also show that MPAzeFlu might be more effective than FF or MF in the pollen season (lower VAS levels are reported in days with MPAzeFlu treatment, and MPAzeFlu is more frequently used as monotherapy) as well as outside the pollen season (more frequent use of monotherapy). Finally, the number of days of reported treatment per user increased from OAH to MPAzeFlu (Table 6).

Table 6 New information provided by this paper
Full size table

Strengths and limitations

Strengths and limitations of MASK have previously been reported [11]. As for all studies using participatory data, potential biases include (i) the likelihood of sampling bias being present (ii) the lack of generalizability of the study that was found, as bothersome symptoms are present in around 80% of users, indicating that most users have a moderate to severe disease, and (iii) outcome misclassification that cannot be assessed. Data obtained with an app are not representative of the general population.

In the previous MASK studies, there was very little information on patient characteristics. In the present study, we examined characteristics at entry in more detail.

As in other studies [4, 11], we used days in a cross-sectional analysis because there is no clear pattern of treatment and a longitudinal study was not feasible since users mostly use the App intermittently.

In the current study, we cannot ascertain that the users are allergic to a given allergen since this information is not available for all patients. Moreover, we did not assess the real pollen exposure of the patients, as only 60% of them agreed to be geolocated.

The diagnosis of AR was not supported by a physician but was a response to the question: “Do you have allergic rhinitis? Yes/No”. Some of the users with non-allergic rhinitis may therefore have responded “Yes” to the question. However, > 95% of responders declared symptoms of AR by questionnaire. Precise patient characterization is impossible using an App, but every observational study using MASK was able to identify days with poor control or criteria of severity [26, 27, 30,31,32]. Moreover, some data are highly similar across studies. These include the percentage of untreated days (i.e. approximately 50% of the total days recorded).

There is a clear deviation in the results obtained in highly populated countries and a very high prevalence of allergic rhinitis with little collection data. These results could possibly influence the data.

The current study has many strengths including larger numbers, multiple countries, range of treatments studied and patient/person-generated data.

Interpretation of the results and generalizability

This real-world assessment of the Allergy Diary using VAS allows the  assessment of treatment efficacy by days [4, 11]. This observational study complements the two previous studies in many aspects (Table 7).

Table 7 Key messages
Full size table

First, it shows that over 75% of patients using the app during the pollen season have bothersome symptoms. Outside of the pollen season, the rate of bothersome symptoms is around 65%. It is therefore likely that most App users have moderate/severe AR and do not therefore represent the general population [33]. It is interesting to note that these levels of impairment are close to those of patients consulting in primary [34] or specialist care [35]. Although the impact of AR is less important outside the pollen season than during, differences are not very important in the ARIA score.

Second, it was expected that MPAzeFlu would have been given to more severe patients. The ARIA score was not different between groups in the pollen season. In contradistinction, the ARIA score was significantly lower outside the pollen season in untreated users and even lower in the MPAzeFlu users.

Third, both during and outside the pollen season, MPAzeFlu is associated with less symptoms, something that seems consistent with being the most potent medication in a randomized controlled trial [36]. However, there are differences between seasons. During the pollen season, the use of MPAzeFlu is associated with the lowest VAS levels in treated groups, and MPAzeFlu is used more commonly as monotherapy. Outside of the pollen season, all medications appear to be associated with similar VAS levels. However, MPAzeFlu is used as a monotherapy in 70% of days whereas the other INCS are used in less than 50% of days. Nevertheless, given the cross-sectional setting of our study, effectiveness cannot be inferred easily.

Fourth, the estimated average number of days reported per user in the MPAzeFlu group was almost twice as high as that among the OAH group. Although there is no simple interpretation, it is suggested that the most effective treatments are reported for a longer period of time. However, we cannot assess duration in this cross-sectional setting, but this finding is consistently found across MASK studies [11]. Again, there is no major difference between seasons.

Fifth, as already found in all users [4, 11], median VAS levels are the lowest in untreated days, both during and outside the pollen season. This can be interpreted as subjects using treatment when they do not feel well, in opposition to the paradigm in which those who take medication are the ones with controlled symptoms (and therefore lower VAS). Also, the patterns of co-medication of MPAzeFlu by comparison to FF or MF are similar in the two periods.

Sixth, the behaviour of users appears to be quite similar between seasons. In particular, they report the same number of days with the same medications.

This study shows that, in real-life, the same treatments have similar patterns during and outside the pollen season for most criteria tested. This is an important finding that may impact guidelines considering AR severity rather than seasonal patterns [37, 38].

Conclusions

Although the MASK mHealth App has generated real-world evidence that has led to novel pharmacotherapy insights, the current study extends our knowledge by (i) assessing the characteristics of the patients, (ii) showing that results can be extended to both the estimated pollen season and the period outside the season, and (iii) showing that rhinitis medications are equally effective during and outside the pollen season (Table 5).

Real-world data (RWD) and real-world evidence (RWE) both play an increasing role in health care decisions supporting clinical trial designs and observational studies to generate innovative and new treatment approaches. This study shows that the overall efficacy of treatments is similar during and outside the pollen season and indicates that medications are similarly effective during the year. It is an important study for the digital transformation of health and care in rhinitis and asthma multimorbidity [3, 39,40,41,41].

Availability of data and materials 

On request to Kyomed Innov. Members of the MASK study group have free access to the data.

Abbreviations

AR:

Allergic rhinitis

ARIA:

Allergic Rhinitis and its Impact on Asthma

MPAzeFlu:

Intranasal azelastine-fluticasone propionate

CET:

Cetirizine

DL:

Desloratadine

FEXO:

Fexofenadine

FF:

Fluticasone Furoate

FP:

Fluticasone Propionate

GRADE:

Grading of Recommendations, Assessment, Development and Evaluation

ICT:

Information Communication Technology

INCS:

Intranasal corticosteroid

INN:

International Nonproprietary Names

LEVOCET:

Levocetirizine

Lora:

Loratadine

MASK-rhinitis:

Mobile Airways Sentinel NetworK for allergic rhinitis

MF:

Mometasone Furoate

OAH:

Oral H1-anti-histamine

RCT:

Randomized controlled trial

VAS:

visual analogue scale

References

  1. Bousquet J, Arnavielhe S, Bedbrook A, Bewick M, Laune D, Mathieu-Dupas E, et al. MASK 2017: ARIA digitally-enabled, integrated, person-centred care for rhinitis and asthma multimorbidity using real-world-evidence. Clin Transl Allergy. 2018;8:45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Bousquet J, Anto JM, Annesi-Maesano I, Dedeu T, Dupas E, Pepin JL, et al. POLLAR: Impact of air POLLution on Asthma and Rhinitis; a European Institute of Innovation and Technology Health (EIT Health) project. Clin Transl Allergy. 2018;8:36.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Bousquet J, Bedbrook A, Czarlewski W, Onorato GL, Arnavielhe S, Laune D, et al. Guidance to 2018 good practice: ARIA digitally-enabled, integrated, person-centred care for rhinitis and asthma. Clin Transl Allergy. 2019;9:16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bedard A, Basagana X, Anto JM, Garcia-Aymerich J, Devillier P, Arnavielhe S, 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):135-43 e6.

  5. Nagarajan N, Jalal D. Resistant Hypertension: Diagnosis and Management. Adv Chronic Kidney Dis. 2019;26(2):99–109.

    Article  PubMed  Google Scholar 

  6. Smith KA, Gray NJ, Saleh F, Cheek E, Frew AJ, Kern F, et al. Characterisation of CD154 + T cells following ex vivo allergen stimulation illustrates distinct T cell responses to seasonal and perennial allergens in allergic and non-allergic individuals. BMC Immunol. 2013;14:49.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Munoz-Cano R, Ribo P, Araujo G, Giralt E, Sanchez-Lopez J, Valero A. Severity of allergic rhinitis impacts sleep and anxiety: results from a large Spanish cohort. Clin Transl Allergy. 2018;8:23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lang K, Allen-Ramey F, Huang H, Rock M, Kaufman E, Dykewicz MS. Health care resource use and associated costs among patients with seasonal versus perennial allergic rhinitis. Allergy Asthma Proc. 2016;37(5):103–11.

    Article  PubMed  Google Scholar 

  9. Menditto E, Costa E, Midao L, Bosnic-Anticevich S, Novellino E, Bialek S, et al. Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study. Clin Exp Allergy. 2019;49(4):442–60.

    Article  PubMed  Google Scholar 

  10. Bedard A, Sofiev M, Arnavielhe S, Anto JM, Garcia-Aymerich J, Thibaudon M, et al. Interactions between air pollution and pollen season for rhinitis using mobile technology: a MASK-POLLAR study. J Allergy Clin Immunol Pract. 2020.

  11. Bousquet J, Devillier P, Arnavielhe S, Bedbrook A, Alexis-Alexandre G, van Eerd M, et al. Treatment of allergic rhinitis using mobile technology with real-world data: The MASK observational pilot study. Allergy. 2018;73(9):1763–74.

    Article  CAS  PubMed  Google Scholar 

  12. Samreth D, Arnavielhe S, Ingenrieth F, Bedbrook A, Onorato GL, Murray R, et al. Geolocation with respect to personal privacy for the Allergy Diary app - a MASK study. World Allergy Organ J. 2018;11(1):15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data; Official Journal of the European Communities, L281, 31; 23. November 1995.

  14. Bedard A, Anto JM, Fonseca JA, Arnavielhe S, Bachert C, Bedbrook A, et al. Correlation between work impairment, scores of rhinitis severity and asthma using the MASK-air((R)) App. Allergy. 2020;75(7):1672–88.

    Article  CAS  PubMed  Google Scholar 

  15. Bousquet J, Agache I, Anto JM, Bergmann KC, Bachert C, Annesi-Maesano I, et al. Google Trends terms reporting rhinitis and related topics differ in European countries. Allergy. 2017;72(8):1261–6.

    Article  CAS  PubMed  Google Scholar 

  16. Bousquet J, Onorato GL, Oliver G, Basagana X, Annesi-Maesano I, Arnavielhe S, et al. Google Trends and pollen concentrations in allergy and airway diseases in France. Allergy. 2019.

  17. Barbosa M, Morais-Almeida M, Sousa C, Bousquet J. The “Big Five” Lung Diseases in CoViD-19 Pandemic – a Google Trends analysis. Pulmol. 2020:in press.

  18. Bousquet J, Agache I, Berger U, Bergmann KC, Besancenot JP, Bousquet PJ, et al. Differences in Reporting the Ragweed Pollen Season Using Google Trends across 15 Countries. Int Arch Allergy Immunol. 2018;176(3–4):181–8.

    Article  PubMed  Google Scholar 

  19. Bousquet J, O’Hehir RE, Anto JM, D’Amato G, Mosges R, Hellings PW, et al. Assessment of thunderstorm-induced asthma using Google Trends. J Allergy Clin Immunol. 2017;140(3):891-3 e7.

  20. Bousquet J, Onorato GL, Oliver G, Basagana X, Annesi-Maesano I, Arnavielhe S, et al. Google Trends and pollen concentrations in allergy and airway diseases in France. Allergy. 2019;74(10):1910–9.

    Article  PubMed  Google Scholar 

  21. Kaidashev I, Morokhovets H, Rodinkova V, Bousquet J. Patterns in Google Trends Terms Reporting Rhinitis and Ragweed Pollen Season in Ukraine. Int Arch Allergy Immunol. 2019;178(4):363–9.

    Article  PubMed  Google Scholar 

  22. Sousa-Pinto B, Anto A, Czarlewski W, Anto JM, Fonseca JA, Bousquet J. Assessment of the impact of media coverage in coronavirus-related Google Trends: Infodemiology study. J Med Internet Res. 2020.

  23. Sofiev M, Palamarchuk Y, Bedard A, Basagana X, Anto JM, Kouznetsov R, et al. A demonstration project of Global Alliance against Chronic Respiratory Diseases: Prediction of interactions between air pollution and allergen exposure-the Mobile Airways Sentinel NetworK-Impact of air POLLution on Asthma and Rhinitis approach. Chin Med J (Engl). 2020.

  24. Bedard A, Sofiev M, Arnavielhe S, Anto JM, Garcia-Aymerich J, Thibaudon M, et al. Interactions Between Air Pollution and Pollen Season for Rhinitis Using Mobile Technology: A MASK-POLLAR Study. J Allergy Clin Immunol Pract. 2020;8(3):1063-73 e4.

  25. Kopp-Kubel S. International Nonproprietary Names (INN) for pharmaceutical substances. Bull World Health Organ. 1995;73(3):275–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Bousquet J, Caimmi DP, Bedbrook A, Bewick M, Hellings PW, Devillier P, et al. Pilot study of mobile phone technology in allergic rhinitis in European countries: the MASK-rhinitis study. Allergy. 2017;72(6):857–65.

    Article  CAS  PubMed  Google Scholar 

  27. Bousquet J, Arnavielhe S, Bedbrook A, Fonseca J, Morais Almeida M, Todo Bom A, et al. The Allergic Rhinitis and its Impact on Asthma (ARIA) score of allergic rhinitis using mobile technology correlates with quality of life: The MASK study. Allergy. 2018;73(2):505–10.

    Article  CAS  PubMed  Google Scholar 

  28. Amaral R, Bousquet J, Pereira AM, Araujo LM, Sa-Sousa A, Jacinto T, et al. Disentangling the heterogeneity of allergic respiratory diseases by latent class analysis reveals novel phenotypes. Allergy. 2019;74(4):698–708.

    Article  CAS  PubMed  Google Scholar 

  29. Bousquet J, Hellings PW, Agache I, Amat F, Annesi-Maesano I, Ansotegui IJ, et al. Allergic Rhinitis and its Impact on Asthma (ARIA) Phase 4 (2018): Change management in allergic rhinitis and asthma multimorbidity using mobile technology. J Allergy Clin Immunol. 2019;143(3):864–79.

    Article  PubMed  Google Scholar 

  30. Caimmi D, Baiz N, Tanno LK, Demoly P, Arnavielhe S, Murray R, et al. Validation of the MASK-rhinitis visual analogue scale on smartphone screens to assess allergic rhinitis control. Clin Exp Allergy. 2017;47(12):1526–33.

    Article  CAS  PubMed  Google Scholar 

  31. Bousquet J, Devillier P, Anto JM, Bewick M, Haahtela T, Arnavielhe S, et al. Daily allergic multimorbidity in rhinitis using mobile technology: a novel concept of the MASK study. Allergy. 2018;73(9):1763–74.

    Article  CAS  PubMed  Google Scholar 

  32. Bousquet J, VandenPlas O, Bewick M, Arnavielhe S, Bedbrook A, Murray R, et al. The Work Productivity and Activity Impairment Allergic Specific (WPAI-AS) Questionnaire Using Mobile Technology: The MASK Study. J Investig Allergol Clin Immunol. 2018;28(1):42–4.

    Article  CAS  PubMed  Google Scholar 

  33. Bauchau V, Durham SR. Epidemiological characterization of the intermittent and persistent types of allergic rhinitis. Allergy. 2005;60(3):350–3.

    Article  CAS  PubMed  Google Scholar 

  34. Bousquet J, Neukirch F, Bousquet PJ, Gehano P, Klossek JM, Le Gal M, et al. Severity and impairment of allergic rhinitis in patients consulting in primary care. J Allergy Clin Immunol. 2006;117(1):158–62.

    Article  PubMed  Google Scholar 

  35. Bousquet J, Annesi-Maesano I, Carat F, Leger D, Rugina M, Pribil C, et al. Characteristics of intermittent and persistent allergic rhinitis: DREAMS study group. Clin Exp Allergy. 2005;35(6):728–32.

    Article  CAS  PubMed  Google Scholar 

  36. Hampel FC, Ratner PH, Van Bavel J, Amar NJ, Daftary P, Wheeler W, et al. Double-blind, placebo-controlled study of azelastine and fluticasone in a single nasal spray delivery device. Ann Allergy Asthma Immunol. 2010;105(2):168–73.

    Article  CAS  PubMed  Google Scholar 

  37. Brozek JL, Bousquet J, Agache I, Agarwal A, Bachert C, Bosnic-Anticevich S, et al. Allergic Rhinitis and its Impact on Asthma (ARIA) Guidelines - 2016 Revision. J Allergy Clin Immunol. 2017;140(4):950–8.

    Article  PubMed  Google Scholar 

  38. Wallace DV, Dykewicz MS, Oppenheimer J, Portnoy JM, Lang DM. Pharmacologic Treatment of Seasonal Allergic Rhinitis: Synopsis of Guidance From the 2017 Joint Task Force on Practice Parameters. Ann Intern Med. 2017;177(12):876–81.

    Article  Google Scholar 

  39. Bousquet JJ, Schunemann HJ, Togias A, Erhola M, Hellings PW, Zuberbier T, et al. Next-generation ARIA care pathways for rhinitis and asthma: a model for multimorbid chronic diseases. Clin Transl Allergy. 2019;9:44.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Valiulis A, Bousquet J, Veryga A, Suprun U, Sergeenko D, Cebotari S, et al. Vilnius Declaration on chronic respiratory diseases: multisectoral care pathways embedding guided self-management, mHealth and air pollution in chronic respiratory diseases. Clin Transl Allergy. 2019;9:7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Bousquet J, Schunemann HJ, Togias A, Bachert C, Erhola M, Hellings PW, 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 e3.

Download references

Funding

MASK, SPAL (EU structural and development funds), unrestricted educational grant from Mylan.

Author information

Authors and Affiliations

  1. ISGlobal, Barcelona, Spain

    A. Bédard, X. Basagaña, J. M. Anto & J. Garcia-Aymerich

  2. CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain

    A. Bédard, X. Basagaña, J. M. Anto & J. Garcia-Aymerich

  3. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    A. Bédard, X. Basagaña, J. M. Anto & J. Garcia-Aymerich

  4. IMIM (Hospital del Mar Research Institute), Barcelona, Spain

    J. M. Anto

  5. UPRES EA220, Pôle des Maladies des Voies Respiratoires, Hôpital Foch, Université Paris-Saclay, Suresnes, France

    P. Devillier

  6. KYomed INNOV, Montpellier, France

    S. Arnavielhe & D. Laune

  7. CHU de Montpellier, Montpellier, France

    A. Bedbrook, G. L. Onorato & J. Bousquet

  8. Medical Consulting Czarlewski, Levallois, France

    W. Czarlewski

  9. Research fellow, OPC, and Director, Cambridge, UK

    R. Murray

  10. Faculdade de Medicina da Universidade do Porto, Lda Porto, Portugal

    R. Almeida & J. A. Fonseca

  11. Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal

    J. Correia da Sousa

  12. ICVS/3B’s, PT Government Associate Laboratory, Braga/Guimarães, Portugal

    J. Correia da Sousa

  13. Faculty of Pharmacy and Competence Center on Active and Healthy Ageing of University of Porto (Porto4Ageing), Porto, Portugal

    E. Costa

  14. Allergy Center, CUF Descobertas Hospital, Lisbon, Portugal

    M. Morais-Almeida

  15. Imunoalergologia, Centro Hospitalar Universitário de Coimbra and Faculty of Medicine, University of Coimbra, Coimbra, Portugal

    A. Todo-Bom

  16. SOS Allergology and Clinical Immunology, USL Toscana Centro, Prato, Italy

    L. Cecchi

  17. Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana, University of Salerno, Salerno, Italy

    G. De Feo

  18. Division for Health Innovation, Campania Region, Federico II University Hospital Naples (R&D and DISMET), Naples, Italy

    M. Illario

  19. CIRFF, Federico II University, Naples, Italy

    E. Menditto

  20. Department of Medical Sciences, Allergy and Clinical Immunology Unit, University of Torino, Mauriziano Hospital, Turin, Italy

    R. Monti

  21. Unit of Geriatric Immunoallergology, University of Bari Medical School, Bari, Italy

    C. Stellato

  22. Medical School Saint Antoine, Epidemiology of Allergic and Respiratory Diseases, Department Institute Pierre Louis of Epidemiology and Public Health, INSERM, Sorbonne Université, Paris, France

    M. T. Ventura & I. Annesi-Maesano

  23. Allergist La Rochelle, La Rochelle, France

    I. Bosse

  24. Allergist, Reims, France

    J. F. Fontaine

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

    N. Pham-Thi

  26. RNSA (Réseau National de Surveillance Aérobiologique), Brussieu, France

    M. Thibaudon

  27. Allergy Unit, Department of Dermatology, University Hospital of Zurich, Zurich, Switzerland

    P. Schmid-Grendelmeier

  28. Service Immunologie et Allergie, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland

    F. Spertini

  29. Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands

    N. H. Chavannes

  30. Department of Otorhinolaryngology, Amsterdam University Medical Centres, AMC, Amsterdam, The Netherlands

    W. J. Fokkens & S. Reitsma

  31. Clinic of Chest Diseases, Immunology and Allergology, Medical Faculty, Vilnius University, Vilnius, Lithuania

    R. Dubakiene

  32. Clinic of Children’s Diseases, Faculty of Medicine, Vilnius University, Vilnius, Lithuania

    R. Emuzyte

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

    V. Kvedariene

  34. Vilnius University Institute of Clinical Medicine, Clinic of Children’s Diseases, Department of Public Health, and Institute of Health Sciences, Vilnius, Lithuania

    A. Valiulis

  35. European Academy of Paediatrics, EAP/UEMS-SP), Brussels, Belgium

    A. Valiulis

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

    P. Kuna

  37. Department of Prevention of Environmental Hazards and Allergology, Medical University of Warsaw, Warsaw, Poland

    B. Samolinski

  38. Center for Rhinology and Allergology, Wiesbaden, Germany

    L. Klimek

  39. Medical Faculty, CRI-Clinical Research International-Ltd, Institute of Medical Statistics, and Computational Biology, University of Cologne, Hamburg, Germany

    R. Mösges & S. Shamai

  40. Department of Otorhinolaryngology, Head and Neck Surgery, Section of Rhinology and Allergy, University Hospital Marburg, Philipps-Universität Marburg, Marburg, Germany

    O. Pfaar

  41. Department of Internal Medicine, Medical University of Graz, Graz, Austria

    R. E. Roller-Wirnsberger

  42. Department of ENT, Medical University of Graz, Graz, Austria

    P. V. Tomazic

  43. Honorary Clinical Research Fellow, Allergy and Respiratory Research Group, The University of Edinburgh, Edinburgh, UK

    D. Ryan

  44. The Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK

    A. Sheikh

  45. Skin and Allergy Hospital, Helsinki University Hospital, and University of Helsinki, Helsinki, Finland

    T. Haahtela & S. Toppila-Salmi

  46. Department of Lung Diseases and Clinical Immunology, University of Turku, Terveystalo Allergy Clinic, Turku, Finland

    E. Valovirta

  47. Allergy Section, Department of Internal Medicine, Hospital Vall d’Hebron, ARADyAL Research Network, Barcelona, Spain

    V. Cardona

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

    J. Mullol

  49. Pneumology and Allergy Department CIBERES and Clinical, & Experimental Respiratory Immunoallergy, IDIBAPS, University of Barcelona, Barcelona, Spain

    A. Valero

  50. Allergy Unit “D Kalogeromitros”, 2nd Dpt of Dermatology and Venereology, National & Kapodistrian University of Athens, “Attikon” University Hospital, Athens, Greece

    M. Makris

  51. Division of Infection, & Respiratory Medicine, Royal Manchester Children’s Hospital Immunity, University of Manchester, Manchester, UK

    N. G. Papadopoulos

  52. Allergy Department, 2nd Pediatric Clinic, Athens General Children’s Hospital “P&A Kyriakou,” University of Athens, Athens, Greece

    N. G. Papadopoulos

  53. Department of Otorhinolaryngology, University of Crete School of Medicine, Heraklion, Greece

    E. P. Prokopakis

  54. Allergy Department, Athens Naval Hospital, Athens, Greece

    F. Psarros

  55. Upper Airways Research Laboratory, ENT Dept, Ghent University Hospital, Ghent, Belgium

    C. Bachert

  56. Sun Yat-sen University, International Airway Research Center, Guangzou, China

    C. Bachert

  57. Academic Medical Center, Univ of Amsterdam, Dept of Otorhinolaryngology, Univ Hospitals Leuven, Leuven, The Netherlands

    P. W. Hellings

  58. European Forum for Research and Education in Allergy and Airway Diseases (EUFOREA), Brussels, Belgium

    P. W. Hellings & B. Pugin

  59. Odense University Hospital, Department of Dermatology and Allergy Centre, Odense Research Center for Anaphylaxis (ORCA), Odense, Denmark

    C. Bindslev-Jensen & E. Eller

  60. Thermofisher Scientific, Uppsala, Sweden

    I. Kull

  61. Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Sach´s Children and Youth Hospital, Södersjukhuset, Sweden

    E. Melén

  62. Sachs’ Children and Youth Hospital, Stockholm and Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

    M. Wickman

  63. Centre for Clinical Research Sörmland, Uppsala University, Eskilstuna, Sweden

    G. De Vries & M. van Eerd

  64. Peercode BV, Geldermalsen, The Netherlands

    I. Agache

  65. Transylvania University Brasov, Brasov, Romania

    I. J. Ansotegui

  66. Department of Allergy and Immunology, Hospital Quirónsalud Bizkaia, Erandio, Spain

    S. Bosnic-Anticevich

  67. Woolcock Institute of Medical Research, University of Sydney, Woolcock Emphysema Centre, Sydney Local Health District, Glebe, NSW, Australia

    S. Bosnic-Anticevich

  68. Nucleo de Excelencia em Asma, Federal University of Bahia, Salvador, Brazil

    A. A. Cruz

  69. WHO GARD Planning Group, Salvador, Brazil

    A. A. Cruz

  70. Division of Allergy/Immunology, University of South Florida, Tampa, FLA, USA

    T. Casale

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

    J. C. Ivancevich

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

    D. E. Larenas-Linnemann

  73. Finnish Meteorological Institute (FMI), Helsinki, Finland

    M. Sofiev

  74. Nova Southeastern University, Fort Lauderdale, FL, USA

    D. Wallace

  75. Department of Medicine, Clinical Immunology and Allergy, McMaster University, Hamilton, ON, Canada

    S. Waserman

  76. Department of Pulmonary Diseases, Faculty of Medicine, Celal Bayar University, Manisa, Turkey

    A. Yorgancioglu

  77. University Hospital, Montpellier, France

    J. Bousquet

  78. INSERM U 1168, VIMA : Ageing and Chronic Diseases Epidemiological and Public Health Approaches, Villejuif, France

    J. Bousquet

  79. Université Versailles St-Quentin-en-Yvelines, UMR-S 1168, Montigny Le Bretonneux, France

    J. Bousquet

  80. Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Berlin, Germany

    J. Bousquet

  81. Department of Dermatology and Allergy, Berlin Institute of Health, Comprehensive Allergy Center, Berlin, Germany

    J. Bousquet

  82. Medscript, Paraparaumu, New Zealand

    R. Murray

  83. MEDIDA, Lda, Porto, Portugal

    J. A. Fonseca

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

    E. Costa

  85. Division of ENT Diseases, CLINTEC, Karolinska Institutet, Stockholm, Sweden

    C. Bachert

  86. Department of ENT Diseases, Karolinska University Hospital, Stockholm, Sweden

    C. Bachert

Authors
  1. A. Bédard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. X. Basagaña
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. M. Anto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Garcia-Aymerich
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P. Devillier
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. S. Arnavielhe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. A. Bedbrook
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. G. L. Onorato
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. W. Czarlewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. R. Murray
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. R. Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. J. A. Fonseca
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. J. Correia da Sousa
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. E. Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. M. Morais-Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. A. Todo-Bom
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. L. Cecchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. G. De Feo
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. M. Illario
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. E. Menditto
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. R. Monti
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. C. Stellato
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. M. T. Ventura
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. I. Annesi-Maesano
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. I. Bosse
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. J. F. Fontaine
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. N. Pham-Thi
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. M. Thibaudon
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. P. Schmid-Grendelmeier
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. F. Spertini
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. N. H. Chavannes
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. W. J. Fokkens
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. S. Reitsma
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. R. Dubakiene
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. R. Emuzyte
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. V. Kvedariene
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. A. Valiulis
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. P. Kuna
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. B. Samolinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. L. Klimek
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. R. Mösges
    View author publications

    You can also search for this author in PubMed Google Scholar

  42. O. Pfaar
    View author publications

    You can also search for this author in PubMed Google Scholar

  43. S. Shamai
    View author publications

    You can also search for this author in PubMed Google Scholar

  44. R. E. Roller-Wirnsberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  45. P. V. Tomazic
    View author publications

    You can also search for this author in PubMed Google Scholar

  46. D. Ryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  47. A. Sheikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  48. T. Haahtela
    View author publications

    You can also search for this author in PubMed Google Scholar

  49. S. Toppila-Salmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  50. E. Valovirta
    View author publications

    You can also search for this author in PubMed Google Scholar

  51. V. Cardona
    View author publications

    You can also search for this author in PubMed Google Scholar

  52. J. Mullol
    View author publications

    You can also search for this author in PubMed Google Scholar

  53. A. Valero
    View author publications

    You can also search for this author in PubMed Google Scholar

  54. M. Makris
    View author publications

    You can also search for this author in PubMed Google Scholar

  55. N. G. Papadopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  56. E. P. Prokopakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  57. F. Psarros
    View author publications

    You can also search for this author in PubMed Google Scholar

  58. C. Bachert
    View author publications

    You can also search for this author in PubMed Google Scholar

  59. P. W. Hellings
    View author publications

    You can also search for this author in PubMed Google Scholar

  60. B. Pugin
    View author publications

    You can also search for this author in PubMed Google Scholar

  61. C. Bindslev-Jensen
    View author publications

    You can also search for this author in PubMed Google Scholar

  62. E. Eller
    View author publications

    You can also search for this author in PubMed Google Scholar

  63. I. Kull
    View author publications

    You can also search for this author in PubMed Google Scholar

  64. E. Melén
    View author publications

    You can also search for this author in PubMed Google Scholar

  65. M. Wickman
    View author publications

    You can also search for this author in PubMed Google Scholar

  66. G. De Vries
    View author publications

    You can also search for this author in PubMed Google Scholar

  67. M. van Eerd
    View author publications

    You can also search for this author in PubMed Google Scholar

  68. I. Agache
    View author publications

    You can also search for this author in PubMed Google Scholar

  69. I. J. Ansotegui
    View author publications

    You can also search for this author in PubMed Google Scholar

  70. S. Bosnic-Anticevich
    View author publications

    You can also search for this author in PubMed Google Scholar

  71. A. A. Cruz
    View author publications

    You can also search for this author in PubMed Google Scholar

  72. T. Casale
    View author publications

    You can also search for this author in PubMed Google Scholar

  73. J. C. Ivancevich
    View author publications

    You can also search for this author in PubMed Google Scholar

  74. D. E. Larenas-Linnemann
    View author publications

    You can also search for this author in PubMed Google Scholar

  75. M. Sofiev
    View author publications

    You can also search for this author in PubMed Google Scholar

  76. D. Wallace
    View author publications

    You can also search for this author in PubMed Google Scholar

  77. S. Waserman
    View author publications

    You can also search for this author in PubMed Google Scholar

  78. A. Yorgancioglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  79. D. Laune
    View author publications

    You can also search for this author in PubMed Google Scholar

  80. J. Bousquet
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

the MASK study group

Contributions

JB proposed the study and is leading the analyses of MASK, he participated in  the writing of the paper; AB performed the analyses of the data and wrote the paper; XB, JMA JG, PD, IA proposed the analyses and reviewed the data and analyses; SA and GLO led the data management; AB is the MASK project manager and reviewed the English; WC and RM participated in the concept of the study and the review of the data; RA, JAF, JCS, MM, AT, EC included Portuguese patients; LC, GF, MI, EM, RM, CSt, MT Ventura included Italian patients; IB, JF Fontaine, N Pham-Thi included French patients; PS, FS included Swiss patients; NHC, WJF, S Reitsma included Dutch patients; RD, RE, VK, AV included Lithuanian patients; PK, BS included Polish patients; LK, RM, OP, SS included German patients; RER, PVT included Austrian patients; DR, AS included UK patients; TH, ST, EV included Finnish patients; VC, JM, AV included Spanish patients; MM, NGP, EPP, FP included Greek patients; CB, PWH, BP included Belgian patients; CB, E included Danish patients; IK, EM, MW included Swedish patients; MT and MS gave advice for pollen counts; GV, ME are the app developpers; IA, IJA, SB, AAC, TC, JCI, DL, DW, SW, AY are members of the MASK study group, they reviewed the protocol and the results; D Laune is a member of Kyomed Innov where the app data are monitored. All authors read and approved the final manuscript.

Corresponding author

Correspondence to J. Bousquet.

Ethics declarations

Ethics approval and consent to participate

The Allergy Diary is CE1 registered. By using k-anonymity, the data were all anonymized including the data related to geolocalization. MASK-air® is in line with the General Data Protection Regulation (GDPR) EU Directive 95/46/EC. Independent Review Board approval was not required since the study is observational and users agree to having their data analysed (terms of use).

Consent for publication

Not applicable.

Competing interests

Dr. Almeida reports grants from ERDF (European Regional Development Fund) through the operation POCI-01-0145-FEDER-029130 (“mINSPIRERS—mHealth para medição e melhoria da adesão à medicação nas doenças respiratórias obstrutivas crónicas—generalização e avaliação de tecnologias de gamificação, suporte por pares e processamento avançado de imagem”) funded by the Programa Operacional Competitividade e Internacionalização—COMPETE2020 and by National Funds through FCT (Fundação para a Ciência e a Tecnologia, during the conduct of the study.Dr. Ansotegui reports personal fees from Hikma, Roxall, Astra Zeneca, Menarini, UCB, Faes Farma, Sanofi, Mundipharma, outside the submitted work.Bachert: Meda, Stallergenes and ALK (speaker). J Bousquet reports personal fees and other from Chiesi, Cipla, Hikma, Menarini, Mundipharma, Mylan, Novartis, Sanofi-Aventis, Takeda, Teva, Uriach, outside the submitted work. other from Kyomed. Dr. Bosnic-Anticevich reports personal fees from Teva, Boehringer Ingelheim, Sanofi, GSK, AstraZeneca, outside the submitted work. Dr. Cardona reports personal fees from ALK, Allergopharma, Allergy Therapeutics, Diater, LETI, Thermofisher, Stallergenes, outside the submitted work. Dr. Cecchi reports personal fees from Menarini, Malesci, ALK Abellò, Menarini, non-financial support from Mundipharma, outside the submitted work.Dr. Correia-de-Sousa reports other from Boheringer Ingelheim, GSK, AstraZeneca, outside the submitted work. Dr. Cruz reports grants and personal fees from Astrazeneca, grants from GSK, personal fees from Boehringer Ingelheim, CHIESI, NOVARTIS, Eurofarma, MEDA Pharma, Boston Scientific, outside the submitted work. Dr. Devillier reports personal fees and non-financial support from Astra Zeneca, Mylan, Stallergenes Greer, personal fees from ALK Abello, Menarini, GlaxoSmithKline, Sanofi, outside the submitted work. Dr. Haahtela reports personal fees from Mundipharma, Novartis, and Orion Pharma, outside the submitted work. Dr. Eller reports other from Thermofisher Scientific, outside the submitted work. Dr. Fonseca reports personal fees from AstraZeneca, GSK, Novartis, Teva, grants from Mundipharma, outside the submitted work. Dr. Ivancevich reports personal fees from Faes Farma Euro-Farma Argentina, other from Lboratorios Casasco, Sanofi, outside the submitted work. Dr. Fokkens reports grants from Mylan, ALK, Allergy Therapeutics, GSK, Sanofi, Novartis, outside the submitted work. Dr. Klimek reports personal fees from MEDA, Sweden, Boehringer Ingelheim, Germany, grants and personal fees from ALK Abelló, Denmark, Novartis, Switzerland, Allergopharma, Germany, Bionorica, Germany, GSK, Great Britain, Lofarma, Italy, grants from Biomay, Austria, HAL, Netherlands, LETI, Spain, Roxall, Germany Bencard, Great Britain, outside the submitted work. Dr. Kuna reports personal fees from Adamed, Boehringer Ingelheim, AstraZeneca, Chiesi, FAES, Berlin Chemie, Novartis, Polpharma, Allergopharma,outside the submitted work. Dr Kvedariene has received payment for consultancy from GSK and for lectures from StallergensGreer, Berlin-CHemie outside the submitted work. Dr. Larenas Linnemann reports personal fees from Amstrong, Astrazeneca, Boehringer Ingelheim, Chiesi, DBV Technologies, Grunenthal, GSK, MEDA, Menarini, MSD, Novartis, Pfizer, Sanofi, Siegfried, UCB, grants from Sanofi, Astrazeneca, Novartis, UCB, GSK, TEVA, Boehringer Ingelheim, Chiesi, outside the submitted work. Dr. Melén reports personal fees from Advisory board reimbursement from Novartis, outside the submitted work. Dr. Ralph Mösges reports personal fees from ALK, allergopharma, Allergy Therapeutics, Hexal, personal fees from Servier, personal fees from Klosterfrau, Stada, UCB, Friulchem, grants from ASIT biotech, Nuvo, Bayer, FAES, GSK, MSD, Johnson&Johnson, Meda, Optima, Ursapharm, BitopAG, Hulka, grants and personal fees from Bencard, grants from Leti, Stallergenes, grants, personal fees and non-financial support from Lofarma, non-financial support from Roxall, from Atmos, from Bionorica, Otonomy, Ferrero, personal fees and non-financial support from Novartis, outside the submitted work. Dr. Papadopoulos reports grants from Gerolymatos, personal fees from Hal Allergy B.V., Novartis Pharma AG, Menarini, Hal Allergy B.V., Mylan, outside the submitted work. Dr. Pfaar reports grants and personal fees from ALK-Abelló, Allergopharma, Stallergenes Greer, HAL Allergy Holding B.V./HAL Allergie GmbH, Bencard Allergie GmbH/Allergy Therapeutics, Lofarma, ASIT Biotech Tools S.A., Laboratorios LETI/LETI Pharma, Anergis S.A., grants from Glaxo Smith Kline, Biomay, Nuvo, Circassia, personal fees from MEDA Pharma/MYLAN, Mobile Chamber Experts (a GA2LEN Partner Indoor Biotechnologies, Astellas Pharma Global, outside the submitted work. Dr. Ryan reports personal fees from Mylan, AZ, AZ, GSK, from Chiesi, BI, grants from Mylan, outside the submitted work.Dr Schmid-Grendelmeier reports honarium and consultancy fees from ALK-Abello, Allergopharma, Bencard, MEDA and Stallergenes Dr. Stelmach reports grants from São Paulo Research Foundation, MSD, grants and personal fees from Novartis, grants, personal fees and non-financial support from AstraZeneca, Chiesi, personal fees and non-financial support from Boheringer Ingelheim, outside the submitted work. Dr. Todo-Bom reports grants and personal fees from Novartis, Mundipharma, GSK Teva Pharma, personal fees from AstraZeneca, grants from Boehringer Ingelheim, Sanofi, Leti, outside the submitted work. Dr. Toppila-Salmi reports other from Biomedical Systems Ltd., Roche Ltd., grants from Erkko Foundation, outside the submitted work. Dr. Waserman reports other from CSL Behring Shire, AstraZeneca, Teva, Meda, other from Merck, GSK, Novartis, Pediapharm, Aralez Sanofi, Stallergenes, outside the submitted work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bédard, A., Basagaña, X., Anto, J.M. et al. Treatment of allergic rhinitis during and outside the pollen season using mobile technology. A MASK study. Clin Transl Allergy 10, 62 (2020). https://doi.org/10.1186/s13601-020-00342-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13601-020-00342-x

Keywords

Clinical and Translational Allergy

ISSN: 2045-7022

Contact us