BACKGROUND:Adherence to rhinitis treatment has been insufficiently assessed. We aimed to use data from the MASK-air mHealth app to assess adherence to oral antihistamines (OAH), intra-nasal corticosteroids (INCS) or azelastine-fluticasone in patients with allergic rhinitis. METHODS:We included regular European MASK-air users with self-reported allergic rhinitis and reporting at least 1 day of OAH, INCS or azelastine-fluticasone. We assessed weeks during which patients answered the MASK-air questionnaire on all days. We restricted our analyses to data provided between January and June, to encompass the pollen seasons across the different assessed countries. We analysed symptoms using visual analogue scales (VASs) and the combined symptom-medication score (CSMS), performing stratified analyses by weekly adherence levels. Medication adherence was computed as the proportion of days in which patients reported rhinitis medication use. Sensitivity analyses were performed considering all weeks with at most 1 day of missing data and all months with at most 4 days of missing data. RESULTS:We assessed 8212 complete weeks (1361 users). Adherence (use of medication > 80% days) to specific drug classes ranged from 31.7% weeks for azelastine-fluticasone to 38.5% weeks for OAH. Similar adherence to rhinitis medication was found in users with or without self-reported asthma, except for INCS (better adherence in asthma patients). VAS and CSMS levels increased from no adherence to full adherence, except for INCS. A higher proportion of days with uncontrolled symptoms was observed in weeks with higher adherence. In full adherence weeks, 41.2% days reported rhinitis co-medication. The sensitivity analyses displayed similar results. CONCLUSIONS:A high adherence was found in patients reporting regular use of MASK-air. Different adherence patterns were found for INCS compared to OAH or azelastine-fluticasone that are likely to impact guidelines.

Health is "a state of complete physical, mental and social well-being, and not merely the absence of disease or infirmity". Health must therefore be considered in its entirety. To begin with, however, it is vital to move swiftly from the concept of holistic health to its practical implementation. This is what the first Digital Health Network symposium proposed on May 30 and 31, 2024. The Digital Health Network was originally set up by a group of players from the healthcare sector, industry, research and elected representatives, with the aim of pooling feedback from experience as close as possible to local realities. The following points were addressed: i) rebalancing the curative approach with a greater emphasis on prevention and health promotion; ii) rebalancing the genetic approach with a greater emphasis on exposomics; iii) insufficient use of digital technology to improve the health of local populations; iv) governance and organizational challenges from the point of view of local decision-makers and observers, with a focus on research; and v) the ambitions of the Digital Health Network. The symposium provided an opportunity to share case studies of public health challenges in local areas, and possible responses in terms of infrastructure and stakeholder participation: citizens, patients, healthcare professionals, service providers, experts and other stakeholders. The challenges of ageing and geriatric frailty underline the importance of a global, preventive and multidisciplinary approach to promote successful, independent ageing.

AbstractBackgroundAn innovation to better manage cat‐allergic patients utilises anti‐Fel d 1 IgY antibodies to neutralise Fel d 1 after its production by the cat. However, there is no published study showing its clinical efficacy in humans in a home setting. A longitudinal, open‐label, proof‐of‐concept study was carried out to approach clinical efficacy of the cat food in cat‐allergic patients.MethodsAfter a baseline evaluation, the cats ate only the cat food for the following 4 months. Daily evaluation of efficacy was performed for 2 weeks at baseline and after 1, 2 and 3 months of intervention for periods of 2 weeks. The MASK‐air app was used daily to assess symptoms, work productivity and medications.ResultsOf the 49 patients screened, 42 were followed up and 33 (78.5%) reported MASK‐air data at all 3 evaluation periods. The primary end point (visual analogue scale [VAS] for global allergy symptoms) was significantly improved (p < 0.0001). All symptoms (VAS nose, eye, and asthma), VAS work and the combined symptom‐medication score significantly improved after 1 month. The percentage of uncontrolled days (VAS>20/100) decreased from 64% at baseline to 35% at 1 month (p < 0.0001) and 14% at 3 months. A sensitivity analysis in patients with uncontrolled disease at baseline found similar results.DiscussionA cat diet containing anti‐Fel d 1 antibodies was able to (i) show decreased allergic symptoms and related outcomes, (ii) inform the design and feasibility of future studies with a control arm and (iii) estimate the sample size of the study.Study registration number: clinicaltrials.gov: NCT05656482.

AbstractRationaleIt is unclear how each individual asthma symptom is associated with asthma diagnosis or control.ObjectivesTo assess the performance of individual asthma symptoms in the identification of patients with asthma and their association with asthma control.MethodsIn this cross‐sectional study, we assessed real‐world data using the MASK‐air® app. We compared the frequency of occurrence of five asthma symptoms (dyspnea, wheezing, chest tightness, fatigue and night symptoms, as assessed by the Control of Allergic Rhinitis and Asthma Test [CARAT] questionnaire) in patients with probable, possible or no current asthma. We calculated the sensitivity, specificity and predictive values of each symptom, and assessed the association between each symptom and asthma control (measured using the e‐DASTHMA score). Results were validated in a sample of patients with a physician‐established diagnosis of asthma.Measurement and Main ResultsWe included 951 patients (2153 CARAT assessments), with 468 having probable asthma, 166 possible asthma and 317 no evidence of asthma. Wheezing displayed the highest specificity (90.5%) and positive predictive value (90.8%). In patients with probable asthma, dyspnea and chest tightness were more strongly associated with asthma control than other symptoms. Dyspnea was the symptom with the highest sensitivity (76.1%) and the one consistently associated with the control of asthma as assessed by e‐DASTHMA. Consistent results were observed when assessing patients with a physician‐made diagnosis of asthma.ConclusionsWheezing and chest tightness were the asthma symptoms with the highest specificity for asthma diagnosis, while dyspnea displayed the highest sensitivity and strongest association with asthma control.

Biomarkers for the diagnosis, treatment and follow-up of patients with rhinitis and/or asthma are urgently needed. Although some biologic biomarkers exist in specialist care for asthma, they cannot be largely used in primary care. There are no validated biomarkers in rhinitis or allergen immunotherapy (AIT) that can be used in clinical practice. The digital transformation of health and health care (including mHealth) places the patient at the center of the health system and is likely to optimize the practice of allergy. Allergic Rhinitis and its Impact on Asthma (ARIA) and EAACI (European Academy of Allergy and Clinical Immunology) developed a Task Force aimed at proposing patient-reported outcome measures (PROMs) as digital biomarkers that can be easily used for different purposes in rhinitis and asthma. It first defined control digital biomarkers that should make a bridge between clinical practice, randomized controlled trials, observational real-life studies and allergen challenges. Using the MASK-air app as a model, a daily electronic combined symptom-medication score for allergic diseases (CSMS) or for asthma (e-DASTHMA), combined with a monthly control questionnaire, was embedded in a strategy similar to the diabetes approach for disease control. To mimic real-life, it secondly proposed quality-of-life digital biomarkers including daily EQ-5D visual analogue scales and the bi-weekly RhinAsthma Patient Perspective (RAAP). The potential implications for the management of allergic respiratory diseases were proposed.

MASK-air® , a validated mHealth app (Medical Device regulation Class IIa) has enabled large observational implementation studies in over 58,000 people with allergic rhinitis and/or asthma. It can help to address unmet patient needs in rhinitis and asthma care. MASK-air® is a Good Practice of DG Santé on digitally-enabled, patient-centred care. It is also a candidate Good Practice of OECD (Organisation for Economic Co-operation and Development). MASK-air® data has enabled novel phenotype discovery and characterisation, as well as novel insights into the management of allergic rhinitis. MASK-air® data show that most rhinitis patients (i) are not adherent and do not follow guidelines, (ii) use as-needed treatment, (iii) do not take medication when they are well, (iv) increase their treatment based on symptoms and (v) do not use the recommended treatment. The data also show that control (symptoms, work productivity, educational performance) is not always improved by medications. A combined symptom-medication score (ARIA-EAACI-CSMS) has been validated for clinical practice and trials. The implications of the novel MASK-air® results should lead to change management in rhinitis and asthma.

Summary: Background: Validated questionnaires are used to assess asthma control over the past 1–4 weeks from reporting. However, they do not adequately capture asthma control in patients with fluctuating symptoms. Using the Mobile Airways Sentinel Network for airway diseases (MASK-air) app, we developed and validated an electronic daily asthma control score (e-DASTHMA). Methods: We used MASK-air data (freely available to users in 27 countries) to develop and assess different daily control scores for asthma. Data-driven control scores were developed based on asthma symptoms reported by a visual analogue scale (VAS) and self-reported asthma medication use. We included the daily monitoring data from all MASK-air users aged 16–90 years (or older than 13 years to 90 years in countries with a lower age of digital consent) who had used the app in at least 3 different calendar months and had reported at least 1 day of asthma medication use. For each score, we assessed construct validity, test–retest reliability, responsiveness, and accuracy. We used VASs on dyspnoea and work disturbance, EQ-5D-VAS, Control of Allergic Rhinitis and Asthma Test (CARAT), CARAT asthma, and Work Productivity and Activity Impairment: Allergy Specific (WPAI:AS) questionnaires as comparators. We performed an internal validation using MASK-air data from Jan 1 to Oct 12, 2022, and an external validation using a cohort of patients with physician-diagnosed asthma (the INSPIRERS cohort) who had had their diagnosis and control (Global Initiative for Asthma [GINA] classification) of asthma ascertained by a physician. Findings: We studied 135 635 days of MASK-air data from 1662 users from May 21, 2015, to Dec 31, 2021. The scores were strongly correlated with VAS dyspnoea (Spearman correlation coefficient range 0·68–0·82) and moderately correlated with work comparators and quality-of-life-related comparators (for WPAI:AS work, we observed Spearman correlation coefficients of 0·59–0·68). They also displayed high test–retest reliability (intraclass correlation coefficients range 0·79–0·95) and moderate-to-high responsiveness (correlation coefficient range 0·69–0·79; effect size measures range 0·57–0·99 in the comparison with VAS dyspnoea). The best-performing score displayed a strong correlation with the effect of asthma on work and school activities in the INSPIRERS cohort (Spearman correlation coefficients 0·70; 95% CI 0·61–0·78) and good accuracy for the identification of patients with uncontrolled or partly controlled asthma according to GINA (area under the receiver operating curve 0·73; 95% CI 0·68–0·78). Interpretation: e-DASTHMA is a good tool for the daily assessment of asthma control. This tool can be used as an endpoint in clinical trials as well as in clinical practice to assess fluctuations in asthma control and guide treatment optimisation. Funding: None.

Allergen immunotherapy (AIT) is an effective treatment for allergic rhinitis and has been hypothesised as rapidly effective.1 Rush subcutaneous AIT to pollen and mites reduces skin test reactivity to allergens within days, in a dose-dependent and time-independent manner.2, 3 Venom rush AIT is also effective within days. The tolerance of beekeepers to bee stings fades in some individuals and is also re-installed after a few stings in a dose-dependent and time-independent manner.4 Finally, desensitisation to drug allergy is effective within hours and there is a refractory period after tolerance.5 These short-term clinical sequences cannot be explained by an adaptive immune response (immunotherapy) but may be related to rapid and short-lasting cell downregulation responses (desensitisation).1, 6 These considerations have prompted the hypothesis that sublingual immunotherapy (SLIT) may induce a rapid relief of allergic symptoms during the pollen season.1 While previous studies have found that SLIT is effective in the same allergy season as when first introduced,7 no study has ever assessed its efficacy on a daily basis. Therefore, in this study, we aimed to assess whether days of SLIT use were associated with better allergy control during the expected pollen season. Such analyses may hint at a potential short-term effect of SLIT, to be assessed by proper studies. MASK-air® is a free mobile app available in 27 countries. The app includes a daily monitoring questionnaire which can be answered on a daily basis. The questionnaire assesses (i) the daily severity and impact of allergy symptoms (through four mandatory visual analogue scales—VASs),8 (ii) the daily rhinitis and asthma medication used on that day by the patient and (iii) whether the patient used AIT on that day. Such information allows the computation of the combined symptom-medication score (CSMS), assessing the daily control of allergic rhinitis.9 We included the daily monitoring data of European MASK-air® users (i) aged between 16 years (or lower—not below 13 years—for countries with a lower age of digital consent) and 90 years, (ii) with a self-reported diagnosis of allergic rhinitis and (iii) on SLIT for grass pollen. We analysed the data provided during the months of May and June (assuming that they corresponded to the grass pollen season in Europe) from 2015 to 2021. We performed a cross-sectional analysis, in which we studied all days reported between May and June for three different samples: (S1) all users under SLIT (of any type) for grass pollen; (S2) users taking SLIT tablets for grass pollen and (S3) users using SLIT (of any type) for grass pollen and reporting at least 1 day of AIT use during the studied period (to account for non-adherence and potential incorrect reporting of SLIT use). Sample 1 was used to assess a sufficiently-powered group. The robustness of the results of SLIT in S1 was assessed in S2 and S3, where different inclusion criteria were adopted. We performed this cross-sectional analysis by building multivariable mixed-effects regression models to assess, for each sample, whether days of AIT use were associated with a better allergic rhinitis control (with the dependent variables being either the CSMS or the VAS quantifying the impact of global allergy symptoms—'VAS global'). In our models, we considered the clustering of observations by users, by country and by month of the year, setting these variables as random effects (i.e. we clustered observations by users, by the user's country and by the month of the year). In addition, results were further adjusted for the following independent variables which were included in our regression models: baseline domains impacted by allergic rhinitis, baseline symptoms of allergic rhinitis, patients' gender and age, self-reported diagnosis of asthma, occurrence of conjunctivitis and use of daily rhinitis or asthma medication in monotherapy or co-medication. We also performed a longitudinal analysis, in which we analysed complete periods of 2 weeks (missing at most an average of 2 days per week) of patients using SLIT for grass pollen and reporting at least 1 day of AIT use during the same period. We performed the longitudinal study in a smaller subset of users, comparing days of AIT use versus days when AIT was not used on the CSMS and VAS global. For such comparisons, we built multivariable mixed-effects regression models similar to those applied in cross-sectional analyses. When responding to the MASK-air® daily questionnaire, it is not possible to skip any of the questions and data are saved only after the final answer. This precludes missing data within each questionnaire. P-values <.05 were considered statistically significant. A Holm–Bonferroni correction was applied to account for multiple analyses. Additional information about study methods and findings is available in the following repository: https://github.com/BernardoSousaPinto/Improvement-of-daily-allergy-control-by-sublingual-immunotherapy-A-MASK-air-study MASK-air® is CE1-registered and complies with the General Data Protection Regulation. All data were anonymised prior to the study. Users agreed to having their data analysed for scientific purposes in the Terms of Use. An independent review board approval was not required for this study. In the cross-sectional analysis, we studied 3968 days from 171 patients in S1. Immunotherapy was reported in 2380 (60.0%) days. There were 111 users (N = 2311 days; S2) using SLIT tablets for grass pollen. Finally, 113 users (N = 3098 days; S3) indicated that they were under SLIT for grass pollen allergy and reported the use of AIT for at least 1 day. In the longitudinal analysis, we studied 2615 days from 45 patients. Immunotherapy was reported in 2026 (77.5%) days. The baseline characteristics of the users were not similar in all samples. The median CSMS and VAS global levels tended to be lower in days with SLIT. In days of MASK-air® use, the average adherence to SLIT was 54.8%. Considering only users reporting at least 1 day of SLIT use, the average adherence was 83.0%. In S1, SLIT days were associated with improved CSMS (regression coefficient = −2.2; 95%CI = −3.1; −1.3; p < .001) and VAS global (regression coefficient = −3.0; 95%CI = −4.5; −1.5; p < .001) (Table 1; Figure 1). SLIT days were also associated with decreased CSMS and VAS global in S2 and S3 (Table 1; Figure 1). Higher adherence to the MASK-air® app was found to be associated with lower CSMS and VAS global. Ancillary analyses were performed (i) in grass pollen SLIT users reporting at least 4 days of MASK-air® use, (ii) comparing patients with high/variable median CSMS versus those with low median CSMS and (iii) considering the effect of SLIT given on the previous day. Similar results were observed in all these analyses, except when, for days without SLIT, we compared days when SLIT had been used on the previous day versus days on which this had not occurred. Results of the longitudinal study were comparable to those of the cross-sectional study (Figure 1), with SLIT days being associated with lower CSMS (regression coefficient = −2.6; 95%CI = −3.6; −1.6) and VAS global (regression coefficient = −4.0; 95%CI = −5.6; −2.3). This study using MASK-air® real-world data suggests that in patients under SLIT during the pollen season, AIT may have a very short-term effect. In particular, in patients under SLIT, days with AIT were associated with better allergy control than those without AIT. However, real-world data are only hypothesis-generating (considering the study design, we should be particularly careful when dealing with temporality) and such hypotheses require confirmation by future well-designed and sufficiently-powered trials. This study has several strengths, including its multinational scope and the large volume of data analysed. In addition, the CSMS and MASK-air® VASs display medium-high validity, reliability and responsiveness.8, 9 Finally, we analysed three samples in cross-sectional analyses and performed a longitudinal analysis, observing robust results. This study also has limitations of which some are common to other mHealth observational studies (e.g. selection biases resulting from overrepresentation of younger users and information biases related to the self-reported nature of the data).8 In addition, there are specific limitations: (i) This is mostly a cross-sectional study, impairing the establishment of causality or of temporal relationships, and therefore being capable only of generating new hypotheses. Although we performed a longitudinal analysis, it encompassed only 45 patients. (ii) We do not have information on the date each patient started using SLIT. We are therefore unable to distinguish patients who have been on SLIT for a period long enough to allow reaching an optimal control versus those who have just started using SLIT. (iii) The grass pollen season has been roughly estimated, considering the period of May–June. (iv) We did not consider the different SLIT products in this study. These different products display highly variable standardisation, allergen content and clinical documentation of efficacy and safety. Such a limitation, however, has been partly overcome by the assessment of S2, as, in Europe, there are only a limited set of available products for grass pollen SLIT tablets. The results of this study raise the hypothesis that SLIT may have a short-term effectiveness. If confirmed in future studies, this may provide a novel strategy in patients allergic to pollens who are uncontrolled despite optimal pharmacotherapy. However, several limitations of the study should be considered and its results should be understood as hypothesis-generating, building the basis for future studies. Jean Bousquet proposed the concept of the paper, analysed the results and wrote the paper. Bernardo Sousa-Pinto made the statistical plan, performed the analysis and wrote the paper. Josep M Anto, G Walter Canonica, Wienczyslawa Czarlewski, Philippe Devillier, Tari Haahtela, Daniel Laune, Joaquim Mullol, Marek Jutel, Piotr Kuna, Mohamed H Shamji, Erkka Valovirta, Torsten Zuberbier and Joao A Fonseca represented the think tank and participated in the analysis and in the writing of the paper. Ludger Klimek, Luisa Brussino, Lorenzo Cecchi, Violeta Kvedariene, Mario Morais-Almeida, Ralph Mösges, Marek Niedoszytko, Nikolaos G Papadopoulos, Vincenzo Patella, Nhân Pham-Thi, Boleslaw Samolinski, Luis Taborda-Barata31, Sanna Toppila-Salmi, Joaquin Sastre, Arunas Valiulis and Maria Teresa Ventura proposed the MASK-air app to their patients. Oliver Pfaar participated in the concept of the study and wrote the paper. All authors have read the paper and given their final approval for submission. JB reports personal fees from Cipla, Menarini, Mylan, Novartis, Purina, Sanofi-Aventis, Teva, Uriach, other from KYomed-Innov, other from Mask-air-SAS, outside the submitted work. LC reports personal fees from Thermofisher, personal fees from Sanofi, personal fees from Astra Zeneca, personal fees from Novartis, outside the submitted work. PD reports personal fees from ALK Abello, personal fees and non-financial support from Boehringer Ingelheim, personal fees from Chiesi, personal fees and non-financial support from Astra Zeneca, personal fees from GlaxoSmithKline, personal fees from Menarini, personal fees from Novartis, personal fees and non-financial support from Stallergenes, personal fees from Sanofi, outside the submitted work. JAF reports being co-founder of an SME that develops mHealth technologies, such as digital biomarkers and has the copyright of the CARAT and a CARATkids PROM. TH reports personal fees from Orion Pharma, outside the submitted work. MJ reports personal fees from ALK-Abello, personal fees from Allergopharma, personal fees from Stallergenes, personal fees from Anergis, personal fees from Allergy Therapeutics, personal fees from Leti, personal fees from HAL, during the conduct of the study; personal fees from GSK, personal fees from Novartis, personal fees from Teva, personal fees from Takeda, personal fees from Chiesi, outside the submitted work. LK reports grants and personal fees from Allergopharma, grants and personal fees from Viatris, personal fees from HAL Allergie, personal fees from ALK Abelló, grants and personal fees from LETI Pharma, grants and personal fees from Stallergenes, grants from Quintiles, grants and personal fees from Sanofi, grants from ASIT biotech, grants from Lofarma, personal fees from Allergy Therapeut., grants from AstraZeneca, grants and personal fees from GSK, grants from Inmunotek, personal fees from Cassella med, personal fees from Novartis, personal fees from Regeneron Pharmaceuticals, personal fees from ROXALL Medizin GmbH, outside the submitted work; and Membership: AeDA DGHNO Deutsche Akademie für Allergologie und klinische Immunologie HNO-BV GPA EAACI. PK reports personal fees from Adamed, personal fees from Berlin Chemie Menarini, personal fees from AstraZeneca, personal fees from Boehringer Ingelheim, personal fees from Celon Pharma, personal fees from Polpharma, personal fees from Teva, personal fees from Novartis, personal fees from Glenmark, personal fees from Zentiva, outside the submitted work. VK reports other from NORAMEDA, outside the submitted work. RM reports personal fees from ALK, grants from ASIT biotech, personal fees from allergopharma, personal fees from Allergy Therapeutics, grants and personal fees from Bencard, grants from Leti, grants, personal fees and non-financial support from Lofarma, non-financial support from Roxall, grants and personal fees from Stallergenes, grants from Optima, personal fees from Friulchem, personal fees from Hexal, personal fees from Servier, personal fees from Klosterfrau, non-financial support from Atmos, personal fees from Bayer, non-financial support from Bionorica, personal fees from FAES, personal fees from GSK, personal fees from MSD, personal fees from Johnson&Johnson, personal fees from Meda, personal fees and non-financial support from Novartis, non-financial support from Otonomy, personal fees from Stada, personal fees from UCB, non-financial support from Ferrero, grants from BitopAG, grants from Hulka, personal fees from Nuvo, grants and personal fees from Ursapharm, personal fees from Menarini, personal fees from Mundipharma, personal fees from Pohl-Boskamp, grants from Inmunotek, grants from Cassella-med GmbH & Co. KG, personal fees from Laboratoire de la Mer, personal fees from Sidroga, grants and personal fees from HAL BV, personal fees from Lek, personal fees from PRO-AdWise, personal fees from Angelini Pharma, grants and non-financial support from JGL, outside the submitted work. JM reports personal fees and other from SANOFI-GENZYME & REGENERON, personal fees and other from NOVARTIS, grants and personal fees from VIATRIS, grants and personal fees from URIACH Group, personal fees from Mitsubishi-Tanabe, personal fees from Menarini, personal fees from UCB, personal fees from AstraZeneca, grants and personal fees from GSK, personal fees from MSD, outside the submitted work. NGP reports personal fees from NOVARTIS, personal fees from NUTRICIA, personal fees from HAL, personal fees from MENARINI/FAES FARMA, personal fees from SANOFI/REGENERON, personal fees from MYLAN, personal fees from ASTRA ZENECA, personal fees from GSK, grants from VIANEX, grants from REG, grants from CAPRICARE, grants from NESTLE, grants from NUMIL, personal fees from ABBOTT, personal fees from ABBVIE, personal fees from OM PHARMA, personal fees from MEDSCAPE, outside the submitted work. OP reports grants and personal fees from ALK-Abelló, grants and personal fees from Allergopharma, grants and personal fees from Stallergenes Greer, grants and personal fees from HAL Allergy Holding B.V./HAL Allergie GmbH, grants and personal fees from Bencard Allergie GmbH/Allergy Therapeutics, grants and personal fees from Lofarma, grants from Biomay, grants from Circassia, grants and personal fees from ASIT Biotech Tools S.A., grants and personal fees from Laboratorios LETI/LETI Pharma, personal fees from MEDA Pharma/MYLAN, grants and personal fees from Anergis S.A., personal fees from Mobile Chamber Experts (a GA2LEN Partner), personal fees from Indoor Biotechnologies, grants and personal fees from GlaxoSmithKline, personal fees from Astellas Pharma Global, personal fees from EUFOREA, personal fees from ROXALL Medizin, personal fees from Novartis, personal fees from Sanofi-Aventis and Sanofi-Genzyme, personal fees from Med Update Europe GmbH, personal fees from streamedup. GmbH, grants from Pohl-Boskamp, grants from Inmunotek S.L., personal fees from John Wiley and Sons, AS, personal fees from Paul-Martini-Stiftung (PMS), personal fees from Regeneron Pharmaceuticals Inc., personal fees from RG Aerztefortbildung, personal fees from Institut für Disease Management, personal fees from Springer GmbH, grants and personal fees from AstraZeneca, personal fees from IQVIA Commercial, personal fees from Ingress Health, personal fees from Wort&Bild Verlag, personal fees from Verlag ME, personal fees from Procter&Gamble, personal fees from Alfried-Krupp Krankenhaus, Essen, outside the submitted work; and member of EAACI Excom, member of ext. board of directors DGAKI; coordinator, main- or co-author of different position papers and guidelines in rhinology, allergology and allergen-immunotherapy. BS reports personal fees from Polpharma, personal fees from Viatris, grants and personal fees from AstraZeneca, personal fees from TEVA, personal fees from patient ombudsman, personal fees from Polish Allergology Society, grants from GSK, outside the submitted work. JS reports grants and personal fees from SANOFI, personal fees from GSK, personal fees from NOVARTIS, personal fees from ASTRA ZENECA, personal fees from MUNDIPHARMA, personal fees from FAES FARMA, outside the submitted work. LTB reports other from LETI, other from Vitoria Laboratories, other from Novartis, other from Diater Laboratories, during the conduct of the study. STS reports other from ERT, from Novartis, from Sanofi Pharma, from Roche Products, grants from GSK, outside the submitted work. TZ reports grants and personal fees from Novartis, grants and personal fees from Henkel, personal fees from Bayer, personal fees from FAES, personal fees from Astra Zeneca, personal fees from AbbVie, personal fees from ALK, personal fees from Almirall, personal fees from Astellas, personal fees from Bayer, personal fees from Bencard, personal fees from Berlin Chemie, personal fees from FAES, personal fees from Hal, personal fees from Leti, personal fees from Mesa, personal fees from Menarini, personal fees from Merck, personal fees from MSD, personal fees from Novartis, personal fees from Pfizer, personal fees from Sanofi, personal fees from Stallergenes, personal fees from Takeda, personal fees from Teva, personal fees from UCB, personal fees from Henkel, personal fees from Kryolan, personal fees from L'Oreal, outside the submitted work and 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). The other authors have no COIs to disclose, outside the submitted work. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

BACKGROUND: In clinical and epidemiological studies, cutoffs of patient-reported outcome measures can be used to classify patients into groups of statistical and clinical relevance. However, visual analog scale (VAS) cutoffs in MASK-air have not been tested. OBJECTIVE: To calculate cutoffs for VAS global, nasal, ocular, and asthma symptoms.METHODS: In a cross-sectional study design of all MASK-air participants, we compared (1) approaches based on the percen-tiles (tertiles or quartiles) of VAS distributions and (2) data -driven approaches based on clusters of data from 2 comparators (VAS work and VAS sleep). We then performed sensitivityanalyses for individual countries and for VAS levels corre-sponding to full allergy control. Finally, we tested the different approaches using MASK-air real-world cross-sectional and lon-gitudinal data to assess the most relevant cutoffs.RESULTS: We assessed 395,223 days from 23,201 MASK-air users with self-reported allergic rhinitis. The percentile-oriented approach resulted in lower cutoff values than the data-driven approach. We obtained consistent results in the data-driven approach. Following the latter, the proposed cutoff differenti-ating "controlled" and "partly-controlled" patients was similar to the cutoff value that had been arbitrarily used (20/100). However, a lower cutoff was obtained to differentiate between "partly-controlled" and "uncontrolled" patients (35 vs the arbitrarily-used value of 50/100).CONCLUSIONS: Using a data-driven approach, we were able to define cutoff values for MASK-air VASs on allergy and asthma symptoms. This may allow for a better classification of patients with rhinitis and asthma according to different levels of control, supporting improved disease management. (c) 2022 American Academy of Allergy, Asthma & Immunology (J Allergy Clin Immunol Pract 2023;11:1281-9)

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.

Background Validated combined symptom-medication scores (CSMSs) are needed to investigate the effects of allergic rhinitis treatments. This study aimed to use real-life data from the MASK-air(R) app to generate and validate hypothesis- and data-driven CSMSs. Methods We used MASK-air(R) data to assess the concurrent validity, test-retest reliability and responsiveness of one hypothesis-driven CSMS (modified CSMS: mCSMS), one mixed hypothesis- and data-driven score (mixed score), and several data-driven CSMSs. The latter were generated with MASK-air(R) data following cluster analysis and regression models or factor analysis. These CSMSs were compared with scales measuring (i) the impact of rhinitis on work productivity (visual analogue scale [VAS] of work of MASK-air(R), and Work Productivity and Activity Impairment: Allergy Specific [WPAI-AS]), (ii) quality-of-life (EQ-5D VAS) and (iii) control of allergic diseases (Control of Allergic Rhinitis and Asthma Test [CARAT]). Results We assessed 317,176 days of MASK-air(R) use from 17,780 users aged 16-90 years, in 25 countries. The mCSMS and the factor analyses-based CSMSs displayed poorer validity and responsiveness compared to the remaining CSMSs. The latter displayed moderate-to-strong correlations with the tested comparators, high test-retest reliability and moderate-to-large responsiveness. Among data-driven CSMSs, a better performance was observed for cluster analyses-based CSMSs. High accuracy (capacity of discriminating different levels of rhinitis control) was observed for the latter (AUC-ROC = 0.904) and for the mixed CSMS (AUC-ROC = 0.820). Conclusion The mixed CSMS and the cluster-based CSMSs presented medium-high validity, reliability and accuracy, rendering them as candidates for primary endpoints in future rhinitis trials.

INTRODUCTION:Data from mHealth apps can provide valuable information on rhinitis control and treatment patterns. However, in MASK-air®, these data have only been analyzed cross-sectionally, without considering the changes of symptoms over time. We analyzed data from MASK-air® longitudinally, clustering weeks according to reported rhinitis symptoms.METHODS:We analyzed MASK-air® data, assessing the weeks for which patients had answered a rhinitis daily questionnaire on all 7 days. We firstly used k-means clustering algorithms for longitudinal data to define clusters of weeks according to the trajectories of reported daily rhinitis symptoms. Clustering was applied separately for weeks when medication was reported or not. We compared obtained clusters on symptoms and rhinitis medication patterns. We then used the latent class mixture model to assess the robustness of results.RESULTS:We analyzed 113,239 days (16,177 complete weeks) from 2590 patients (mean age ± SD = 39.1 ± 13.7 years). The first clustering algorithm identified ten clusters among weeks with medication use: seven with low variability in rhinitis control during the week and three with highly-variable control. Clusters with poorly-controlled rhinitis displayed a higher frequency of rhinitis co-medication, a more frequent change of medication schemes and more pronounced seasonal patterns. Six clusters were identified in weeks when no rhinitis medication was used, displaying similar control patterns. The second clustering method provided similar results. Moreover, patients displayed consistent levels of rhinitis control, reporting several weeks with similar levels of control.CONCLUSIONS:We identified 16 patterns of weekly rhinitis control. Co-medication and medication change schemes were common in uncontrolled weeks, reinforcing the hypothesis that patients treat themselves according to their symptoms.

Different treatments exist for allergic rhinitis (AR), including pharmacotherapy and allergen immunotherapy (AIT), but they have not been compared using direct patient data (i.e., “real‐world data”). We aimed to compare AR pharmacological treatments on (i) daily symptoms, (ii) frequency of use in co‐medication, (iii) visual analogue scales (VASs) on allergy symptom control considering the minimal important difference (MID) and (iv) the effect of AIT.

Abstract Background Evidence regarding the effectiveness of allergen immunotherapy (AIT) on allergic rhinitis has been provided mostly by randomised controlled trials, with little data from real‐life studies. Objective To compare the reported control of allergic rhinitis symptoms in three groups of users of the MASK‐air® app: those receiving sublingual AIT (SLIT), those receiving subcutaneous AIT (SCIT), and those receiving no AIT. Methods We assessed the MASK‐air® data of European users with self‐reported grass pollen allergy, comparing the data reported by patients receiving SLIT, SCIT and no AIT. Outcome variables included the daily impact of allergy symptoms globally and on work (measured by visual analogue scales—VASs), and a combined symptom‐medication score (CSMS). We applied Bayesian mixed‐effects models, with clustering by patient, country and pollen season. Results We analysed a total of 42,756 days from 1,093 grass allergy patients, including 18,479 days of users under AIT. Compared to no AIT, SCIT was associated with similar VAS levels and CSMS. Compared to no AIT, SLIT‐tablet was associated with lower values of VAS global allergy symptoms (average difference = 7.5 units out of 100; 95% credible interval [95%CrI] = −12.1;−2.8), lower VAS Work (average difference = 5.0; 95%CrI = −8.5;−1.5), and a lower CSMS (average difference = 3.7; 95%CrI = −9.3;2.2). When compared to SCIT, SLIT‐tablet was associated with lower VAS global allergy symptoms (average difference = 10.2; 95%CrI = −17.2;−2.8), lower VAS Work (average difference = 7.8; 95%CrI = −15.1;0.2), and a lower CSMS (average difference = 9.3; 95%CrI = −18.5;0.2). Conclusion In patients with grass pollen allergy, SLIT‐tablet, when compared to no AIT and to SCIT, is associated with lower reported symptom severity. Future longitudinal studies following internationally‐harmonised standards for performing and reporting real‐world data in AIT are needed to better understand its ‘real‐world’ effectiveness.

Digital health is an umbrella term which encompasses eHealth and benefits from areas such as advanced computer sciences. eHealth includes mHealth apps, which offer the potential to redesign aspects of healthcare delivery. The capacity of apps to collect large amounts of longitudinal, real-time, real-world data enables the progression of biomedical knowledge. Apps for rhinitis and rhinosinusitis were searched for in the Google Play and Apple App stores, via an automatic market research tool recently developed using JavaScript. Over 1500 apps for allergic rhinitis and rhinosinusitis were identified, some dealing with multimorbidity. However, only six apps for rhinitis (AirRater, AllergyMonitor, AllerSearch, Husteblume, MASK-air and Pollen App) and one for rhinosinusitis (Galenus Health) have so far published results in the scientific literature. These apps were reviewed for their validation, discovery of novel allergy phenotypes, optimisation of identifying the pollen season, novel approaches in diagnosis and management (pharmacotherapy and allergen immunotherapy) as well as adherence to treatment. Published evidence demonstrates the potential of mobile health apps to advance in the characterisation, diagnosis and management of rhinitis and rhinosinusitis patients.

This Pocket Guide was developed by an ARIA and EAACI joint study group from a background paper of the ARIA-MASK study group and from the EAACI guidelines on allergen immunotherapy. Bousquet J, Pfaar O, Togias A, et al. (2019). ARIA Care pathways for allergen immunotherapy. Allergy 2019; 74: 2087–2102. Agache, Lau S, Akdis CA, et al. EAACI guidelines on allergen immunotherapy: house dust mite-driven allergic asthma. Allergy, 2019;74:855-73. AIT is a proven therapeutic option for the treatment of allergic rhinitis, conjunctivitis, and/or asthma using sublingual (SLIT) or subcutaneous (SCIT) routes. However, AIT is more expensive than symptomatic treatments for allergic diseases (excluding biologicals). It is justified (i) in patients with rhinitis otherwise uncontrolled by symptomatic treatment or (ii) as an add-on to regular asthma treatment in controlled or partially-controlled asthmatic patients sensitised to house dust mites aiming to decrease asthma exacerbations, rescue and controller medication, and to improve quality of life. Care pathways are structured multi-disciplinary care plans detailing the key steps of patient care. They promote the translation of guideline recommendations to their application in clinical practice. Although many international and national AIT guidelines have been produced, this is the first care pathway for AIT. This pocket guide applies to sublingual (SLIT) and sub-cutaneous (SCIT) immunotherapy for allergic rhinitis. It has been revised by members from 65 countries (Figure 1). The decision to prescribe AIT should be based on relevant symptoms during allergen exposure, demonstration of sensitisation to the relevant allergens, and availability of good-quality extracts with proven efficacy and safety. Some allergen extracts are approved for marketing in the EU (list in annex) with some others also approved by national health agencies. For certain products, efficacy and safety have been demonstrated in appropriate clinical studies on adults and children. The extrapolation to untested products, allergens or a different population from the one evaluated in the trial is not appropriate and not in line with current guidelines as there is no class-effect in AIT. Both monosensitised and polysensitised patients can be treated. However, in the latter case, the most clinically relevant allergen(s) should be used when symptoms are clearly present with allergen source exposure and when allergy tests confirm clinical findings. Precision medicine aims at the customisation of healthcare, tailored to the characteristics of each individual patient. The stratification of patients into subpopulations is the basis of clinical decision making (Figure 2). In allergic diseases, patient stratification is required to: Propose the appropriate pharmacotherapy. Identify the most suitable candidates for AIT. Reduce the amount of time and resources needed to match the right patient to an optimal care management programme. Optimise costs as expensive therapeutic interventions are not necessary or suitable for all patients. Patient stratification may also help to improve the patient's engagement. Precise diagnosis with history, skin prick tests and/or specific IgE and, if applicable, component-resolved in vitro testing. In some cases, where the above-mentioned diagnostic tools do not allow for precise diagnosis, allergen provocation testing (nasal, ocular and, in some cases, bronchial) may be needed. Proven indications: Allergic rhinitis, conjunctivitis and/or asthma. Symptoms predominantly induced by the relevant allergen exposure. Patient stratification: Poor control of nasal or ocular symptoms despite optimal medications according to guidelines with documented adherence to treatment. Exceptions to requiring optimum symptomatic treatment prior to considering AIT include unacceptable side effects of the medications. Allergic asthma fully controlled under background asthma medication (see EAACI HDM-AIT GL) However, for partially controlled asthma, HDM-AIT may facilitate achieving asthma control (see EAACI HDM-AIT GL) Good clinical documentation of efficacy and safety for the AIT product with relevant trials. The patient's (and caregiver's) views represent an essential component. There are currently no in vivo or in vitro biomarkers validated for monitoring the efficacy of AIT although several potential candidates are currently being investigated. Apps can be used: To acquire real-world evidence to confirm the efficacy of AIT in situations where randomised controlled trials are difficult to perform. To assess air quality index including pollen exposure and air pollution. By physicians and patients for stratification of patients and follow-up. The selection of pharmacotherapy and AIT for patients with AR and/or allergic conjunctivitis may be better supported by evidence algorithms to aid patients and healthcare professionals jointly determine the treatment and its step-up or step-down strategy depending on rhinitis control (shared decision-making). A simple algorithm is proposed as an aid for physicians to determine the treatment of their patients (Figure 3). In the case of remaining ocular symptoms, add intra-ocular treatment. AIT is effective, has long-term beneficial effects after cessation, and may delay or prevent the onset of asthma. AIT can be initiated in children with moderate/severe rhinitis that is not controlled by appropriate medications according to guidelines. An algorithm for HDM-driven allergic asthma diagnosis and management is proposed by the EAACI guidelines. For patients with concomitant allergic rhinitis and sensitised to house dust mite—with persisting asthma symptoms despite low-moderate dose of inhaled corticosteroids—SLIT can be considered, provided FEV1 is >70% predicted. House dust mite SLIT should initially be considered as an add-on therapy to controller treatment, and reduction in asthma controllers should be performed gradually under the supervision of a physician. Immunotherapy is not indicated for the treatment of acute exacerbations, and patients must be informed of the need to seek medical attention immediately if their asthma deteriorates suddenly (Figure 4). One strength of AIT is that it has the potential to control all allergic diseases related to a specific allergen, including rhinitis, conjunctivitis and asthma. Local reactions: A typical reaction is redness and swelling at the injection site immediately or several hours after the injection. Sometimes, sneezing, nasal congestion or hives can occur. Systemic reactions: Serious reactions to injections are very rare and require immediate medical attention. Symptoms of an anaphylactic reaction can include swelling in the throat, wheezing or tightness in the chest, nausea and dizziness. The most serious reactions develop within 30 min after the injection, and patients are advised to wait in their doctor's surgery for at least 30 min after an injection. Severe bronchospasm can also occur, especially in patients where asthma is not controlled. Allergen drops or tablets have a more favourable safety profile than injections. The initial dose should be performed in the doctor's surgery, and patients are advised to remain in the surgery for at least 30 min after administration. Thereafter, SLIT can be administered at home once the first dose has been given under the supervision of a physician. Allergic reactions: The majority of patients will experience mild local reactions of the oropharyngeal passage. This is usually controlled by predosing with an antihistamine 30 min before the administration of SLIT. Sometimes, sneezing, nasal congestion or hives can occur. Anaphylaxis is rarely described. In some countries, SLIT tablets include a warning about possible severe allergic reactions, and adrenaline auto-injectors are routinely recommended. This is not the case in Europe. IAgache is an Associate Editor Allergy and CTA. CA reports grants from Allergopharma, grants from Idorsia, Swiss National Science Foundation, Christine Kühne-Center for Allergy Research and Education, European Commission's Horison's 2020 Framework Programme, Cure, Novartis Research Institutes, Astra Zeneca, scibase, advisory role in Sanofi/Regeneron, grants from Glakso Smith-Kline, advisory role in scibase. IA reports personal fees from Hikma, Roxall, Astra Zeneca, Menarini, UCB, Faes Farma, Sanofi, Mundipharma, Bial, Amgen, Stallergenes. SBA reports grants from TEVA, personal fees from TEVA, AstraZeneca, Boehringer Ingelheim, GSK, Sanofi, Mylan. VC reports personal fees from ALK, Allergy Therapeutics, LETI, Thermofisher, Merck, Astrazeneca, GSK. TC reports grants and personal fees from Stallergenes. PD reports personal fees from ALK-Abello, Stallergenes-Greer, Astra Zeneca, GlaxoSmithKline, Mylan, Sanofi. SD reports personal fees and non-financial support from ALK Abello, personal fees from Adiga, Biomay, Allergopharma, Anergis, Allergy Therapeutics. TH reports personal fees from GSK, Mundipharma, Orion Pharma. SH reports other from ALK-Abelló, other from ALK-Abelló. EH reports personal fees from Sanofi, Novartis, GSK, AstraZeneca, Circassia, Nestlè Purina. JCI reports personal fees from Faes Farma, Laboratorios Casasco Argentina, Abbott de Ecuador, EuroFarma Argentina. MJ reports personal fees from ALK-Abello, Allergopharma, Stallergenes, Anergis, Allergy Therapeutics, Circassia, Leti, Biomay, HAL, during the conduct of the study; personal fees from Astra-Zeneka, GSK, Novartis, Teva, Vectura, UCB, Takeda, Roche, Janssen, Medimmune, Chiesi,. LK reports grants and personal fees from Allergopharma, MEDA/Mylan, LETI Pharma, Sanofi, grants from Stallergenes, Quintiles, ASIT biotech, grants from ALK Abelló, Lofarma, AstraZeneca, GSK, Inmunotk, personal fees from Allergy Therapeut., HAL Allergie, Cassella med; and Membership: AeDA, DGHNO, Deutsche Akademie für Allergologie und klinische Immunologie, HNO-BV, GPA, EAACI. PK reports personal fees from Adamed, Berlin Chemie Menarini, Boehringer Ingelheim, AstraZeneca, Lekam, Novartis, Polpharma, GSK, Polpharma, Sanofi, teva. VK reports other from GSK, non-financial support from Mylan, AstraZeneca, Dimuna, Norameda. SL reports personal fees from DBV, Sanofi Aventis, Allergopharma, ALK, Nutricia, Bencard. EM reports personal fees from Sanofi, Novartis, AstraZeneca and Chiesi. JM reports personal fees and other from SANOFI-GENZYME & REGENERON, NOVARTIS, ALLAKOS, MITSUBISHI-TANABE, MENARINI, UCB, ASTRAZENECA, GSK, MSD, grants and personal fees from MYLAN-MEDA Pharma, URIACH Group. MO reports personal fees from Hycor Diagnostics, Thermo Fisher Phadia. YO reports personal fees from Torii Pharmaceutical Co., Ltd., Shionogi Pharmaceutical Co.,Ltd. OP received research grants from Inmunotek S.L., Novartis and MINECO and has received fees for giving scientific lectures or participation in Advisory Boards from: Allergy Therapeutics, Amgen, AstraZeneca, Diater, GlaxoSmithKline, S.A, Inmunotek S.L, Novartis, Sanofi-Genzyme and Stallergenes. NGP reports personal fees from Novartis, Nutricia, HAL, MENARINI/FAES FARMA, SANOFI, MYLAN/MEDA, BIOMAY, AstraZeneca, GSK, MSD, ASIT BIOTECH, Boehringer Ingelheim, grants from Gerolymatos International SA, Capricare. OP 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., Glaxo Smith Kline, grants from Biomay, Circassia, Pohl-Boskamp, Inmunotek S.L., personal fees from MEDA Pharma/MYLAN, Mobile Chamber Experts (a GA2LEN Partner), Indoor Biotechnologies, Astellas Pharma Global, EUFOREA, ROXALL Medizin, Novartis, Sanofi-Aventis and Sanofi-Genzyme, Med Update Europe GmbH, streamedup! GmbH, John Wiley and Sons, AS. DPreports grants and personal fees from GlaxoSmithKline, personal fees from Menarini, Pliva, Belupo, AbbVie, Novartis, MSD, Chiesi, Revenio, personal fees and non-financial support from Boehringer Ingelheim, non-financial support from Philips. MR is on the Advisory board- A. Menarini - Speaker - Astra Zeneca, Novartis, Sanofi, Mylan. FSRreports speaker and advisory fees from AstraZeneca, Novartis, Sanofi, GSK, Teva and Lusomedicamenta. GR reports payment to his Institution from Allergo Pharma. BSreports personal fees from Allergopharma, during the conduct of the study; grants from National Health Programm, grant, personal fees from Polpharma, ASTRA, personal fees from Mylan, Adamed, patient ombudsman, national Centre for Research and Development, Polish Allergology Society. JS reports grants and personal fees from Sanofi, personal fees from GSK, Novartis, Astra Zeneca, Mundipharma, Faes Farma. GS reports personal fees from ALK, and leds on the BSACI Rhinitis Guidelines and lead for EUFOREA on Allergic Rhinitis. PSG reports personal fees from Allergopharma, ALK, grants from Bencard, grants and personal fees from Stallergenes. JS reports personal fees from Mylan, F2F events. ATB reports grants and personal fees from Teva, AstraZeneca, GSK Sanofi, Mundipharma, personal fees from Bial, Novartis. MJTreports grants from European Commission, SEAIC, ISCIII, personal fees from Diater laboratory, Leti laboratory, Aimmune Therapeutics. MW reports personal fees from ALK-Abello, Allergopharma, AstraZeneca, Bencard, Genzyme, GlaxoSmithKline, HAL Allergy, LETI, Meda Pharma, Novartis, Sanofi, Stallergenes, Teva. DW reports other from Optinose, ALK, Sanofi; past Co-Chair of the Joint Task Force on Practice Parameters of the AAAAI and ACAAI. Second author of a recently published practice parameter on Rhinitis. MW reports other from Aralez (Medexus), Pediapharm, Pfizer, Astra Zeneca, GSK, Alk. MZ reports personal fees from Takeda. TZ reports and Organizational affiliations: Commitee 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). Secretary General: Global Allergy and Asthma European Network (GA2LEN). Member: Committee on Allergy Diagnosis and Molecular Allergology, World Allergy Organization (WAO). Countries with Pocket Guide members Proposed Flow of Precision Medicine approach in allergic diseases. *examples of exceptions: Thunderstorm-induced asthma, patient with moderate rhinitis and severe asthma during pollen season Treatment algorithm using visual analogue scale (VAS) for adolescents and adults AIT, allergen immunotherapy; VAS, visual analogue scale. Algorithm for AIT in asthma

Abstract Background MASK‐air® is an app that supports allergic rhinitis patients in disease control. Users register daily allergy symptoms and their impact on activities using visual analog scales (VASs). We aimed to assess the concurrent validity, reliability, and responsiveness of these daily VASs. Methods Daily monitoring VAS data were assessed in MASK‐air® users with allergic rhinitis. Concurrent validity was assessed by correlating daily VAS values with those of the EuroQol‐5 Dimensions (EQ‐5D) VAS, the Control of Allergic Rhinitis and Asthma Test (CARAT) score, and the Work Productivity and Activity Impairment Allergic Specific (WPAI‐AS) Questionnaire (work and activity impairment scores). Intra‐rater reliability was assessed in users providing multiple daily VASs within the same day. Test–retest reliability was tested in clinically stable users, as defined by the EQ‐5D VAS, CARAT, or “VAS Work” (i.e., VAS assessing the impact of allergy on work). Responsiveness was determined in users with two consecutive measurements of EQ‐5D‐VAS or “VAS Work” indicating clinical change. Results A total of 17,780 MASK‐air® users, with 317,176 VAS days, were assessed. Concurrent validity was moderate–high (Spearman correlation coefficient range: 0.437–0.716). Intra‐rater reliability intraclass correlation coefficients (ICCs) ranged between 0.870 (VAS assessing global allergy symptoms) and 0.937 (VAS assessing allergy symptoms on sleep). Test–retest reliability ICCs ranged between 0.604 and 0.878—“VAS Work” and “VAS asthma” presented the highest ICCs. Moderate/large responsiveness effect sizes were observed—the sleep VAS was associated with lower responsiveness, while the global allergy symptoms VAS demonstrated higher responsiveness. Conclusion In MASK‐air®, daily monitoring VASs have high intra‐rater reliability and moderate–high validity, reliability, and responsiveness, pointing to a reliable measure of symptom loads.

Jean Bousquet, Anna Bedbrook, Wienczyslawa Czarlewski, Giuseppe De Carlo, Joao A. Fonseca, Miguel A. González Ballester, Maddalena Illario, Seppo Koskinen, Tiina Laatikainen, Gabrielle L. Onorato, Susanna Palkonen, Vincenzo Patella, Nhân Pham-Thi, Francesca Puggioni, Maria Teresa Ventura, Guy Joos, Piotr Kuna, Renaud Louis, Michael Makris, Petra Zalud, Torsten Zuberbier, Claus Bachert, Luisa Brussino, Pedro Carreiro-Martins, Carme Carrion y Ribas, Maciej Chalubinski, Elisio M. Costa, Govert de Vries, Bilun Gemicioglu, Dimitra Gennimata, Yann Micheli, Marek Niedoszytko, Frederico S. Regateiro, Jan Romantowski, Luis Taborda-Barata, Sanna Toppila-Salmi, Ioanna Tsiligianni, Frederic Viart, Daniel Laune

INTRODUCTION:MASK-air® is an app whose aim is to reduce the global burden of allergic rhinitis and asthma. A transfer of innovative practices was performed to disseminate and implement MASK-air® in European regions. The aim of the study was to examine the implementation of the MASK-air® app in older adults of the Puglia TWINNING in order to investigate (i) the rate of acceptance in this population, (ii) the reasons for refusal and (iii) the evaluation of the app after its use.METHODS:All consecutive geriatric patients aged between 65 and 90 years were included by the outpatient clinic of the Bari Geriatric Immunoallergology Unit. After a 1-h training session, older adults used the app for 6 months. A 6-item questionnaire was developed by our unit to evaluate the impact of the app on the management of the disease and its treatment.RESULTS:Among the 174 recruited patients, 102 accepted to use the app (mean age, SD: 72.4 ± 4.6 years), 6 were lost to follow-up, and 63 had a low education level. The reasons given not to use the app included lack of interest (11%), lack of access to a smartphone or tablet (53%), low computer literacy (28%), and distrust (8%). At follow-up, the overall satisfaction was high (89%), the patient considered MASK-air® "advantageous" (95%), compliance to treatment was improved (81%), and the rate of loss to follow-up had decreased to 6%.CONCLUSION:Older adults with a low level of education can use the MASK-air® app after a short training session.

This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers. For decades, information available for the patients and allergologists consisted of pollen counts, which are vital but insufficient. New technology paves the way to substantial increase in amount and diversity of the data. This paper reviews old and newly suggested methods to predict pollen and air pollutant concentrations in the air and proposes an allergy risk concept, which combines the pollen and pollution information and transforms it into a qualitative risk index. This new index is available in an app (Mobile Airways Sentinel NetworK-air) that was developed in the frame of the European Union grant Impact of Air POLLution on sleep, Asthma and Rhinitis (a project of European Institute of Innovation and Technology-Health). On-going transformation of the pollen allergy information support is based on new technological solutions for pollen and air quality monitoring and predictions. The new information-technology and artificial-intelligence-based solutions help to convert this information into easy-to-use services for both medical practitioners and allergy sufferers.