Digitally-enabled, patient-centred care in rhinitis and asthma multimorbidity: The ARIA-MASK-air® approach.

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.

Correlation between work impairment, scores of rhinitis severity and asthma using the MASK-air® App.

BACKGROUND: In allergic rhinitis, a relevant outcome providing information on the effectiveness of interventions is needed. In MASK-air (Mobile Airways Sentinel Network), a visual analogue scale (VAS) for work is used as a relevant outcome. This study aimed to assess the performance of the work VAS work by comparing VAS work with other VAS measurements and symptom-medication scores obtained concurrently. METHODS: All consecutive MASK-air users in 23 countries from 1 June 2016 to 31 October 2018 were included (14 189 users; 205 904 days). Geolocalized users self-assessed daily symptom control using the touchscreen functionality on their smart phone to click on VAS scores (ranging from 0 to 100) for overall symptoms (global), nose, eyes, asthma and work. Two symptom-medication scores were used: the modified EAACI CSMS score and the MASK control score for rhinitis. To assess data quality, the intra-individual response variability (IRV) index was calculated. RESULTS: A strong correlation was observed between VAS work and other VAS. The highest levels for correlation with VAS work and variance explained in VAS work were found with VAS global, followed by VAS nose, eye and asthma. In comparison with VAS global, the mCSMS and MASK control score showed a lower correlation with VAS work. Results are unlikely to be explained by a low quality of data arising from repeated VAS measures. CONCLUSIONS: VAS work correlates with other outcomes (VAS global, nose, eye and asthma) but less well with a symptom-medication score. VAS work should be considered as a potentially useful AR outcome in intervention studies.

Interactions Between Air Pollution and Pollen Season for Rhinitis Using Mobile Technology: A MASK-POLLAR Study.

BACKGROUND: Several studies have suggested an interaction between air pollution and pollen exposure with an impact on allergy symptoms. However, large studies with real-life data are not available. OBJECTIVE: To investigate associations between major air pollutants (ozone and particulate matter with a diameter of <2.5 µm) and allergic rhinitis (AR) control during grass and birch pollen seasons as well as outside the pollen season. METHODS: The daily impact of allergic symptoms was recorded by the Allergy Diary (Mobile Airways Sentinel NetworK [MASK-air]) app (a validated mHealth tool for rhinitis management) using visual analog scales (VASs) in Northern and Central Europe users in 2017 and 2018. Uncontrolled AR was defined using symptoms and medications. Pollutant levels were assessed using the System for Integrated modeLing of Atmospheric coMposition database. Pollen seasons were assessed by regions using Google Trends. Generalized estimating equation models were used to account for repeated measures per user, adjusting for sex, age, treatment, and country. Analyses were stratified by pollen seasons to investigate interactions between air pollutants and pollen exposure. RESULTS: A total of 3323 geolocated individuals (36,440 VAS-days) were studied. Associations between uncontrolled rhinitis and pollutants were stronger during the grass pollen season. Days with uncontrolled AR increased by 25% for an interquartile range increase in ozone levels during the grass pollen season (odds ratio of 1.25 [95% CI, 1.11-1.41] in 2017 and of 1.14 [95% CI, 1.04-1.25] in 2018). A similar trend was found for particulate matter with a diameter of less than 2.5 µm, especially in 2017. CONCLUSIONS: These results suggest that the relationship between uncontrolled AR and air pollution is modified by the presence of grass pollens. This study confirms the impact of pollutants in the grass pollen season but not in the birch pollen season.

Mobile Technology in Allergic Rhinitis: Evolution in Management or Revolution in Health and Care?

Smart devices and Internet-based applications (apps) are largely used in allergic rhinitis and may help to address some unmet needs. However, these new tools need to first of all be tested for privacy rules, acceptability, usability, and cost-effectiveness. Second, they should be evaluated in the frame of the digital transformation of health, their impact on health care delivery, and health outcomes. This review (1) summarizes some existing mobile health apps for allergic rhinitis and reviews those in which testing has been published, (2) discusses apps that include risk factors of allergic rhinitis, (3) examines the impact of mobile health apps in phenotype discovery, (4) provides real-world evidence for care pathways, and finally (5) discusses mobile health tools enabling the digital transformation of health and care, empowering citizens, and building a healthier society.

How Knowledge Emerges From Artificial Intelligence Algorithm and Data Visualization for Diabetes Management.

BACKGROUND: Self-monitoring blood glucose (SMBG) is facilitated by application available to analyze these data. They are mainly based on descriptive statistical analyses. In this study, we are proposing a method inspired by artificial intelligence algorithm for displaying glycemic data in an intelligible way with high-level information that is compatible with the short duration allocated to medical visits. METHOD: We propose a display method based on a numerical glycemic data conversion using a qualitative color scale that exhibits the patient's overall glycemic state. Moreover, a machine learning algorithm inputs these displays to exhibit recurrent glycemic pattern over configurable extended time period. RESULTS: A demonstrator of our method, output as a glycemic map, could be used by the physician during quarterly patient consultations. We have tested this methodology retrospectively on a database in order to observe the behavior of our algorithm. In some data files we were able to highlight some of the glycemic patterns characteristics that remain invisible on the tabular representations or through the use of descriptive statistic. In a next step the interpretation will have to be done by physicians to confirm they underlie knowledge. CONCLUSIONS: Our approach with artificial intelligence algorithm paired up with graphical color display allow a large database fast analysis to provide insights on diabetes knowledge. The next steps are first to set up a clinical trial to validate this methodology with dedicated patients and physicians then we will adapt our methodology for the huge data sets generated by continuous glycemic measurement (CGM) devices.

Mobile technology offers novel insights into the control and treatment of allergic rhinitis: The MASK study.

BACKGROUND: Mobile health can be used to generate innovative insights into optimizing treatment to improve allergic rhinitis (AR) control. OBJECTIVES: A cross-sectional real-world observational study was undertaken in 22 countries to complement a pilot study and provide novel information on medication use, disease control, and work productivity in the everyday life of patients with AR. METHODS: A mobile phone app (Allergy Diary, which is freely available on Google Play and Apple stores) was used to collect the data of daily visual analogue scale (VAS) scores for (1) overall allergic symptoms; (2) nasal, ocular, and asthma symptoms; (3) work; and (4) medication use by using a treatment scroll list including all allergy medications (prescribed and over-the-counter) customized for 22 countries. The 4 most common intranasal medications containing intranasal corticosteroids and 8 oral H1-antihistamines were studied. RESULTS: Nine thousand one hundred twenty-two users filled in 112,054 days of VASs in 2016 and 2017. Assessment of days was informative. Control of days with rhinitis differed between no (best control), single (good control for intranasal corticosteroid-treated days), or multiple (worst control) treatments. Users with the worst control increased the range of treatments being used. The same trend was found for asthma, eye symptoms, and work productivity. Differences between oral H1-antihistamines were found. CONCLUSIONS: This study confirms the usefulness of the Allergy Diary in accessing and assessing behavior in patients with AR. This observational study using a very simple assessment tool (VAS) on a mobile phone had the potential to answer questions previously thought infeasible.

Google Trends and pollen concentrations in allergy and airway diseases in France.

BACKGROUND: Google Trends (GTs) is a web-based surveillance tool that explores the searching trends of specific queries via Google. This tool proposes to reflect the real-life epidemiology of allergic rhinitis and asthma. However, the validation of GTs against pollen concentrations is missing at the country level. OBJECTIVES: In the present study, we used GTs (a) to compare the terms related to allergy in France, (b) to assess seasonal variations across the country for 5 years and (c) to compare GTs and pollen concentrations for 2016. METHODS: Google Trends queries were initially searched to investigate the terms reflecting pollen and allergic diseases. 13- and 5-year GTs were used in France. Then, 5-year GTs were assessed in all metropolitan French regions to assess the seasonality of GTs. Finally, GTs were compared with pollen concentrations (Réseau National de Surveillance en Aerobiology) for 2016 in seven regions (GTs) and corresponding cities (pollen concentrations). RESULTS: The combination of searches for "allergy" as a disease, "pollen" as a disease cause and "ragweed" as a plant was needed to fully assess the pollen season in France. "Asthma" did not show any seasonality. Using the 5-year GTs, an annual and clear seasonality of queries was found in all regions depending on the predicted pollen exposure for spring and a summer peak but not for winter peaks. The agreement between GT queries and pollen concentrations is usually poor except for spring trees and grasses. Moreover, cypress pollens are insufficiently reported by GTs. CONCLUSIONS: Google Trends cannot predict the pollen season in France.

Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study.

BACKGROUND: Mobile technology may help to better understand the adherence to treatment. MASK-rhinitis (Mobile Airways Sentinel NetworK for allergic rhinitis) is a patient-centred ICT system. A mobile phone app (the Allergy Diary) central to MASK is available in 22 countries. OBJECTIVES: To assess the adherence to treatment in allergic rhinitis patients using the Allergy Diary App. METHODS: An observational cross-sectional study was carried out on all users who filled in the Allergy Diary from 1 January 2016 to 1 August 2017. Secondary adherence was assessed by using the modified Medication Possession Ratio (MPR) and the Proportion of days covered (PDC) approach. RESULTS: A total of 12 143 users were registered. A total of 6 949 users reported at least one VAS data recording. Among them, 1 887 users reported ≥7 VAS data. About 1 195 subjects were included in the analysis of adherence. One hundred and thirty-six (11.28%) users were adherent (MPR ≥70% and PDC ≤1.25), 51 (4.23%) were partly adherent (MPR ≥70% and PDC = 1.50) and 176 (14.60%) were switchers. On the other hand, 832 (69.05%) users were non-adherent to medications (MPR <70%). Of those, the largest group was non-adherent to medications and the time interval was increased in 442 (36.68%) users. CONCLUSION AND CLINICAL RELEVANCE: Adherence to treatment is low. The relative efficacy of continuous vs on-demand treatment for allergic rhinitis symptoms is still a matter of debate. This study shows an approach for measuring retrospective adherence based on a mobile app. This also represents a novel approach for analysing medication-taking behaviour in a real-world setting.

POLLAR: Impact of air POLLution on Asthma and Rhinitis; a European Institute of Innovation and Technology Health (EIT Health) project.

Allergic rhinitis (AR) is impacted by allergens and air pollution but interactions between air pollution, sleep and allergic diseases are insufficiently understood. POLLAR (Impact of air POLLution on sleep, Asthma and Rhinitis) is a project of the European Institute of Innovation and Technology (EIT Health). It will use a freely-existing application for AR monitoring that has been tested in 23 countries (the Allergy Diary, iOS and Android, 17,000 users, TLR8). The Allergy Diary will be combined with a new tool allowing queries on allergen, pollen (TLR2), sleep quality and disorders (TRL2) as well as existing longitudinal and geolocalized pollution data. Machine learning will be used to assess the relationship between air pollution, sleep and AR comparing polluted and non-polluted areas in 6 EU countries. Data generated in 2018 will be confirmed in 2019 and extended by the individual prospective assessment of pollution (portable sensor, TLR7) in AR. Sleep apnea patients will be used as a demonstrator of sleep disorder that can be modulated in terms of symptoms and severity by air pollution and AR. The geographic information system GIS will map the results. Consequences on quality of life (EQ-5D), asthma, school, work and sleep will be monitored and disseminated towards the population. The impacts of POLLAR will be (1) to propose novel care pathways integrating pollution, sleep and patients' literacy, (2) to study sleep consequences of pollution and its impact on frequent chronic diseases, (3) to improve work productivity, (4) to propose the basis for a sentinel network at the EU level for pollution and allergy, (5) to assess the societal implications of the interaction. MASK paper N°32.

Electronic Clinical Decision Support System for allergic rhinitis management: MASK e-CDSS.

BACKGROUND: Allergic rhinitis (AR) management has changed in recent years following the switch from the concept of disease severity to the concept of disease control, publication of the AR clinical decision support system (CDSS) and development of mobile health (m-health) tools for patients (eg Allergy Diary). The Allergy Diary Companion app for healthcare providers is currently being developed and will be launched in 2018. It incorporates the AR CDSS to provide evidence-based treatment recommendations, linking all key stakeholders in AR management. OBJECTIVE: To produce an electronic version of the AR CDSS (e-CDSS) for incorporation into the Allergy Diary Companion, to describe the app interfaces used to collect information necessary to inform the e-CDSS and to summarize some key features of the Allergy Diary Companion. METHODS: The steps involved in producing the e-CDSS and incorporating it into the Allergy Diary Companion were (a) generation of treatment management scenarios; (b) expert consensus on treatment recommendations; (c) generation of electronic decisional algorithms to describe all AR CDSS scenarios; (d) digitization of these algorithms to form the e-CDSS; and (e) embedding the e-CDSS into the app to permit easy user e-CDSS interfacing. RESULTS: Key experts in the AR field agreed on the AR CDSS approach to AR management and on specific treatment recommendations provided by Allergy Diary Companion. Based on this consensus, decision processes were developed and programmed into the Allergy Diary Companion using Titanium Appcelerator (JavaScript) for IOS tablets. To our knowledge, this is the first time the development of any m-health tool has been described in this transparent and detailed way, providing confidence, not only in the app, but also in the provided management recommendations. CONCLUSION: The Allergy Diary Companion for providers provides guideline and expert-endorsed AR management recommendations. [MASK paper No 32].

MACVIA clinical decision algorithm in adolescents and adults with allergic rhinitis.

The selection of pharmacotherapy for patients with allergic rhinitis (AR) depends on several factors, including age, prominent symptoms, symptom severity, control of AR, patient preferences, and cost. Allergen exposure and the resulting symptoms vary, and treatment adjustment is required. Clinical decision support systems (CDSSs) might be beneficial for the assessment of disease control. CDSSs should be based on the best evidence and algorithms to aid patients and health care professionals to jointly determine treatment and its step-up or step-down strategy depending on AR control. Contre les MAladies Chroniques pour un VIeillissement Actif en Languedoc-Roussillon (MACVIA-LR [fighting chronic diseases for active and healthy ageing]), one of the reference sites of the European Innovation Partnership on Active and Healthy Ageing, has initiated an allergy sentinel network (the MACVIA-ARIA Sentinel Network). A CDSS is currently being developed to optimize AR control. An algorithm developed by consensus is presented in this article. This algorithm should be confirmed by appropriate trials.

MASK-rhinitis, a single tool for integrated care pathways in allergic rhinitis.

Allergic rhinitis (AR) is among the most common diseases globally. MASK-rhinitis is a simple ICT tool to implement care pathways for allergic rhinitis from patients to health care providers using a common language and a clinical decision support system. This is based on the assessment of the control of allergic rhinitis by a visual analogue scale on and App and a tablet. MASK-rhinitis will allow (i) the patients to screen for allergic disease, (ii) the pharmacists, to guide them in the prescription of OTC medications and direct the uncontrolled patients to physicians, (iii) the primary care physician, to prescribe appropriate treatment and to follow-up with the patient according to the physician's instructions (CDSS) and assessment of control and (vi) the specialist and outpatient clinics in allergology, if there is failure to gain control by the primary physician. MASK-rhinitis will be important for establishing care pathways across the life cycle, stratify patients with severe uncontrolled rhinitis and to perform clinical trials.