Aligning the Good Practice MASK With the Objectives of the European Innovation Partnership on Active and Healthy Ageing

Self-Management in Allergic Rhinitis: Strategies, Outcomes and Integration into Clinical Care

Allergic rhinitis (AR) is a common disease, characterized by typical symptoms and type 2 inflammation. Standard treatment is usually effective and safe, and most medications are available as over-the-counter (OTC). Therefore, AR patients, mostly at the AR onset or if symptoms are mild, prefer to manage symptoms themselves. However, self-management could be associated with problems, including inadequate control or medication abuse. Therefore, this paper aimed at presenting and discussing this issue. Ideally, self-management should be conducted under medical supervision. In this regard, telemedicine could represent a valuable tool for implementing self-management. Visual analog scale (VAS) is a perfect parameter to measure symptom severity, medications use, and AR control. In addition, VAS is easily monitored over time. Presently, a mobile application allows to AR patients of self-managing themselves. Therefore, mobile healthcare may supply fundamental support in communication and decision-making. In this regard, VAS represents the best tool to monitor symptoms' severity and control over time. Finally, pharmacological and non-pharmacological remedies are numerous and effective but should be used wisely.

Real-world data using mHealth apps in rhinitis, rhinosinusitis and their multimorbidities

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.

Automatic market research of mobile health apps for the self-management of allergic rhinitis

BACKGROUND

Only a small number of apps addressing allergic rhinitis (AR) patients have been evaluated. This makes their selection difficult. We aimed to introduce a new approach to market research for AR apps, based on the automatic screening of Apple App and Google Play stores.

METHODS

A JavaScript programme was devised for automatic app screening, and applied in a market assessment of AR self-management apps. We searched the Google Play and Apple App stores of three countries (USA, UK and Australia) with the following search terms: "hay fever", "hayfever", "asthma", "rhinitis", "allergic rhinitis". Apps were eligible if symptoms were evaluated. Results obtained with the automatic programme were compared to those of a blinded manual search. As an example, we used the search to assess apps that can be used to design a combined medication score for AR.

RESULTS

The automatic search programme identified 39 potentially eligible apps out of a total of 1593 retrieved apps. Each of the 39 apps was individually checked, with 20 being classified as relevant. The manual search identified 19 relevant apps (out of 6750 screened apps). Combining both methods, a total of 21 relevant apps were identified, pointing to a sensitivity of 95% and a specificity of 99% for the automatic method. Among these 21 apps, only two could be used for the combined symptom-medication score for AR.

CONCLUSIONS

The programmed algorithm presented herein is able to continuously retrieve all relevant AR apps in the Apple App and Google Play stores, with high sensitivity and specificity. This approach has the potential to unveil the gaps and unmet needs of the apps developed so far.