The “allergic nose as a pollen detector” concept: e-Diaries to predict pollen trends

Paolo Maria Matricardi, Tara Hoffmann, Stephanie Dramburg 

Volume34, Issue 6 June 2023 e13966 Open Access

Abstract

Hirst pollen traps and operator pollen recognition are worldwide used by aerobiologists, providing essential services for the diagnosis and monitoring of allergic patients. More recently, semiautomated or fully automated detector systems have been developed, which facilitate prediction of pollen exposure and risk for the individual patient. In parallel, smartphone apps consisting of short questionnaires filled in daily by the patient/user provide daily scores, time trajectories, and descriptive reports of the severity of respiratory allergies in patients with pollen allergy.

The usual scientific and clinical approach to this matter is to monitor the environment (pollen concentration) in order to predict the risk of symptoms (allergic rhinitis) in a population. We discuss here the opposite, contraintuitive possibility, that is, the use of e-diaries to collect daily information of mono-sensitized pollen-allergic patients in order to predict the clinically efficient airborne exposure to a given pollen, area, and time period. In line with the “Patient as Sensor” concept, proposed in 2013 by Bernd Resch, the “allergic nose” may be used as a pollen detector in addition to existing calibrated hardware sensors, namely the pollen stations, thus contributing with individual measurements, sensations, and symptoms' perception. The target of this review is to present a novel concept of pollen monitoring based on “pollen-detector” patients to inspire future cooperative studies aimed at investigating and hopefully validating our hypothesis.

Key Message

We propose here the “allergic nose as a pollen detector” concept. The “allergic nose” of patients can be used as a “pollen detector” which, through e-Diaries on geo-located smartphone apps, is continuously acquired within a given geographic area. Real-time geolocated individual symptoms data may help generate dynamic GeoAI maps of clinically relevant pollen exposure and “navigation systems” driving pollen-allergic patients away from pollen exposure.

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