Open Access

Johannes Karg

• Pollen allergies represent one of the most common non-communicable diseases in Germany and Europe. Due to an interplay of various factors such as environmental changes and rapid urbanization, the prevalence globally appears to be still on the rise. Affected patients show a substantial disease-related burden and experience a significant reduction in Quality of Life. Yet patients appear to often self-manage and trivialize their condition and often do not seek medical supervision. Due to the rapid growth of digital health interventions, the development of mHealth apps targeting pollen allergies is also widespread. These typically aim to improve self-management and consist of different functionalities, such as knowledge sections, symptom diaries and pollen forecasts. However, current research on the impact of the app usage and the various functionalities on patients is very limited. The aim of the thesis was therefore to investigate the clinical efficacy and the impact on patientPollen allergies represent one of the most common non-communicable diseases in Germany and Europe. Due to an interplay of various factors such as environmental changes and rapid urbanization, the prevalence globally appears to be still on the rise. Affected patients show a substantial disease-related burden and experience a significant reduction in Quality of Life. Yet patients appear to often self-manage and trivialize their condition and often do not seek medical supervision. Due to the rapid growth of digital health interventions, the development of mHealth apps targeting pollen allergies is also widespread. These typically aim to improve self-management and consist of different functionalities, such as knowledge sections, symptom diaries and pollen forecasts. However, current research on the impact of the app usage and the various functionalities on patients is very limited. The aim of the thesis was therefore to investigate the clinical efficacy and the impact on patient behavior of an app for patients suffering from grass pollen allergy. Also, the clinical efficacy of allergen avoidance strategies was explored. Finally, this thesis evaluated an exploratory symptom forecasting model which was developed based on the obtained clinical data and publicly available environmental data. The results of this thesis were based on a randomized, controlled clinical trial evaluating the mHealth app “PollDi” which was designed for patients suffering from grass pollen allergy. The study included 167 participants in the greater area of Augsburg, that were allocated into three groups. Each group received a different version of the app with different available functions. Group A had access to all functionalities including the pollen forecast, group B to the diary and the basic information and group C only to the basic information. In summary, the main results showed a positive correlation between increasing frequency of protective behavior and reduction of symptom severity across the cohort. No difference between the groups was observed regarding the frequency of the discussed behavioral strategies. Group A showed the highest medication intake throughout the study period. Generally, the app usage was perceived as positive by the majority of participants, independent of patient clusters, and negative effects were infrequently reported. In terms of Quality of Life, asthmatic patients, and among these especially female patients, appeared to benefit most of the app usage. The evaluation of the symptom forecasting model showed yet insufficient but promising results for the future, provided having high-quality data available. It can be concluded that mHealth apps for pollen allergies may present a promising tool to improve self-management and could serve as a valuable addition to traditional treatments. Further, the generation of real-world-data offers the possibility to develop and integrate personalized machine-learning models such as symptom forecasts or individual treatment response prediction. As pollen allergies represent a complex disease entity and the interplay of a variety of factors, such as meteorological factors, pollen concentration and personal factors influence symptom severity, personalized approaches seem promising for alleviating symptom burden in the future.…