Evaluating the concordance of pollen forecasting apps against automated pollen monitoring: A single-site experience.

Background: Individuals with allergic rhinitis and asthma rely on accurate pollen forecasts to avoid allergen exposure and manage symptoms. However, many widely used weather and health applications (apps) use manual pollen counting methods, which may vary in accuracy. Objective: This study aimed to evaluate the concordance between popular pollen forecasting apps and real-time data collected from an automated pollen monitoring device at a single site in the Chicago area. Methods: We compared daily pollen forecasts from 2 commonly used consumer apps (The Weather Channel app and the AccuWeather app) with pollen data recorded by the PollenSense automated monitoring device over 2 months. To assess daily concordance, forecasted pollen levels and automated counts were categorized as being in the low, moderate, or high ranges. Descriptive and inferential assessment of accuracy and reliability of consumer-facing pollen forecasts were performed. Results: Across the study period, concordance between the consumer apps and the PollenSense counts was low (the forecast levels for the AccuWeather app were 7% for grass, 33% for ragweed, and 56% for mold, whereas those for The Weather Channel app were 29% for grass and 34.% for ragweed). No statistically significant association was found between the pollen forecasts and measured pollen levels. Conclusion: The popular pollen forecasting apps demonstrated poor concordance with real-time automated pollen data. These findings highlight the limitations of current forecasting tools and underscore the need for improved, validated technologies to support clinical decision making and public health recommendations for individuals affected by pollen allergies.