Investigating the effects of weather on headache occurrence using a smartphone application and artificial intelligence: A retrospective observational cross‐sectional study

Abstract

OBJECTIVE: To investigate the relationship between weather and headache occurrence using big data from an electronic headache diary smartphone application with recent statistical and deep learning (DL)-based methods. BACKGROUND: The relationship between weather and headache occurrence remains unknown. METHODS: From a database of 1 million users, data from 4375 users with 336,951 hourly headache events and weather data from December 2020 to November 2021 were analyzed. We developed statistical and DL-based models to predict the number of hourly headache occurrences mainly from weather factors. Temporal validation was performed using data from December 2019 to November 2020. Apart from the user dataset used in this model development, the physician-diagnosed headache prevalence was gathered. RESULTS: was 53.7% for the DL-based model. CONCLUSIONS: Using big data, we found that low barometric pressure, barometric pressure changes, higher humidity, and rainfall were associated with an increased number of headache occurrences.

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