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Observational Study
. 2023 May;63(5):585-600.
doi: 10.1111/head.14482. Epub 2023 Feb 28.

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

Masahito Katsuki  1 , Muneto Tatsumoto  2 , Kazuhito Kimoto  3 , Takashige Iiyama  4 , Masato Tajima  4 , Tsuyoshi Munakata  4 , Taihei Miyamoto  4 , Tomokazu Shimazu  5
Affiliations
Observational Study

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

Masahito Katsuki et al. Headache. 2023 May.

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: Of the 40,617 respondents, 15,127/40,617 (37.2%) users experienced physician-diagnosed migraine, and 2458/40,617 (6.1%) users had physician-diagnosed non-migraine headaches. The mean (standard deviation) age of the 4375 filtered users was 34 (11.2) years, and 89.2% were female (3902/4375). Lower barometric pressure (p < 0.001, gain = 3.9), higher humidity (p < 0.001, gain = 7.1), more rainfall (p < 0.001, gain = 3.1), a significant decrease in barometric pressure 6 h before (p < 0.001, gain = 11.7), higher barometric pressure at 6:00 a.m. on the day (p < 0.001, gain = 4.6), lower barometric pressure on the next day (p < 0.001, gain = 6.7), and raw time-series barometric type I (remaining low around headache attack, p < 0.001, gain = 10.1) and type II (decreasing around headache attack, p < 0.001, gain = 10.1) changes over 6 days, were significantly associated with headache occurrences in both the statistical and DL-based models. For temporal validation, the root mean squared error (RMSE) was 13.4, and the determination coefficient (R2 ) was 52.9% for the statistical model. The RMSE was 10.2, and the R2 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.

Keywords: artificial intelligence; episodic headache; episodic migraine; episodic tension-type headache; real-world data; smartphone application.

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References

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