Department of Neurosurgery, Itoigawa General Hospital, Itoigawa, Japan.
Headache Center and Medical Safety Management Center, Dokkyo Medical University, Mibu, Japan.
Headache. 2023 May;63(5):585-600. doi: 10.1111/head.14482. Epub 2023 Feb 28.
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.
The relationship between weather and headache occurrence remains unknown.
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.
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 (R ) was 52.9% for the statistical model. The RMSE was 10.2, and the R was 53.7% for the DL-based model.
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.
利用电子头痛日记智能手机应用程序中的大数据，结合最新的统计和深度学习（DL）方法，研究天气与头痛发作之间的关系。
天气与头痛发作之间的关系尚不清楚。
从 100 万用户的数据库中，分析了 4375 名用户的 336951 小时头痛发作数据和 2020 年 12 月至 2021 年 11 月的天气数据。我们开发了统计和基于深度学习的模型，主要从天气因素预测每小时头痛发作次数。使用 2019 年 12 月至 2020 年 11 月的数据进行了时间验证。除了用于模型开发的用户数据集外，还收集了医生诊断的头痛患病率。
在 40617 名应答者中，15127/40617（37.2%）名用户患有经医生诊断的偏头痛，2458/40617（6.1%）名用户患有经医生诊断的非偏头痛性头痛。4375 名筛选出的用户的平均（标准差）年龄为 34（11.2）岁，89.2%为女性（3902/4375）。气压较低（p<0.001，增益=3.9）、湿度较高（p<0.001，增益=7.1）、降雨量较多（p<0.001，增益=3.1）、气压在头痛发作前 6 小时显著下降（p<0.001，增益=11.7）、上午 6 点气压较高（p<0.001，增益=4.6）、次日气压较低（p<0.001，增益=6.7）以及气压原始时间序列类型 I（在头痛发作周围保持较低水平，p<0.001，增益=10.1）和类型 II（在头痛发作周围下降，p<0.001，增益=10.1）在 6 天内的变化，在统计和基于深度学习的模型中均与头痛发作显著相关。对于时间验证，统计模型的均方根误差（RMSE）为 13.4，决定系数（R）为 52.9%。基于深度学习的模型的 RMSE 为 10.2，R 为 53.7%。
使用大数据，我们发现气压较低、气压变化、湿度较高和降雨与头痛发作次数增加有关。
