天气相关性疼痛(TENKITSU)的流行病学和临床特征及发病预测信息服务的发展

Pain Research Pub Date : 2021-07-30 DOI:10.11154/pain.36.75
Jun Sato, Ryosuke Ueyama, Kiyoteru Morita, T. Furuya, Yasuko Otsuka, Sayaka Hatakeyama, Mayumi Toda, Naho Toda
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引用次数: 3

摘要

天气变化伴随着大气压的下降被认为会引发流星病,即与天气相关的疼痛。本文介绍了天气相关疼痛(TENKITSU)的流行病学和临床特征,并简要描述了其发病机制。根据2020年天气疼痛调查,据估计,日本至少有1000万人患有天气疼痛,但在一般临床实践中似乎很难应对。为了制定有效的预防措施,有必要建立一个高度准确的气象预报。我们进行了一项大规模的互联网调查,并建立了一个预测模型。调查期约为一年,共进行了35次调查。我们分析了从天气新闻(WN)用户(共157698)获得的症状报告与气压数据之间的相关性。对导致天气相关疼痛发作的大气压变化模式进行了索引。我们发现,大气压力的明显变化、作为天气崩溃前兆的大气压力的微小变化(微压力波动)以及大气压力的昼夜波动(大气潮汐)与症状恶化相关。因此,我们对这三个因素的贡献进行了加权,建立了一个模型,预测每3小时提前6天出现天气疼痛的风险,并开始在WN使用它。
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The epidemiological and clinical features of weather–related pain (TENKITSU) and development of prediction information service for the onset of pain
Weather changes accompanied by decreases in barometric pressure are suggested to trigger meteoropathy, i.e., weather–related pain. In this paper, the epidemiological and clinical features of weather–related pain ( TENKITSU ) are shown and the mechanism is briefly described. From the weather pain survey 2020, it is estimated that there are at least 10 million people who have weather pain in Japan, but it seems that it is difficult to deal with it in general clinical practice. It is necessary to establish a highly accurate meteoro pathy forecast in order to establish effective preventive treatments. We conducted a large–scale Internet survey and built a predictive model. The survey period was about one year, and 35 surveys were done. We analyzed the correlation between the symptom reports obtained from weather news ( WN ) users ( 157,698 in total ) and the barometric pressure data. The barometric pressure change pattern that contributes to the onset of weather–related pain was indexed. We found that obvious changes in atmospheric pressure, minute changes in atmospheric pressure ( micro– pressure fluctuations ) which occur as a precursor to the collapse of the weather, and diurnal fluctuations in atmospheric pressure ( atmospheric tide ) correlate with worsening of symptoms. Therefore, we weighted the contributions of these three factors, built a model that predicts the risk of developing weather pain up to 6 days ahead every 3 hours, and started using it at WN.
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Pain Research
Pain Research CLINICAL NEUROLOGY-
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