The association between symptoms, activity tracking and weather conditions in patients with arthritis and other chronic pain

几个世纪以来，人们一直猜测天气和关节疼痛之间存在联系。然而，数据质量问题让研究人员还无法得出任何明确的结论。多云有疼痛的机会是由英国关节炎研究中心资助的一个重要的新项目，该项目将利用智能手机的广泛使用来收集关于患者症状、体力活动和天气状况的定期、高质量数据，并调查关节炎和其他慢性疼痛患者之间的关系。这个博士项目将使用来自云与疼痛机会研究的数据来：1.开发和测试基于天气条件预测关节炎患者疼痛的统计模型2。调查被动监测的体力活动水平与患者报告的症状之间的关系第一年和第二年将包括流行病学、生物统计学、统计计算和预测建模方面的培训。关于关节炎患者的天气和症状的文献综述，重点是分析方法，以及环境暴露对健康的短期影响的流行病学模型的回顾。对通过智能手机GPS定位获得的当地天气状况与通过智能手机应用程序输入的患者症状数据之间的关联进行纵向分析。开发基于天气的疼痛预测模型(“疼痛预测”)。第二年和第三年将包括从相关活动监测仪获取、清理和准备体力活动数据，并确定最适合分析的措施。活动措施和症状数据的纵向分析。基于天气预报的未来关节疼痛预测模型将允许患者计划他们的活动。了解被动监测的体力活动和疾病严重程度之间的联系将有助于开发一种用于临床护理和研究的新的结果衡量标准。

For centuries, it has been speculated that there is an association between the weather and joint pain. However, data quality issues have left researchers as yet unable to reach any definite conclusions. Cloudy with a Chance of Pain is a major new project funded by Arthritis Research UK that will exploit the widespread uptake of smartphones to gather regular, high quality data on patient symptoms, physical activity and weather conditions and investigate the relationships between them in patients with arthritis and other chronic pain.This PhD project will use data from the Cloudy with a Chance of Pain study to:1. Develop and test a statistical model for forecasting pain in patients with arthritis based on weather conditions2. Investigate the association between passively-monitored physical activity levels and patient-reported symptomsIn years 1 and 2 will include training in epidemiology, biostatistics, statistical computing and prediction modelling. Literature review on weather and symptoms in patients with arthritis, with a focus on analytical methodology, as well as review of epidemiological models of short-term effects of environmental exposures on health. Longitudinal analysis of association between local weather conditions obtained via smartphone GPS location and patient symptom data entered via a smartphone app. Development of a prediction model for pain based on the weather ("pain forecast").Year 2 and 3 will include obtaining, cleaning and preparing physical activity data from linked activity monitors and determine measures best suited for analysis. Longitudinal analysis of activity measures and symptom data. A prediction model for future joint pain based on weather forecasts would allow patients to plan their activities. An understanding of the association between passively monitored physical activity and disease severity would allow development of a novel outcome measure for use in clinical care and research.