身处建筑物内外如何影响天气与慢性疼痛患者疼痛之间的因果关系

Claire L. Little, David M. Schultz, Belay B. Yimer, Anna L. Beukenhorst
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摘要

尽管许多人认为他们的疼痛会随着天气条件而波动,但天气和疼痛都可能与户外活动的时间有关。例如,宜人的天气可能意味着人们有更多的时间在户外进行体育锻炼和接触天气,从而导致更多的疼痛(或更少的疼痛);而恶劣的天气或剧烈的疼痛可能会让人们呆在室内,久坐不动,不接触天气。我们进行了一项智能手机研究,让慢性疼痛的参与者报告每天疼痛的严重程度以及户外活动的时间。我们探讨了四个天气变量(气温、露点温度、气压和风速)与疼痛之间的关系,并提出了一个三步法来解开它们之间的影响:(i)提出一组可信的有向环图(也称为 DAG),以解释户外时间的潜在作用(例如,碰撞器、效应调节器、媒介)、对撞机、效应调节器、中介),(ii) 分析观测数据与假定模型的匹配性,(iii) 结合观测数据证据和特定领域知识,确定最合理的模型。我们发现,数据并不支持将户外活动时间作为天气变量与疼痛之间关系的媒介或中介。另一方面,户外活动时间会改变温度与疼痛之间的影响,以及风速与疼痛之间的影响,参与者在室内活动的日子里没有这种影响,而如果他们一天中的部分或全部时间都在户外活动,这种影响就会出现。我们的研究结果表明了使用有向无环图研究因果推断的实用性。
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How Being Inside or Outside of Buildings Affects the Causal Relationship Between Weather and Pain Among People Living with Chronic Pain
Although many people believe their pain fluctuates with weather conditions, both weather and pain may be associated with time spent outside. For example, pleasant weather may mean that people spend more time outside doing physical activity and exposed to the weather, leading to more (or less) pain, and poor weather or severe pain may keep people inside, sedentary, and not exposed to the weather. We conducted a smartphone study where participants with chronic pain reported daily pain severity, as well as time spent outside. We address the relationship between four weather variables (temperature, dewpoint temperature, pressure, and wind speed) and pain by proposing a three-step approach to untangle their effects: (i) propose a set of plausible directed acyclic graphs (also known as DAGs) that account for potential roles of time spent outside (e.g., collider, effect modifier, mediator), (ii) analyze the compatibility of the observed data with the assumed model, and (iii) identify the most plausible model by combining evidence from the observed data and domain-specific knowledge. We found that the data do not support time spent outside as a collider or mediator of the relationship between weather variables and pain. On the other hand, time spent outside modifies the effect between temperature and pain, as well as wind speed and pain, with the effect being absent on days that participants spent inside and present if they spent some or all of the day outside. Our results show the utility of using directed acyclic graphs for studying causal inference.
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