Claire L. Little, David M. Schultz, Belay B. Yimer, Anna L. Beukenhorst
{"title":"How Being Inside or Outside of Buildings Affects the Causal Relationship Between Weather and Pain Among People Living with Chronic Pain","authors":"Claire L. Little, David M. Schultz, Belay B. Yimer, Anna L. Beukenhorst","doi":"arxiv-2401.17678","DOIUrl":null,"url":null,"abstract":"Although many people believe their pain fluctuates with weather conditions,\nboth weather and pain may be associated with time spent outside. For example,\npleasant weather may mean that people spend more time outside doing physical\nactivity and exposed to the weather, leading to more (or less) pain, and poor\nweather or severe pain may keep people inside, sedentary, and not exposed to\nthe weather. We conducted a smartphone study where participants with chronic\npain reported daily pain severity, as well as time spent outside. We address\nthe relationship between four weather variables (temperature, dewpoint\ntemperature, pressure, and wind speed) and pain by proposing a three-step\napproach to untangle their effects: (i) propose a set of plausible directed\nacyclic graphs (also known as DAGs) that account for potential roles of time\nspent outside (e.g., collider, effect modifier, mediator), (ii) analyze the\ncompatibility of the observed data with the assumed model, and (iii) identify\nthe most plausible model by combining evidence from the observed data and\ndomain-specific knowledge. We found that the data do not support time spent\noutside as a collider or mediator of the relationship between weather variables\nand pain. On the other hand, time spent outside modifies the effect between\ntemperature and pain, as well as wind speed and pain, with the effect being\nabsent on days that participants spent inside and present if they spent some or\nall of the day outside. Our results show the utility of using directed acyclic\ngraphs for studying causal inference.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"19 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2401.17678","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.