Low atmospheric pressure is associated with more severe symptoms of pain


A new mass study of people living with chronic pain in the UK has demonstrated the links between pain and certain atmospheric weather conditions.

Weather systems in the UK could cause chronic pain suffers to experience more or less pain on certain days as a result of certain pressure patterns and accompanying rain, humidity, and temperature caused by movements in the jet stream, according to new research published in the Bulletin of the American Meteorological Society.

To better characterise which weather conditions most affect pain, a group of University of Manchester–based researchers and their collaborators, funded by Versus Arthritis, conducted a 15-month long study with over 13,000 UK residents living with chronic-pain conditions.

In this study, called “Cloudy With a Chance of Pain,” the participants recorded their daily pain intensity within an app on their smartphones.

The GPS location of the phone would then link to the weather data. The team’s previous work used a statistical approach to examining the difference in local weather between days where individuals had an increase in pain over the previous day versus days they did not have such a pain event.

In this new study, the team analysed the data across all of the UK as a meteorologist would do. The researchers ranked all days in the study by the percentage of people responding who recorded a pain event.

The most painful days had 23% of participants reporting an increase in pain, and the least painful days had 10% of participants reporting an increase in pain.

The researchers took the 45 days at the top of the ranking (the top 10% of all study days) and averaged the weather conditions on those days to determine the weather patterns present when the most number of people were in pain.

They did the same for the 45 days where the least number of people reported pain (bottom 10%).

These research results show for the first time the weather patterns on days with a large number of people reporting pain, compared to days with a low number of people reporting pain. On the most painful days, the jet stream was aimed right at the UK, with below-normal (or low) pressure over the UK.

The humidity and precipitation rate were both above normal, and winds were stronger. In contrast, on the least painful days, the jet stream tended to blow north of the UK, bringing above-normal (or high) pressure to the UK. The humidity and precipitation rate were both below normal, and winds were weaker.

The new research was led by Professor David Schultz, Department of Earth and Environmental Sciences, the University of Manchester, and is a collaboration with the Cloudy With a Chance of Pain team led by Professor Will Dixon, from The University of Manchester. Prof Schultz has now been awarded the 2020 European Meteorological Society S. W. Tromp Foundation award for “Outstanding Achievement in Biometeorology” for this research paper.

“Over 2400 years ago, Hippocrates wrote that different wind directions could bring better or worse health to individuals. said Prof Dixon.

“The belief by people living with long-term pain conditions, such as arthritis, that their pain is affected by the weather remains prevalent today, with about 75% of people with chronic pain believing this to be true. Yet, there is disagreement over what weather condition makes their pain worse.”

Prof. Schultz added, “Part of the reason for this lack of consensus is that previous researchers have treated the different measures of the weather such as pressure, temperature, humidity separately, which assumes that one could vary the temperature while holding all of the other weather measures fixed.

Of course, the real atmosphere does not behave like this, as all the variables are changing simultaneously. A simple analysis clearly won’t do to get at understanding how weather affects pain.”

This research confirms and expands on previous research from the Manchester researchers. Because this study is the largest in terms of both duration and number of participants, it allows greater confidence in the results.

Although not everyone believes in the link between weather and pain, the results of this project should give comfort and support to those who have claimed that the weather affects their pain, but have been dismissed.

Finally, this research also begins to shed light on the environmental conditions that modulate pain, insight that might be explored further for improving the treatment, management, and forecasting of pain.

Weather has been thought to affect symptoms in patients with chronic disease since the time of Hippocrates over 2000 years ago.1 Around three-quarters of people living with arthritis believe their pain is affected by the weather.2,3

Many report their pain is made worse by the cold, rain, and low atmospheric pressure. Others report that their pain is made worse by warmth and high humidity. Despite much research examining the existence and nature of the weather–pain relationship,4 there remains no scientific consensus.

Studies have failed to reach consensus in part due to their small sample sizes or short durations (commonly fewer than 100 participants or one month or less); by considering a limited range of weather conditions; and heterogeneity in study design (e.g. the populations studied, methods for assessing pain, assumptions to determine the weather exposure, and statistical analysis techniques).5–11

Resolving this question requires collection of high-quality symptom and weather data on large numbers of individuals. Such data also need to include other factors potentially linked to daily pain variation and weather, such as mood and amount of physical activity.

Collecting this kind of multi-faceted data in large populations over long periods of time, however, has been difficult.

The increasing uptake of smartphones offers new and significant opportunities for health research.12 Smartphones allow the integration of data collection into daily life using applications (apps).

Furthermore, embedded technologies within the smartphones, such as the Global Positioning System (GPS), can be used to link the data collection to specific locations. We created Cloudy with a Chance of Pain,13,14 a national United Kingdom smartphone study, to collect a large dataset to examine the relationship between local weather and daily pain in people living with long-term pain conditions.


Recruitment and retention
The study app was downloaded by 13,207 users over the 12-month recruitment period (Figs ​(Figs11 and ​and2a)2a) with recruitment from all 124 UK postcode areas. A total of 10,584 participants had complete baseline information and at least one pain entry, with 6850 (65%) participants remaining in the study beyond their first week and 4692 (44%) beyond their first month (Fig. ​(Fig.2b).2b). Further description of engagement clusters is provided in Supplementary Table 2 and Supplementary Figs 1–3.

A total of 2658 participants had at least one hazard period matched to a control period in the same month (Fig. ​(Fig.3)3) and were included in the final analysis. There were 9695 hazard periods included in the analysis for the final 2658 participants, matched to 81,727 control periods in 6431 participant-months.

A total of 1235 participants contributed one month, and the remaining 1423 participants contributed 2–15 months.

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Fig. 1
User interface of the study app (uMotif, London). Each colored segment represents one of the ten data items. Participants report their symptoms on a five-point scale by dragging the segment from the center outwards
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Fig. 2
Recruitment and retention. a Cumulative recruitment and number of active participants through time. The blue line represents the cumulative number of participants with a completed baseline questionnaire and at least one pain score submitted. The red line represents the current number of active participants (i.e. those who have submitted their first but not yet their last pain score in the study period). b Retention through time. The graph represents the retention of active participants through time as a survival probability from the day of their recruitment. Participants were censored when they were no longer eligible for follow-up. Eligible follow-up time ranged from 90 days (for those recruited on 20 January 2017) to 456 days (for those recruited on 20 January 2016)
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Fig. 3
Example participant timeline of 21 days, showing participant-reported items (here, pain severity, mood, and exercise) and weather data (here, temperature and relative humidity). Pain events with their associated hazard periods (dark grey) occur when pain severity increases by two or more ordinal categories between consecutive days (e.g. from Day 4 to Day 5). Control periods (light gray) occur on days that were eligible to be a pain event, but where pain did not increase by two or more ordinal categories. Days where there was no recorded pain on the preceding day, or where the preceding day’s pain was severe or very severe (and could thus not increase by two or more categories), were not eligible to be pain-event days or control days. The case-crossover analysis compared the weather on pain-event days to weather on control days within a risk set of a calendar month

The final cohort was active for a median of 165 days (interquartile range, IQR 84–245) with symptoms submitted on an average of 73% of all days. Cohort members were predominantly female (83%), had a mean age of 51 years (standard deviation 12.6), and had a range of different pain conditions, predominantly arthritis (Supplementary Table 1).

The median number of weather stations associated with each participant during the course of their active data-collection period was 9 (IQR 4–14) with a maximum of 82 stations, indicating how mobile participants were during the course of the study and the importance of accounting for the weather at different locations over the course of the study.

As an illustration of the structure of the data, the proportion of participants reporting a pain event was plotted as a heat map per calendar day for the study period (Fig. ​(Fig.4),4), aligned with the average United Kingdom weather data for the same time period. On any given day during the study, about 1–6% of participants had a pain event.

At the start of the study, most participants believed in an association between weather and their pain (median score 8 out of 10, IQR 6–9).

The demographics, health conditions and baseline beliefs of the 2658 participants included in the analysis were representative of the 10,584 participants who downloaded the app and provided baseline information (Supplementary Table 2).

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Fig. 4
The proportion of eligible active participants reporting a pain event during the study period, aligned with average UK weather data from February 2016 to April 2017. Heat map colors indicate the percentage of participants reporting a pain event on that day, ranging from 1–6% participants. The denominator per day is the number of participants who reported their pain on the day of interest and the prior day, irrespective of the level of pain on the prior day and thus their eligibility for a pain event
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University of Manchester


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