Can you spot any patterns linking weather and pain?

We want to uncover whether there is a relationship between weather and symptoms… and we need your help.

Cloudy With a Chance of Pain is a grand experiment that is collecting lots and lots of data across the UK. People with arthritis and other chronic pain conditions are providing daily symptom reports using their smartphones, whilst their phones are automatically collecting hourly local weather data.

This generates a VERY big dataset and we would like your help to explore it. We want you to become a detective, to dive deep into the data and help us spot the patterns. In this citizen science experiment, you are invited to explore real-life symptoms shown alongside the weather data. If you think you can spot an association (or not), you’ll be able to submit your thoughts and hypotheses.

Individual participants data are charted as graphs like this:

When we show data from all our participants, the graphs are joined together to make a landscape like this:

Your job is to try and see whether there is a pattern that links the symptom graphs or landscapes to the weather graphs or landscapes. It’s that easy. Anyone visiting this site will be able to explore anonymous data collected by all participants in the study. In addition, if you have signed up to the study and are logged on, and have provided sufficient data within the last week (5 of the last 7 days), you will first be able to view your own data. Once we have collected all your ideas, the research team will do some formal analyses, focusing on the most commonly submitted suggestions. Together we can work it out!

Start the Citizen Science Experiment
Data loading (5MB)

What correlations do your data reveal?

On this page, you can see graphs of your own symptoms in the last week. You will initially see your pain levels. If you want, you can switch to look at mood, fatigue, morning stiffness or wellbeing.

Below your symptoms, you will see the weather that occurred around you throughout this week. You will initially see hourly weather data, but you can switch to view daily weather summaries (daily maximum, minimum, average or range). We are not able to show the type of weather (e.g. rain, fog).

The graph below shows 7 days of your

records, as well as

weather reports collected at your location.

So…did you spot any patterns? We want you to tell us what you think, including if you can’t spot any relationship. There’s no right or wrong answer. Below, you can slowly build up your hypothesis which you can submit at any time.

Thank you for taking part in the study and for logging on. Unfortunately, you haven’t entered data for at least 5 of the last 7 days: we can’t generate graphs for you due to too many gaps. Please fill in your motif every day and come back again soon!

Explore population level data for now!

What can we learn from other people’s data?

On this page, we would like you to spot the relationships between symptoms from all our participants and the weather to which they were exposed.

The symptom data are shown on the left of the screen. You can select from pain, mood, fatigue, morning stiffness or wellbeing. Weather data are displayed on the right. Within the weather data, you can opt to display hourly or daily statistics for a range of weather variables. You are also able to explore different time periods by using the time selector. If you can spot a relationship between the symptoms and weather (or not), you can then submit your thoughts or hypotheses below.

Landscape showing symptom and weather data from 750 participants from

The symptom landscape shows

records, weather landscape shows



Where does the data come from?

The landscape of symptom data is created from the records of 750 participants, sampled from thousands of participants of the study.

To be as accurate as possible in these graphics, we only display records from participants who have submitted at least 80% of the symptom records across the last week. We've also removed records originating from cities we don't have complete weather reports from. Finally, we've filled in the missing data using an average of known records.


Thank you for your contribution!

Thank you for taking part in our citizen science experiment. Your ideas will be sent to the research team in Manchester and will help inform their analysis of the data.

Spread the word, share your hypotheses: