Evergreen Life insights

COVID-19 Insights

We are using data provided by our users to generate health and wellbeing insights throughout the COVID-19 pandemic.

The below graphs and maps are based on information provided by over 50,000 Evergreen Life users. The statistical analysis is being done by Evergreen Life in partnership with the University of Manchester's Department of Mathematics, University of Liverpool’s Department of Electrical Engineering and Institute of Population Health Sciences.
Regional trends - Staying at home
Below you can see the % of Evergreen Life users reporting that they are staying at home, broken down by English region. At this time we are still working on gathering enough data for Scotland, Wales and Northern Ireland to be able to show the same data for those regions.

If you are in Scotland, Wales or Northern Ireland please download the app and self-report!
Regional trends - Symptomatic
Below you can see the % of Evergreen Life users reporting symptoms, broken down by English region.
At this time we are still working on gathering enough data for Scotland, Wales and Northern Ireland to be able to show the same data for those regions.

If you are in Scotland, Wales or Northern Ireland please download the app and self-report
COVID-19 symptom correlates
Using Evergreen Life user data, we have discovered certain lifestyle factors which show a relationship with COVID-19 symptoms. The following shows the observed differences in several different lifestyle factors, between users who have ever reported having COVID-19 symptoms, versus not.

On average, users who report an unhealthier lifestyle, such as eating red meat or more than the recommended amount of alcohol, are more likely to have reported having COVID-19 symptoms, as indicated by the ruby coloured bars. Conversely users who take part in vigorous activity are less likely to have reported COVID-19 symptoms.

Insights you have helped us discover

  • Users with symptoms are more likely to eat processed food
  • Users with a higher BMI are more likely to have reported having COVID-19 symptoms
  • Users with symptoms are also more likely to be anxious* and less likely to be satisfied with their sleep
  • Users who are staying at home are more likely to be eating vegetables and other healthy foods like oily fish*
  • Users who are staying at home are also drinking alcohol more frequently but are drinking fewer units per day*
Visualisation of COVID in the months after lockdown
The graphs below demonstrate the % of users who are symptomatic and the % of those that staying at home since March 24th.
*Statistically significant at the 99% confidence level; †Statistically significant at the 95% confidence level; ‡Statistically significant at the 90% confidence level
Insights & correlates disclaimer: Using information from over 53,000 Evergreen Life users, these figures are derived from univariate Linear Probability Models (LPMs) (https://bit.ly/3aKaCeG) and multivariate LPMs, where we control for users’ gender and age. Results are robust (in terms of sign, magnitude and statistical significance) to using logistic regression (https://bit.ly/3aHN0HI). LPM results are preferred for ease of interpretation.
Symptoms and staying at home heatmaps: These maps use information provided by over 53,000 Evergreen Life users. Each data point uses the latest response for each user that has responded within the 2 weeks prior to each day. The reported percentage is taken as the expected value of the Beta Distribution (https://bit.ly/3kcGWfq) corresponding to the counts for symptomatic, asymptomatic, staying at home, and not staying at home (plus one), respectively. CIs are taken as the frequencies that correspond to the regularised incomplete beta function taking a value of 2.5% and 97.5%. We define areas with insufficient data as those with an estimated size of the (beta distributed) error exceeding 10%.
National trend line graphs: These graphs use information provided by over 53,000 Evergreen Life users. Each data point uses the latest response for each user that has responded within the 2 weeks prior to each day. The reported percentage is taken as the expected value of the Beta Distribution (https://bit.ly/3kcGWfq) corresponding to the counts for symptomatic, asymptomatic, staying at home, and not staying at home (using a=b=1 as shape parameters), respectively. CIs are taken as the frequencies that correspond to the regularised incomplete beta function taking a value of 2.5% and 97.5%.
±Staying at Home disclaimer: The guidance given by the UK government has changed several times during the COVID pandemic. As a result, whilst we have monitored adherence to national guidance previously, we now show users’ behaviour in terms of whether they are staying at home, regardless of whether this is due to local or national guidance, or if they cannot work from home. Users are excluded from the display if they are a key worker.

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