Anecdotally, it is well established how fatigue coincides with various meteorological conditions. The science, though, remains vague as it is hard both to isolate particular weather conditions and objectify intrinsically subjective feelings like fatigue. But thanks to big data sets like provided by Google Trends, there are ways of getting there and the results are surprisingly clear.
Google Trends
Google Trends is a service by the Google search engine where you can get statistical insight into what users online are searching, as well as when and where they are search for it. The provided data can be considered reliable as the user base is sufficiently large, while Google’s reputation is sufficiently good to believe the numbers they provide.
You can display individual search terms and entire topical areas, including those related to fatigue. When your goal is to look for statistical connections between fatigue and the weather, the best approach is to look for a large, dense urban area where you can expect the weather to be uniform.
Weather Data
As soon as you have chosen your target area and timeframe, you need the weather data for that area and timeframe. You can download these for free from various weather related websites like the respective national weather or climate authorities. For the US, this would be NOAA, whereas data for Germany is available on DWD.
Berlin Tempelhof
One example will be Berlin with the weather station Berlin Tempelhof, which is a large park area located right in the middle of Berlin. Berlin is the only urban area in Germany for which Google Trends offers enough data points to get to a useful result. One problem is insufficient data for the night hours. This can be compensated by only creating the average for the entire day, but excluding hours with zero.
Los Angeles International Airport
The second example will be Los Angeles with numbers from its international airport. Los Angeles is far larger than Berlin offering sufficient data points also at night. In this case it makes sense to use moving averages to compensate for outliers. Using moving averages makes it easier to find patterns in the heystack of numbers. The result may not be sufficient for a deeper analysis, but it serves the purpose of determining whether there are patterns in the first place worth digging deeper.
Take-Aways
The comparison between air pressure and Google Trends data does indicate a possible connection between fatigue and weather conditions, especially in regards to air pressure. Especially fast dropping air pressure appears to lead to complaints about fatigue, whereas the influence of gradually dropping air pressure is only small and inconclusive.
Rising air pressure appears to lead to complaints about fatigue in a similar fashion, although in a less disruptive manner than it is the case during dropping air pressure phases. Search spikes for fatigue can also happen during stable air pressure, although in the case of Los Angeles on February 22nd, this appears to be coincidental. Since this was a Friday, the likely explanation is in relation to the subsequent work week.
Next Step
Both, Los Angeles and Berlin are located on an altitude of less than 100m (300ft), while air pressure drops by 1HPa per meter of elevation, the next step is to compare urban areas on higher altitude. The question is whether fatigue and air presssure changes are equally relevant on all heights, or more prevalent in low air pressure environments.