As researchers, we like data. Data helps us to understand questions such as: is our program working? How is it working? Who is working for? Are we reaching the right people? Are champions running the program as we intended? Are there any barriers or facilitators to implementation? How can we improve? Good data is essential for building the evidence base around supporting desk workers to sit less and move more. We are currently deep in the process of cleaning, coding and preparing to analyse the data from the implementation trial of BeUpstanding and thought this might be a good opportunity to discuss the importance of data.
So, how do we decide what data to collect?
The type of data depends on the research question we are trying to address. In BeUpstanding, we are trying to address a lot of research questions, so we are collecting lots of different types of data. For those of you who have taken part in the program, you may have completed surveys. Surveys are a very common data collection method as you can easily collect large amounts of data and — if you are asking numeric questions — the data can be relatively easily analysed. This sort of data is helping us to understand questions such as who is taking part in the program, how much people are sitting, standing and moving at work (and how much they want to be sitting, standing and moving), what are the perceived barriers and enablers to sitting less and moving more, and what strategies people are currently doing to sit less and move more at work. Within surveys, we also often ask open text questions as well, like “describe any impacts of COVID-19 on your team” or “please let us know any feedback on the program”. Unlike the numeric data, we need to manually code this information. Generally, we do this by identifying key themes in the responses, like staff shifting to work from home or increased workload for the COVID-19 impacts. We then code each response into these key themes. This obviously takes a bit more time than the numeric data, but can also provide richer information.
Another common method through which we collect data is via interviews. Here we are seeking more detailed and in-depth understanding of the experiences of participants and other relevant contextual information. This rich detail is important as it provides information and detail that we may not be capturing via a standard question in a survey. Data is also automatically collected through our online BeUpstanding Champion Toolkit. Here, we are collecting data such as logins and resource downloads. This information helps us to understand engagement with the toolkit, including how that engagement happened across time. Our project managers also track their tasks within the backend of the toolkit, which will be used as part of the economic analysis.
We understand that data is precious. Within BeUpstanding, we are always striving for that goldilocks point, where there is the right amount of data (and the right type of data) to generate the evidence needed, without overburdening the people we are collecting the data from. If you want to read more about the data we are collecting, check out our protocol paper for the implementation trial. Most importantly, we want give a huge shout out and thank you to everyone who contributes data to the BeUpstanding program of research – you are helping to make an impact!