Cover story for Pulse@UM Year 2021 Issue 1: Big Data & Artificial Intelligence in Medicine and Healthcare Research
Using Google Analytics for Research: A Case Study
By Professor Dr Ng Chirk Jenn, Dr Ooi Chor Yau & Dr William Khoo Swee Keong
Web-based apps have been widely used as a form of health intervention. Studies have shown that web-based apps are effective in changing health behaviour. However, to understand how to implement web-based apps in a real-world setting, a form of monitoring is necessary. One of the tools that is currently available for free is Google Analytics (GA). GA is a free tool that can be used to monitor web-traffic by tracking and analysing web-traffic data. It provides some insights into the behaviour of people that access the website by monitoring data such as number of visits, duration, pages accessed and user location.
In this case study, we used ScreenMen, a web-based app that was developed to increase the uptake of screening in men. ScreenMen undertook a rigorous and systematic development process based on theories, evidence and needs of men. ScreenMen targets men between 20 and 50 years of age as this group of men usually do not go for health screening. ScreenMen can be accessed easily via various platforms including laptop, desktop and mobile devices, as long as a web-browser and internet connection are available. A pilot study is currently being conducted to implement ScreenMen in a government health clinic. By using GA, we are able to determine the number of patients who accessed and completed ScreenMen; and the time taken to complete the screening process using ScreenMen. A unique QR code is generated for each promotional material (e.g., pamphlet, bunting, banner) in the health clinic so that it allows the researcher to identify how men prefer to access ScreenMen.
The main strength of using GA as a data collection method is that it can easily capture comprehensive data on user behaviour. For example, we are able to track every single user who accessed ScreenMen: the time, duration and each web page accessed in the web-based app. We are also able track the users based on their location, the types of platform, and number of times they accessed ScreenMen. However, GA has its limitations; ascertaining the validity of the data can be a problem. For example, in our study, we are looking at men using ScreenMen but we cannot be certain if the users are men or women. Another validity issue is the duration of using ScreenMen; if the user is idle while accessing ScreenMen, the duration of completing the web-based app will be longer than expected.
Overall, GA is a good tool to collect data on web-traffic and user behaviour in using a web-based app. However, other forms of evaluation are necessary to complement GA as a data collection and analytic tool. Future studies should look into the effectiveness of using GA as part of the process evaluation of web-based apps.
1. Wantland DJ, Portillo CJ, Holzemer WL, Slaughter R, McGhee EM. (2004). The effectiveness of Web-based vs. non-Web-based interventions: a meta-analysis of behavioral change outcomes. J Med Internet Res. 6(4):e40.
2. Teo CH. (2019). A Mobile Web App to Improve Health Screening Uptake in Men (ScreenMen): Utility and Usability Evaluation Study. JMIR mHealth and uHealth. 7(4):e10216.
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