Even having written multiple essays or group project reports in my first year of undergraduate studies, this Laidlaw research attachment report still appears to me as a massive piece of work done. Not being allowed to contact overseas (in my case, UCL) supervisors at the application stage unlike my UK counterparts, I had limited information regarding the scope of the project I was going to do, especially when the description on UCL's Laidlaw project list for my selection had 5 different related approaches. Since May 2023, two months before my attachment at the Institute of Sports, Exercise and Health under Dr Jo Blodgett, I had the chance to finally speak (albeit through Zoom) with my supervisor and start discussing what to focus on for the project. Due to ethics approval and funding constraints, what I had planned to do before departure first had to be changed upon my arrival, then again updated after a delayed ethics approval.

That said, after sorting out some further logistics issues (as pre-season training does not start that early in July, and our data collection relies on actual footballers), I had three packed weeks of data collection and at the same time, analysis and wrapping up the Laidlaw components of the project. Of course, the first three weeks were not to be wasted, we prototyped and tested the usage of OpenCap (app.opencap.ai) and drew up a detailed protocol for the testing – which I am glad to know is being used for subsequent data collection at the ISEH/UCL to expand the sample size now – and developed questionnaires as well as other cruicial elements for the onsite data collection (at the football pitch).

As explained in the report and poster, this stage (upon completion of the 6-week attachment) focused on the analysis of first-week differences with respect to baseline characteristics, particularly prior training experience. This was because of the short 6-week placement and internal Laidlaw deadlines, and therefore the full data was not yet available when I left London – the plan was to first analyse the data available at hand, then further explore the full menstrual cycle with an expanded sample size (current n = 20). Since August, the team at ISEH has been analysing the differences in LESS (a jump-landing test) and double-leg squat parameters across the cycle. The individual errors of the LESS score are a particular focus that we are looking into currently. We are expanding to another 3-5 teams to increase the sample size, and hope to publish the results by the end of 2024. 

I hope you enjoy the read – the use of innovative AI-based algorithmic tools that can prove to be scalable, and portable for movement dynamics + kinematics measurement is certainly what I think this project is unique, but for sure the potential findings regarding menstrual cycle's impact on athletes is also very exciting to wait for!