After the success of collecting 12 hours of data during a road ultra marathon in May of this year, I was fortunate to get the chance to take things up a level at the September 2017 Golden Ultra Trail Race. Martin Parnell told me about the race in the summer and immediately I saw the chance to do something pretty unique in terms of running gait data collection.
The Golden Ultra is a 3 day event in Golden, BC (Canada) that challenges the participants with 3 different stages on 3 different courses, all off road. Included in the fun is a 2000m ascent up a mountain on day 1, a 60km trail ultra on all kinds of angles on day 2 and a trail half marathon with technical terrain on day 3. What was motivating for me as the biomechanics specialist performing the data collection was the opportunity to follow a runner’s data during 3 consecutive days in a single event where each successive day the runner would have to run again on tired legs. Not only that, I could also compare the data across each of the days challenges to see how the specific terrain affected gait pattern. Finally I would also be able to see the effect if any, of using poles on certain stages that are thought to help with stability and impact.
Across the 3 days my plan was to use 8 sensors mounted all over the runners body to measure up to 22 parameters simultaneously, of which 20 would be useful in understanding the gait and biomechanics of the runner. Each night I would have a chance to download the days data and recharge all of the batteries in the sensors. If all went well the total recorded data time would be close to 18 hours.
My principal subject for the study would be Martin. At 61 Martin is a veteran of literally hundreds of marathons and ultra marathons. His attention to detail and meticulous planning were a huge help to me. One of the key reasons for working with Martin on this project though was the pacing of his running. According to his predictions he would use all of the allowed time for each day, getting to finish line just before the finish line closed. This meant he was on his feet for the maximum possible time, giving me the maximum possible data collection duration. Whilst monitoring an elite runner would have had its merits for sure, it simply wouldn’t have provided the sheer quantity of data that someone like me really needed.
In a future follow-up post I’ll pull out some of the highlights of the event as seen through the data. In the meantime I have more data analysis to perform!