The Boston Marathon is without doubt the most famous marathon running event in the world. It’s popularity and fame mean that it has needed to enforce strict time-based application criteria in an effort to control participant numbers. Runners from all over the world are drawn to Boston by its history and challenging course. And 2018 was no exception in terms of tough challenge. With many international elites dropping out of the race and surprise winners on the men's and women's sides and some very inclement weather. This case study follows Lisa and her 2018 Boston marathon through her wearable gait data.
In an effort to keep our case studies digestible and targeted for website purposes, for each dataset that we present we will try to focus on one key area that was highly significant for the runner in their story. In this case study we will look into the information that can be gained from looking at left and right ground contact time measurement. A number of different wearable devices can perform the task of collecting this type of data. In this case we used version 3 Runscribe footpods.
For background context Lisa finished the race in 3 hours 34 minutes slightly below her best of 3 hours 31 minutes in very wet, windy and cold conditions and on a course that is well known for its hills, most notably Heartbreak Hill at 33 km.
In the first plot (below) we can see her pace curve for the race in grey and her overall combined (integrated left and right) ground contact time in the purple curve. As is generally expected in gait analysis her contact times are clearly inversely related to her speed. As she runs faster (drop in her pace value), her contact time on the ground decreases. And she runs uphill and downhill her inversely related speed and contact times respond accordingly to slope and resistance. However looking across the entire race her ground contact times do not exhibit any significant overall trends. Rather the combined contact time is consistently within the same range from the start line all the way to the finish.
Pace in min/km and overall ground contact time in ms against run time. Elevation profile in black.
So to gain more insight we need to break out the separate left and right side data. The Runscribe system comprises 2 individual pods, one on each shoe that collect, store and then upload separate data files. These 'duplicate' metrics can then be used to make statistical comparison and improve each data file's accuracy in a process sometimes called 'fusion'. Hence we are then afforded the opportunity to make valuable and dependable left to right comparisons for data at any part of the race.
The second plot (below) now shows 2 purple curves, the brighter purple representing the left side and the darker lavender representing the right side. Overall it is clear to see that the 2 curves do not track on top of one another but show clear separations throughout with just short moments of overlap or left - right symmetry. The fleeting moments of balance between the left and right sides appear to correlate with points where the pace briefly increases in pulses between 2 hours 15 minutes and 2 hours 55 minutes.
Ground contact times left and right in ms and pace in min/km against run time. Elevation profile in black.
On closer inspection it is also possible to observe a period where the purple curves are further apart than during the bulk of the race, between 7 minutes and 2 hours 15 minutes. Essentially marking out the first two thirds of the race. Beyond 2 hours 15 minutes the remainder of the race sees the curves track closer together until the final section to the finish at 3 hours 23 minutes onward.
There are different speculative interpretations that could be made from this data but key to understanding more is to know the runner (their baseline data) and their description of the race as well as the course demands and the environmental conditions (if its thought that they may have played a role). After integrating all of these sources of information together a story emerges that touches on learning points that can apply to many other outdoor gait analyses.
The marked divergence of the contact time curves at 7 minutes coincides with the end of a sharp downhill early in the race and a transition to flat running at which point Lisa was already cold and simply trying to get 'warmed up' and started being buffeted by a cross wind. As we will elaborate more on in a future article, slope angles and environmental conditions can have a measurable effect on gait mechanics and bilateral asymmetry. It appears in this dataset that Lisa's contact time asymmetry was triggered by this change from sharp downhill to flat angle and the influence of a strong cross wind and did not receive a sufficient stimulus to improve (become more symmetric) during the following 1 hour 30 minutes.
Then at approximately 1 hour 40 minutes her contact times very gradually start to become more symmetric. The convergence of the curves continues until 2 hours 15 minutes. This phase in the race coincides with a slight drop in the strength of the cross wind and a period of long, gentle uphill where Lisa was forced to work a bit harder to maintain pace, thus encouraging the brain to communicate better with the key muscles. This neuromuscular 'switch-on' effect means Lisa becomes more symmetric as she pushes on uphill. Pace also plays a big part in whether a runner displays more or less symmetric data and this in evidence in the fleeting moments of symmetry after 2 hours 25 minutes, where bursts of faster running on rolling downhills correlate with increased symmetry. This is further compounded by what might be referred to as a residual potentiation effect in the muscles carried over after the gradual uphill section.
So whilst not exhaustive this case study highlights a bit of what can be learned from the collection of ground contact time data when left and right sides are measured independently. Some of the terms used in this post will be expanded further upon in future follow-up posts as we explore more real life running and dive deeper into the interpretation of wearable gait analysis data. If there any particular questions that you have or topics that you like to hear more about then please feel free to comment below this post as we are always keen to stay aligned with what our readers would like to know.