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Monitoring a runner's gait characteristics over time

Performing a gait analysis on a runner as a one-off can be very valuable in itself if it leads into actions that truly help the runner move better. But gait analysis really comes into it's own when a particular runner receives multiple follow-up assessments over a significant period of time. In the best case this would start with the collection of a baseline dataset that profiles the runner in a healthy state with no injury issues, and then continues with repeated assessments to analyze if the runner is continuing to display the same baseline profile or deviating away from it in some way. 

To illustrate just how effective and important this can be in the 'real world' the remainder of this article will focus around an example of a runner that worked with Runfisix throughout their 2018 running season to help them stay injury free whilst they pursued a Boston Qualifier time in the marathon. 

In March 2018 the runner received their first gait analysis during a period of injury free running. Below is one of the data results from that assessment, the left and right side recorded impact in G's (relates to the nature of the footstrike on each side of the body). Other metrics measured at the same also showed similar bilateral asymmetry - these include the ground contact time, flight ratio and braking G's. Although impact has been chosen here, any of these metrics could have been used in this example.


IMPACT G's LEFT: 12.2          IMPACT G's RIGHT: 12.5        % ASYMMETRY: 2


 Runfisx Gait Analysis Monitoring Feb 2018

Data measured on February 28th 2018. 


The data from this initial assessment helped to create a profile of the runner in their healthy state. Following this the runner received further regular reassessments, all performed in the same manner with the same metrics being recorded. In some cases the re-assessments were as close together as 2 weeks.

At the start of November a typical routine assessment was performed that highlighted a deviation away from the runner's typical profile. In the same week the runner began reporting feelings of tightness and soreness in certain soft tissue areas after workouts. Below are the impact data collected during the re-assessment where deviations from the baseline data were noted. 


IMPACT G's LEFT: 11.7          IMPACT G's RIGHT: 9.1         % ASYMMETRY: 22


Runfisx Gait Analysis Monitoring Nov 2018

Data measured on November 1st 2018. 


Action was taken immediately following interpretation of this re-assessment data with the runner attending a chiropractic appointment and 2 physiotherapy appointments. The running training schedule was also adapted by Runfisix staff in accordance with the need to ensure the runner did not suffer a serious full-blown injury. 10 days later another re-assessment was performed to monitor whether the therapeutic interventions were having a positive effect on the runner's gait. Below is the impact data measured from this follow-up re-assessment. 


IMPACT G's LEFT: 9.9             IMPACT G's RIGHT: 9.7        % ASYMMETRY: 2


Runfisx Gait Analysis Monitoring Nov 2018

Data measured on November 11th 2018.


It is clear to see from the 3 impact datasets shown above that the runner in this case had a baseline healthy impact asymmetry close to 0. However during the routine regular monitoring they started to become asymmetric, with the percentage asymmetry measured on November 1st 2018 at 22%. Following treatment the runner's body responded well, moving back to the original baseline asymmetry of 2%. This pattern was also seen reflected in the contact time data, flight ratio data and braking G's data. 

(note the drop in absolute impact values over the 3 assessments, is mostly pace related, as these assessments were performed during a year of training where the runner dramatically improved running form).

This particular example shows some pretty dramatic change and coincidentally also features a return back to the exact asymmetry percentage as the original baseline. Most importantly it highlights the power of follow-up re-assessments and of monitoring runners over time with a consistent gait analysis approach. As was the case with this runner, it was possible to use the gait data as a basis for interpreting that the runner was heading away from their healthy 'normal'  and towards increased risk of injury. And hence there was time to incorporate medical intervention and move the runner back toward a more balanced running gait. 

Fortunately in this case the runner was able to maintain their weekly running training without sustaining an injury and without having to take any time out that would have led to a detraining effect. Consequently the runner was able to sustain their fitness improvements and go on to achieve their racing targets before the end of the year.

With more and more being written today on the subject of injury prevention, the compliance to a regular schedule of re-assessment and the application of objective numerical data collection are 2 great ways to actually put injury prevention into action. In the hands of skilled professionals that know how to interpret the results and how to take appropriate action, this kind of methodology can have a significant impact on a runner's chance of sustaining a serious injury.

Certainly for us at Runfisix this style of work has become routine and has been critical to how we help runners every week spend more time running and less time injured. Feel free to leave any comments below on your experiences on this topic or further applications that you can envision from using this approach. 

  • Nov 18, 2019
  • Category: Articles
  • Comment: 1
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Ramon Rueda March 22, 2020

Thanks for sharing this. I currently own two pairs of runscribe pods and plan on enhancing our gait analysis protocol here in Wichita Falls, Tx. Is there any resource out there that teaches you how to interpret the data?

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