Medical devices and equipment are not always easy to come about, especially if they are costly or when they are needed urgently. This is where valid apps that can provide the same function with similar reliability come in handy. Balsalobre-Fernández et al studied the validity and reliability of the app Runmatic (iOS, available for both the iPhone and iPad) for measuring various aspects of running mechanics. Runners who are training often use optoelectronic devices to track their performance and to prevent non-contact injury during training. Specifically, performance can be tracked by leg stiffness, vertical oscillation from the center of mass, and ground contact time. Non-contact injury can be predicted based on leg stiffness and leg asymmetry. While this all sounds very complicated, these values are all computed with set formulae with only a couple of simple input parameters: contact and takeoff times.

Previous studies have shown that cameras that can capture at least 240 frames per second are sufficient for determining the aerial time in a run. Therefore, iPhone 6 or above can be used for this purpose. To test Runmatic, participants were asked to do a standardized warm-up, followed by 6 different 30 second runs on a Gymrol S2500 motorized treadmill at either 2.77, 3.33, 3.88, 4.44, 5, or 5.55 m/s at 0 incline in random order with 1 minute passive rest in between each run. Eight steps in the middle of each run were analyzed using Runmatic and Optojump Next (a single-meter kit) as the control. Runmatic determines the contact time by calculating the time between the first frame where the runner’s foot contacts the treadmill and when the foot takes off from the treadmill. Aerial time is calculated using the first frame where one foot takes off from the treadmill and when the other foot contacts the treadmill. The initial frames for analysis are selected manually in the app.

The results of the study showed that Runmatic did well when compared with the reference device, Optojump Next. Overall, the validity of Runmatic was high (r=0.94-0.99; p<0.001) with high agreement with Optojump Next (ICC=0.965-0.991) for the different parameters computed based on contact and takeoff times. A significant difference for aerial time was observed using the t test, but the authors explained that this could be an artifact of the smaller values for aerial time, because the difference in magnitude of the values is very low. As such, the authors conclude that Runmatic is a great alternative for measuring contact time, aerial time, vertical oscillation, leg stiffness, maximum relative force, and step frequency.

Although not mentioned explicitly in the study, we can easily see the advantages Runmatic has over the conventional optoelectronic devices. The app is convenient, it can keep performance records all in one place, help track progress over time, help you see how you compare with friends, and perhaps eventually send personalized warnings to the runner when measurements show increased risk for injury. It could be improved though by providing an accompanying mount with the purchase of the app (currently selling for $9.99 in the iOS store), so that the runner will not need an additional partner to physically hold the iPhone behind the treadmill to capture the videos.

Source: The Validity and Reliability of an iPhone App for Measuring Running Mechanics