How Precisely Can Easily Accessible Variables Predict Achilles and Patellar Tendon Forces during Running?

Abstract

Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, the main objective of this study was to investigate whether algorithms can be developed for predicting patellar and Achilles tendon force and impulse during running using measures that can be easily collected by runners using commercially available devices. A secondary objective was to evaluate the predictive performance of the algorithms against the commonly used running distance. Trials of 24 recreational runners were collected with an Xsens suit and a Garmin Forerunner 735XT at three different intended running speeds. Data were analyzed using a mixed-effects multiple regression model, which was used to model the association between the estimated forces in anatomical structures and the training load variables during the fixed running speeds. This provides twelve algorithms for predicting patellar or Achilles tendon peak force and impulse per stride. The algorithms developed in the current study were always superior to the running distance algorithm.

The Effect of Wearable-Based Real-Time Feedback on Running Injuries and Running Performance: A Randomized Controlled Trial

“…There are, however, some limitations to the algorithms implemented within the wearable. Specifically, although methods have been developed to estimate absolute tissue loading from running wearables using various statistical methods, 4,8,22 the wearable used in this study did not measure the absolute load on tissues but rather inferred a relative loading distribution from correlations with spatiotemporal metrics reported in the literature. Such an approach may introduce errors at the individual level (eg, because of the differences in tissue properties or geometry), which in turn may reduce the effectiveness of feedback.…”

Per‐step and cumulative load at three common running injury locations: The effect of speed, surface gradient, and cadence

“…For example, while we used a fixed speed to isolate the effect of changes in surface gradient from changes in speed, runners may slow down while running uphill, while speeding up while running downhill 67 . Recent developments in wearable technology to estimate tissue loading may overcome this limitation and allow load and damage estimation in‐field 68,69 …”

“…67 Recent developments in wearable technology to estimate tissue loading may overcome this limitation and allow load and damage estimation in-field. 68,69 A third limitation is that our relatively small sample size did not allow us to explore sex-specific effects that may occur due to biomechanical differences between males and females. 14,70 Nevertheless, we attempted to account for sex effects, for example by including a sexspecific patellofemoral contact area.…”

Predicting Tissue Loads in Running from Inertial Measurement Units

“…The present study differs from most previously reported results by targeting so-called structure-specific loads, i.e., the force on the Achilles tendon and patella ligament. To our knowledge, only one other study has attempted to predict these structure-specific loads from wearable devices [ 36 ]. In this study, Brund et al found mean absolute percentage errors ranging from 13 to 30%, which appear similar to the normalized root mean squares error of 15–20% we report for the best performing models.…”